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  • 201.
    Marashian, Shahrzad
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Shiraz Univ, Sch Mech Engn, Shiraz, Iran..
    Sadrizadeh, Sasan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH Royal Inst Technol, Dept Civil & Architectural Engn, Stockholm, Sweden..
    Abouali, Omid
    Shiraz Univ, Sch Mech Engn, Shiraz, Iran.;KTH Royal Inst Technol, Dept Civil & Architectural Engn, Stockholm, Sweden..
    Modeling particle distribution in a ventilated room with modified discrete random walk methodsIn: The International Journal of Ventilation, ISSN 1473-3315, E-ISSN 2044-4044Article in journal (Refereed)
    Abstract [en]

    The airflow and micro-particle dispersion in a 3-D ventilated scaled room has been simulated numerically. The flow field was studied by the Eulerian method using a Reynolds Averaged Navier-Stokes model, and we used the Lagrangian approach to solve the equations of particle motion. The purpose is to evaluate and compare various discrete random walk methods (DRW) and continuous random walk methods (CRW) to evaluate particle concentration distribution in indoor environments. The isotropic DRW method's performance has been compared with models in which anisotropy of turbulence is applied, including CRW and modified DRW models based on near-wall direct numerical simulation results, near-wall kinetic energy, and the helicity of the flow. The results reveal that the isotropic DRW method can predict particle concentration in the indoor environment, and using a modified DRW model is not necessary.

  • 202.
    Marashian, Shahrzad
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Vadiee, Amir
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Abouali, Omid
    KTH, Sweden.
    Sadrizadeh, Sasan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH, Sweden.
    Enhancing Indoor Environmental Simulations: A Comprehensive Review of CFD Methods2023In: / [ed] Konstantinos G. Kyprianidis, Erik Dahlquist, Ioanna Aslanidou, Avinash Renuke, Gaurav Mirlekar, Tiina Komulainen, and Lars Eriksson, Västerås, Sweden, 2023Conference paper (Refereed)
    Abstract [en]

    Computational Fluid Dynamics (CFD) simulations are extensively used to model indoor environments, including airflow patterns, temperature distribution, and contaminant dispersion. These simulations provide valuable insights for improving indoor air quality, enhancing thermal comfort, optimizing energy efficiency, and informing design decisions. The recent global pandemic has emphasized the importance of understanding airflow patterns and particle dispersion in indoor spaces, highlighting the potential of CFD simulations to guide strategies for improving indoor air quality and public health. Consequently, there has been a significant increase in research focused on studying the transport and dispersion of pollutants in indoor environments using CFD techniques. These simulations are vital in advancing engineers' understanding of indoor environments; however, achieving accurate results requires careful method selection and proper implementation of each step. This paper aims to review the state-of-the-art CFD simulations of indoor environments, specifically focusing on strategies employed for three main simulation components: geometry and grid generation, ventilation strategies, and turbulence model selection. Researchers can select suitable techniques for their specific applications by comparing different indoor airflow simulation strategies.

  • 203.
    Marashian, Shahrzad
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    ZendehAli, Nafiseh
    Vadiee, Amir
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Abouali, Omid
    Sadrizadeh, Sasan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Investigation of two different ventilation designs in a single-bed isolation room2023In: The 11th International Conference on Sustainable Development in Building and Environment, 2023Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    The recent epidemic of the coronavirus disease showed the increased importance of controlling the transmission of contamination in the ward areas more than before. The performance of the ventilation systems in healthcare facilities can significantly impact the overall healthcare quality. This paper aims to compare two ventilation designs in an isolation room of a hospital and study the indoor airflow pattern. Computational fluid dynamics using ANSYS Fluent software was employed for the numerical simulation of the fluid flow. The simulation included the prediction of flow patterns and particle trajectories with the additional investigation into the impact of considering human thermal plume and modeling particle trajectories considering the turbulent fluctuations using the discrete random walk method in the simulation.  

  • 204.
    Marchand, Charlotte
    et al.
    Linnéuniversitetet, Institutionen för biologi och miljö (BOM).
    Hogland, William
    Linnéuniversitetet, Institutionen för biologi och miljö (BOM).
    Kaczala, Fabio
    Linnéuniversitetet, Institutionen för biologi och miljö (BOM).
    Jani, Yahya
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Linnéuniversitetet, Institutionen för biologi och miljö (BOM), Sweden.
    Marchand, Lilian
    INRA, France.
    Augustsson, Anna
    Linnéuniversitetet, Institutionen för biologi och miljö (BOM).
    Hijri, Mohamed
    Université de Montréal, Canada.
    Effect of Medicago sativa L. and compost on organic and inorganic pollutant removal from a mixed contaminated soil and risk assessment using ecotoxicological tests2016In: International journal of phytoremediation, ISSN 1522-6514, E-ISSN 1549-7879, Vol. 18, no 11, p. 1136-1147Article in journal (Refereed)
    Abstract [en]

    Several Gentle Remediation Options (GRO), e.g. plant-based options (phytoremediation), singly and combined with soil amendments, can be simultaneously efficient for degrading organic pollutants and either stabilizing or extracting trace elements (TE). Here, a 5-month greenhouse trial was performed to test the efficiency of Medicago sativa L., singly and combined with a compost addition (30% w/w), to treat soils contaminated by petroleum hydrocarbons (PHC), Co and Pb collected at an auto scrap yard. After five months, total soil Pb significantly decreased in the compost-amended soil planted with M. sativa, but not total soil Co. Compost incorporation into the soil promoted PHC degradation, M. sativa growth and survival, and shoot Pb concentrations (3.8 mg/kg DW). Residual risk assessment after the phytoremediation trial showed a positive effect of compost amendment on plant growth and earthworm development. The O2 uptake by soil microorganisms was lower in the compost-amended soil, suggesting a decrease in microbial activity. This study underlined the benefits of the phytoremediation option based on M. sativa cultivation and compost amendment for remediating PHC and Pb contaminated soils.

  • 205.
    Martinsen, Madeleine
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhou, Y.
    Baidu Inc, Beijing, China.
    Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks2023In: Machines, E-ISSN 2075-1702, Vol. 11, no 10, article id 940Article in journal (Refereed)
    Abstract [en]

    In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established.

  • 206.
    Martinsen, Madeleine
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. ABB, Forskargränd 7, 722 26 Västerås, Sweden.
    Zhou, Yuanye
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 339, article id 120988Article in journal (Refereed)
    Abstract [en]

    Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s con-fidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO2 emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO2 per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3-5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.

  • 207.
    Marzi, E.
    et al.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Morini, M.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Saletti, C.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Vouros, Stavros
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Gambarotta, A.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Power-to-Gas for energy system flexibility under uncertainty in demand, production and price2023In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 284, article id 129212Article in journal (Refereed)
    Abstract [en]

    The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as Mixed-Integer Linear Programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances.

  • 208.
    Masrur Hossain, M.
    et al.
    Department of Mechanical Engineering, University of Washington, Seattle, WA, United States; Department of Mechanical and Production Engineering, Islamic University of Technology, Bangladesh.
    Afnan Ahmed, N.
    Department of Mechanical and Production Engineering, Islamic University of Technology, Bangladesh.
    Abid Shahriyar, M.
    Department of Mechanical and Production Engineering, Islamic University of Technology, Bangladesh.
    Monjurul Ehsan, M.
    Department of Mechanical and Production Engineering, Islamic University of Technology, Bangladesh.
    Riaz, F.
    Department of Mechanical Engineering, National University of Singapore, Singapore; Mechanical Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
    Salehin, S.
    Department of Mechanical and Production Engineering, Islamic University of Technology, Bangladesh.
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Analysis and optimization of a modified Kalina cycle system for low-grade heat utilization2021In: Energy Conversion and Management: X, ISSN 2590-1745, Vol. 12, article id 100121Article in journal (Refereed)
    Abstract [en]

    Kalina cycle system (KCS) offers an attractive prospect to produce power by utilizing low-grade heat sources where traditional power cycles cannot be implemented. Intending to explore the potential of exploiting low-grade heat sources for conversion to electrical energy, this study proposes two modified power generation cycles based on KCS-34. A multi-phase expander is positioned between the Kalina separator and the second heat regenerator in the proposed X-modification. In contrast, it is located between the mixer and second regenerator for Y-modification. To explore the potential benefits and limitations of the proposed modifications contrasted with the KCS-34, thermodynamic modeling and optimization have been conducted. The influence of critical decision parameters on overall cycle performance is analyzed. The result elucidates that by implementing an additional multi-phase expander, a significant amount of energy can be extracted from a lean ammonia water loop and X-modification can deliver superior thermodynamic performance compared with the Y-modification and the original KCS-34. With a reduced turbine inlet pressure of 58 bar and an ammonia concentration of 80%, the X-modified cycle's efficiency reaches a peak value of 17% and a net power yield of 1015 kW. An increase of 6.35% can be achieved compared with the conventional KCS-34 operating at the same conditions. Maximum exergy destruction of the working substance was observed in the condenser. 

  • 209.
    Midemalm, Joel
    et al.
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Vadiee, AmirMälardalen University, School of Business, Society and Engineering, Future Energy Center.Uhlemann, ElisabethMälardalen University, School of Innovation, Design and Engineering, Embedded Systems.Georgsson, FredrikUmeå universitet, Sweden.Carlsson-Kvarnlöf,, GunillaKarlstads universitet, Sweden.Månsson, JonasLunds tekniska högskola, Sweden.Edström, KristinaKungliga Tekniska högskolan, Sweden.Pettersson, LennartLuleå tekniska universitet, Sweden.Johansson, PedherBlekinge tekniska högskola, Sweden.
    Bidrag från den 9:e utvecklingskonferensen för Sveriges ingenjörsutbildningar2023Conference proceedings (editor) (Refereed)
    Download full text (pdf)
    fulltext
  • 210.
    Mohan, Sooraj
    et al.
    Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
    Dinesha, P.
    Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid2022In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 259, article id 124968Article in journal (Refereed)
    Abstract [en]

    Organic Rankine cycle (ORC) has been demonstrated to extract useful work output from low-grade heat sources like solar-thermal, biomass/biofuel combustion, geothermal, and waste heat. However, working fluid selection for ORC is a complex process and calls for careful optimization. To address this problem, the current work constitutes a design of experiments approach with a full-factorial design. A heat source temperature of 150 °C is selected, and a list of 11 possible candidates of working fluid mixtures (among hydrocarbons) is taken. Work output and efficiencies from each fluid are determined based on the design of experiments, and the results are used to model an artificial neural network (ANN). Equations for work output and first law efficiency are developed using tan sigmoid function and ANN constants which act as objective functions that are maximized using multi-objective particle swarm optimization (PSO). The results of the ANN-PSO model is validated with the values from thermodynamic analysis with less than 2% error. The optimal working fluid obtained for maximum work output is R600a operating at an evaporator pressure of 1.88 MPa without any superheating. The resulting maximum work output is 7.15 kW at 8.05% thermal efficiency and an exergy efficiency of 38.13%.

  • 211.
    Monghasemi, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Vadiee, Amir
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Jalilzadehazhari, E.
    Division of Civil Engineering and Built Environment, Department of Civil and Industrial Engineering, Uppsala University, Uppsala, 75104, Sweden.
    Rank-Based Assessment of Grid-Connected Rooftop Solar Panel Deployments Considering Scenarios for a Postponed Installation2023In: Energies, E-ISSN 1996-1073, Vol. 16, no 21, article id 7335Article in journal (Refereed)
    Abstract [en]

    Installing solar photovoltaic panels on building rooftops can help property managers generate renewable energy and reduce electricity costs. However, the existence of multiple efficiency indicators and ambiguity in interpreting these metrics limits the comparison of the performance of individual installation projects. This paper presents a methodology using data envelopment analysis to evaluate suitable candidates for rooftop solar panel installation. This approach integrates rooftop area, solar irradiation, temperature, costs, energy yield, and revenue to evaluate the relative efficiency of each building. To demonstrate the methodology, it was applied to rank 22 residential buildings, revealing the top performers for installation in 2022. The approach was subsequently adapted to assess potential outcomes under deferred implementation up to 2030, encompassing a diverse range of climate and pricing scenarios. Five installations were found to be optimal irrespective of the future scenarios. In addition, a super-efficiency approach was applied to overcome the low level of discrimination among the possible installations and to rank each individual unit uniquely. The analysis is designed to guide property owners in identifying favorable solar photovoltaic investments within their portfolios under changing conditions. 

  • 212.
    Moradi, R.
    et al.
    Università eCampus, Centro di Ricerca per l’Energia, l’Ambiente e il Territorio, CO, Novedrate, Italy.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tascioni, R.
    Università eCampus, Centro di Ricerca per l’Energia, l’Ambiente e il Territorio, CO, Novedrate, Italy.
    Cioccolanti, L.
    Università eCampus, Centro di Ricerca per l’Energia, l’Ambiente e il Territorio, CO, Novedrate, Italy.
    THERMODYNAMIC ANALYSIS OF THE OFF-DESIGN PERFORMANCE OF A MICRO SOLAR ORGANIC RANKINE CYCLE TRIGENERATION SYSTEM2021In: International Seminar on ORC Power Systems, Knowledge Center on Organic Rankine Cycle Technology (KCORC) , 2021Conference paper (Refereed)
    Abstract [en]

    Organic Rankine Cycle (ORC) systems are considered as one of the most suitable technologies to produce electricity from low-temperature sources. ORC units can efficiently convert low-temperature solar energy into electric and thermal power. Independently from the solar technology used, the hourly and the seasonal fluctuations of solar energy entail challenging dynamic effects and bring these systems to operate in off-design conditions. Such effects are even more influential at micro-to-small scales granting paramount importance to the comprehensive understanding of their behavior. In this study, the annual performances of a 4 kWe/50 kWth solar ORC trigenerative system for residential applications are numerically investigated. Four different modeling approaches commonly used in annual system-level simulations of ORC systems are compared. These models differ in the system-level modeling approach and the components modeling method. The analysis has shown that the simplest ORC model results in the lowest discrepancy compared to the model with the least assumption, in which the components are modeled empirically, and the high and low pressures of the system are found iteratively. The difference between the produced electric energy using the four models is significantly higher in hot months, in which the average temperature of the water tank is high due to the requirements of the vapor generator of the absorption chiller. In this case, the expander pressure ratio drops drastically depending on the system model algorithm, which affects the produced electric power depending on the adopted expander model. On the contrary, the discrepancy between the models for the produced thermal energy is negligible.

  • 213.
    Moretti, A.
    et al.
    Università degli studi di Udine, Polytechnic Department of Engineering and Architecture (DPIA), Udine, 33100, Italy.
    Ivan, Heidi Lynn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible?2024In: Journal of Water Process Engineering, E-ISSN 2214-7144, Vol. 60, article id 105061Article in journal (Refereed)
    Abstract [en]

    Efficient characterization of wastewater stream quality is vital to ensure the safe discharge or reuse of treated wastewater (WW). There are numerous parameters employed to characterize water quality, some required by directives (e.g. biological oxygen demand (BOD), total nitrogen (TN), total phosphates (TP)), while others used for process controls (e.g. flow, temperature, pH). Well-accepted methods to assess these parameters have traditionally been laboratory-based, taking place either off-line or at-line, and presenting a significant delay between sampling and result. Alternative characterization methods can run in-line or on-line, generally being more cost-effective. Unfortunately, these methods are often not accepted when providing information to regulatory bodies. The current review aims to describe available laboratory-based approaches and compare them with innovative real-time (RT) solutions. Transitioning from laboratory-based to RT measurements means obtaining valuable process data, avoiding time delays, and the possibility to optimize the (WW) treatment management. A variety of sensor categories are examined to illustrate a general framework in which RT applications can replace longer conventional processes, with an eye toward potential drawbacks. A significant enhancement in the RT measurements can be achieved through the employment of advanced soft-sensing techniques and the Internet of Things (IoT), coupled with machine learning (ML) and artificial intelligence (AI).

  • 214.
    Munir, M. Adeel
    et al.
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Habib, M. Salman
    Univ Engn & Technol Lahore, Dept Ind & Mfg Engn, Lahore, Pakistan..
    Hussain, Amjad
    Univ Engn & Technol Lahore, Dept Mech Engn, Lahore, Pakistan..
    Shahbaz, Muhammad Ali
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Qamar, Adnan
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Masood, Tariq
    Univ Strathclyde, Dept Design Mfg & Engn Management, Glasgow, Scotland..
    Sultan, M.
    Bahauddin Zakariya Univ, Dept Agr Engn, Multan, Pakistan..
    Mujtaba, M. A.
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Imran, Shahid
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Hasan, Mudassir
    King Khalid Univ, Coll Engn, Chem Engn Dept, Abha, Saudi Arabia..
    Akhtar, Muhammad Saeed
    Yeungnam Univ, Coll Engn, Sch Chem Engn, Gyongsan, South Korea..
    Ayub, Hafiz Muhammad Uzair
    Yeungnam Univ, Coll Engn, Sch Chem Engn, Gyongsan, South Korea..
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Blockchain Adoption for Sustainable Supply Chain Management: Economic, Environmental, and Social Perspectives2022In: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 10, article id 899632Article in journal (Refereed)
    Abstract [en]

    Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network.

  • 215.
    Mutafela, Richard N.
    et al.
    Linnéuniversitetet, Institutionen för biologi och miljö (BOM).
    Mantero, Juan
    University of Gothenburg, Sweden;University of Seville, Spain.
    Jani, Yahya
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Linnéuniversitetet, Institutionen för biologi och miljö (BOM), Sweden.
    Thomas, Rimon
    University of Gothenburg, Sweden.
    Holm, Elis
    University of Gothenburg, Sweden.
    Hogland, William
    Linnéuniversitetet, Institutionen för biologi och miljö (BOM), Sweden.
    Radiometrical and physico-chemical characterisation of contaminated glass waste from a glass dump in Sweden2020In: Chemosphere, ISSN 0045-6535, E-ISSN 1879-1298, Vol. 241, p. 1-10, article id 124964Article in journal (Refereed)
    Abstract [en]

    Around former glass factories in south eastern Sweden, there are dozens of dumps whose radioactivity and physico-chemical properties were not investigated previously. Thus, radiometric and physico-chemical characteristics of waste at Madesjö glass dump were studied to evaluate pre-recycling storage requirements and potential radiological and environmental risks. The material was sieved, hand-sorted, leached and scanned with X-Ray Fluorescence (XRF). External dose rates and activity concentrations of Naturally Occurring Radioactive Materials from 238U, 232Th series and 40K were also measured coupled with a radiological risk assessment. Results showed that the waste was 95% glass and dominated by fine fractions (< 11.3 mm) at 43.6%. The fine fraction had pH 7.8, 2.6% moisture content, 123 mg kg-1 Total Dissolved Solids, 37.2 mg kg-1 Dissolved Organic Carbon and 10.5 mg kg-1 fluorides. Compared with Swedish EPA guidelines, the elements As, Cd, Pb and Zn were in hazardous concentrations while Pb leached more than the limits for inert and non-hazardous wastes. With 40K activity concentration up to 3000 Bq kg-1, enhanced external dose rates of 40K were established (0.20 mSv h-1) although no radiological risk was found since both External Hazard Index (Hex) and Gamma Index (Iγ) were < 1. The glass dump needs remediation and storage of the waste materials under a safe hazardous waste class ‘Bank Account’ storage cell as a secondary resource for potential future recycling.

  • 216.
    Mählkvist, Simon
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Kanthal, Sweden.
    Ejenstam, Jesper
    Kanthal, Sweden.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Consolidating industrial batch process data for machine learning2022In: / [ed] Esko Juuso, Bernt Lie, Erik Dahlquist and Jari Ruuska, 2022, p. 76-83Conference paper (Refereed)
    Abstract [en]

    The paradigm change of Industry 4.0 brings attention to data-driven modeling and the incentive to apply machine learning methods in the process industry. Further, capitalizing on a great deal of data available is an adverse task. For batch processes, the dataset is in a threeway format (Batch × Sensor × Time). Depending on the process and the goal of the analysis, it might be necessary to aggregate batches together. For this reason, a campaign unfolding structure is applied. By grouping the batches under new labels relevant to the analytical goal, campaigns are created. These labels can be created from periodical occurrences, such as refurbishing the refractory lining in the case of the case study. In order to utilize the three-way batch format, it is necessary to align the batches. In order to address this, the feature-oriented approach Statistical Pattern Analysis (SPA) is applied. SPA derives statistics, e.g., mean, skewness and kurtosis from the time series, consequently aligning the batches. The SPA and the campaign approach create a dataset consisting of select statistics instead of an irregular three-way array. Functional data analysis (FDA) is used to smooth and extract first- and second-order derivative information from the sensors in which functional behavior can be observed before creating features. Principal Component Analysis (PCA) is used to examine the final dataset. Further, industrial processes are notoriously nonlinear, and even more so batch processes. Therefore, kernel-based principal component analysis (KPCA) is used to review the final dataset. The KPCA can accommodate different underlying characteristics by modifying the kernel function used. 

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  • 217.
    Mählkvist, Simon
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ejenstam, Jesper
    Kanthal AB, S-73427 Hallstahammar, Sweden..
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Cost-Sensitive Decision Support for Industrial Batch Processes2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 23, article id 9464Article in journal (Refereed)
    Abstract [en]

    In this work, cost-sensitive decision support was developed. Using Batch Data Analytics (BDA) methods of the batch data structure and feature accommodation, the batch process property and sensor data can be accommodated. The batch data structure organises the batch processes' data, and the feature accommodation approach derives statistics from the time series, consequently aligning the time series with the other features. Three machine learning classifiers were implemented for comparison: Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM). It is possible to filter out the low-probability predictions by leveraging the classifiers' probability estimations. Consequently, the decision support has a trade-off between accuracy and coverage. Cost-sensitive learning was used to implement a cost matrix, which further aggregates the accuracy-coverage trade into cost metrics. Also, two scenarios were implemented for accommodating out-of-coverage batches. The batch is discarded in one scenario, and the other is processed. The Random Forest classifier was shown to outperform the other classifiers and, compared to the baseline scenario, had a relative cost of 26%. This synergy of methods provides cost-aware decision support for analysing the intricate workings of a multiprocess batch data system.

  • 218.
    Neshat, M.
    et al.
    Center for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Mirjalili, S.
    University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
    Garcia, D. A.
    Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Gandomi, A. H.
    Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia.
    Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy2023In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 278, article id 127701Article in journal (Refereed)
    Abstract [en]

    Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation.

  • 219.
    Neshat, M.
    et al.
    Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
    Sergiienko, N. Y.
    School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, 5005, SA, Australia.
    Majidi Nezhad, Meysam
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    da Silva, L. S. P.
    Delmar Systems, Perth, 6000, WA, Australia.
    Amini, E.
    Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
    Marsooli, R.
    Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
    Astiaso Garcia, D.
    Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy.
    Mirjalili, S.
    Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, 4006, QLD, Australia.
    Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method2024In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 362, article id 122955Article in journal (Refereed)
    Abstract [en]

    Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms.

  • 220.
    Netzell, Pontus
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kazmi, H.
    Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden2024In: Energies, E-ISSN 1996-1073, Vol. 17, no 10, article id 2246Article in journal (Refereed)
    Abstract [en]

    As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the "weekly Na & iuml;ve" benchmark with a relative Root Mean Square Errors of 32-34% and 39-40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

  • 221.
    Nie, H.
    et al.
    ilu University of Technology (Shandong Academy of Sciences), School of Mechanical & Automotive Engineering, Jinan, Chin.
    Du, T.
    School of Energy and Power Engineering, Shandong University, Jinan, China.
    Lv, Y.
    ilu University of Technology (Shandong Academy of Sciences), School of Mechanical & Automotive Engineering, Jinan, 250353, Chin.
    Zhu, X.
    ilu University of Technology (Shandong Academy of Sciences), School of Mechanical & Automotive Engineering, Jinan, 250353, Chin.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Flow Characteristics of Temperature-Driven Bistable Slit Jet in Tube2021In: Energy Proceedings, Scanditale AB , 2021, Vol. 22Conference paper (Refereed)
    Abstract [en]

    In recent years, control of bistable flow of slit jet has attracted the interests of researchers due to its extensive applications in many fields. But there are still many problems in controlling jet flow by temperature. In order to further explore the influence of temperature on jet flow. This paper mainly carried out the numerical simulation on the control of bistable flow in a tube actuated by temperature. The slit jet was formed by two semi-circular tubes arranged side by side at different Re, different spacing ratios and different temperatures. It was found that, both spacing ratio and Re affect jet wall attachment. In case of temperature difference between the two semicircular tubes, the jet is inclined to the side at higher temperature. Higher temperature gradient corresponds to larger deflection angle and less arrival time of jet from the start point to the attached side. Setting the specific parameters, the split jet is stably attached to the side at higher temperature after running for a period of time. When the temperature of two walls is reversed, the split jet attached wall will also be conversed. This study lays a theoretical foundation for the further development of bistable characteristics of flow field and the application of dynamic thermal management devices.

  • 222.
    Nikbakht, Mehran Vahedi
    et al.
    Sadjad Univ Technol, Dept Civil Engn, Mashhad 9188148848, Iran.;Assoc Talent Liberty Technol TULTECH, EE-10615 Tallinn, Estonia..
    Gheibi, Mohammad
    Tech Univ Liberec, Fac Mechatron Informat & Interdisciplinary Studies, Liberec 46117, Czech Republic.;Tech Univ Liberec, Inst Nanomat Adv Technol & Innovat, Liberec 46117, Czech Republic..
    Montazeri, Hassan
    Assoc Talent Liberty Technol TULTECH, EE-10615 Tallinn, Estonia..
    Khaksar, Reza Yeganeh
    Sadjad Univ Technol, Dept Civil Engn, Mashhad 9188148848, Iran..
    Moezzi, Reza
    Assoc Talent Liberty Technol TULTECH, EE-10615 Tallinn, Estonia.;Tech Univ Liberec, Fac Mechatron Informat & Interdisciplinary Studies, Liberec 46117, Czech Republic.;Estonian Univ Life Sci, Inst Forestry & Engn Sci, EE-51006 Tartu, Estonia..
    Vadiee, Amir
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Identification and Ranking of Factors Affecting the Delay Risk of High-Rise Construction Projects Using AHP and VIKOR Methods2024In: INFRASTRUCTURES, ISSN 2412-3811, Vol. 9, no 2, article id 24Article in journal (Refereed)
    Abstract [en]

    Construction projects, especially those for commercial purposes, require thorough planning and control to ensure success within predetermined budgets and timelines. This research, conducted in Mashhad, Iran, employs the analytic hierarchy process (AHP) and VIKOR methods to identify and rank factors influencing delays in high-rise projects. The study, based on a sample of 40 projects, emphasizes the comprehensive nature of our research method. The scale for features in project selection includes societal importance (with different applications including cultural hubs, affordable housing initiatives, and urban renewal for social equity), size (less and more than 20 units in residential projects), and diversity (mixed-use development, inclusive infrastructure, and cultural and recreational spaces), contributing to a comprehensive analysis of construction delays. Expert project managers and engineers provided insights through two questionnaires, and their responses underwent thorough analysis. Our findings not only underscore the significance of factors contributing to project success but also rank their impact on the likelihood of delays. The study reveals that the negative effects of these factors on cost, time, and project quality vary. Time emerges as the most influential parameter, with approximately six times more impact on cost and nine times more on quality. Contractor financial weakness, delays in allocating financial and credit resources, insufficient project resource allocation, contractor technical and executive weakness, and a lack of proper implementation and project control are identified as the most important factors contributing to delays.

  • 223.
    Nilpueng, K.
    et al.
    King Mongkut's University of Technology North Bangkok, Bangsue, Bangkok, Thailand.
    Chomamuang, T.
    King Mongkut's University of Technology North Bangkok, Bangsue, Bangkok, Thailand.
    Mesgarpour, Mehrdad
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Zhejiang Provincial Engineering Research Center for the Safety of Pressure Vessel and Pipeline, Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, China.
    Mahian, O.
    Wongwises, S.
    Zhejiang Provincial Engineering Research Center for the Safety of Pressure Vessel and Pipeline, Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, China.
    Thermal-hydraulic performance of a plate heat exchanger with grooved copper foam2023In: Case Studies in Thermal Engineering, ISSN 2214-157X, Vol. 51, article id 103525Article in journal (Refereed)
    Abstract [en]

    This study proposes a plate heat exchanger (PHE) partially filled with metal foam having checkered pattern grooves. The study presents new experimental data obtained from PHE with grooved copper foam that elucidates the effect of mass flux, copper foam groove width, and pore density on the heat transfer coefficient (HTC) and pressure drop (ΔP). The testing is conducted with a water mass flux ranging from 120 to 320 kg/m2s, a groove width ranging from 2 to 6 mm, and a pore density of 30 pores per inch (PPI) and 50 PPI. The results demonstrate that HTC and ΔP increase as the copper foam groove width is reduced. Considering the impact of copper foam groove width on the filling rate, the HTC ratio increases significantly at a filling rate between 50 and 75%. Furthermore, an increase in pore density enhances HTC and ΔP. The plate heat exchanger inserted with copper foam (PHE_CF) provides the optimum thermal-hydraulic performance (TP). However, at a low Reynolds number, the results show that TP of PHE with grooved copper foam with a groove width of 2 mm is similar to PHE_CF. New correlations are also proposed to predict ΔP and HTC in the practical applications.

  • 224.
    Nookuea, Worrada
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Impacts of Thermo–Physical Properties on the Design, Operation, and Cost of Monoethanolamine–Based Chemical Absorption2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The thermodynamic and transport properties of CO2 mixtures are essential to the design, operation, and optimization of all carbon capture and storage processes. To retrieve accurate property values, accurate property models are required. However, there are many properties, which are in turn affected by many factors. Moreover, property model development is behind the requirement of accurate properties. Therefore, it is important to quantify the property impacts on the process design for CCS to prioritize the development of models of the properties that are the most important ones.

    According to the identified knowledge gaps, the impacts of the following thermo-physical properties were selected for quantitative analysis: density, viscosity, diffusivity, and surface tension on the column designs for the chemical absorption using aqueous monoethanolamine. The in–house rate–based absorption and desorption models were developed in MATLAB to simulate the processes, and sensitivity studies were done for each property. For the diameter design, developing more accurate gas phase density models should be prioritized. However, developing a more accurate liquid phase density model is also important, due to its significant impact and larger model uncertainty range. For the absorber packing height design, development of the liquid phase density and viscosity models should be prioritized. In addition, for the desorber packing height design, development of the gas phase diffusivity and density model should be prioritized. Regarding the impacts on the cost of the absorber and the overall equipment, development of the density and viscosity models of the aqueous amine solution with CO2 loading should be prioritized. However, as far as desorber cost is concerned, development of the gas phase density and diffusivity model of the CO2/H2O mixture should be prioritized.

    The rate-based chemical absorption and desorption models were developed in Aspen Plus to evaluate the impacts of mass transfer coefficient models and desorber pressure. The liquid mass transfer coefficient has more significant impacts on the simulation of the absorber than it does to the simulation of the desorber. Moreover, the impacts on the concentration profiles are more significant compared to those on the temperature profiles. In addition, regenerating CO2 at elevated pressures shows the potential to reduce the energy penalty of CO2 capture and compression.

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  • 225.
    Nourozi, B.
    et al.
    Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Holmberg, S.
    Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Duwig, C.
    Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Afshari, A.
    Department of Built Environment, Aalborg University, Copenhagen, Denmark.
    Wargocki, P.
    Department of Environmental and Resource Engineering, Technical University of Denmark, Copenhagen, Denmark.
    Olesen, B.
    Department of Environmental and Resource Engineering, Technical University of Denmark, Copenhagen, Denmark.
    Sadrizadeh, Sasan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Heating energy implications of utilizing gas-phase air cleaners in buildings’ centralized air handling units2022In: Results in Engineering (RINENG), ISSN 2590-1230, Vol. 16, article id 100619Article in journal (Refereed)
    Abstract [en]

    Ventilation systems are a vital component of buildings in order to ensure a healthy and comfortable environment for the occupants. In cold climate regions, ventilation systems are responsible for approximately 30% of building heat losses. In addition to outdoor pollutants (particulate matters, NOX, etc.), indoor emissions from materials in the form of gas pollutants and emissions from occupants are the principal indoor air quality metrics for securing an acceptable indoor concentration level. Therefore, it is of great interest to study the use of gas-phase air cleaning technologies in low-energy centralized air handling units. This study focused on reducing buildings' heating requirements by recirculating indoor air while maintaining an acceptable indoor air quality level. The heating performance of a typical residential and office building in the central Swedish climate was studied by dynamic building simulations. Indoor air recirculation rates and air changes per hour were the key parameters considered during the simulation of the building's heating demand and indoor gaseous air pollution concentration. We found that introducing indoor air recirculation reduces buildings' heating demand depending on the air change rates per hour. The results show that it is possible to reduce the energy use for heating by less than approximaytely 10% and 20% for residential and office buildings, respectively and maintain acceptable indoor air quality by using gas-phase air cleaning. 

  • 226.
    Nourozi, B.
    et al.
    KTH Royal Institute of Technology, Sweden.
    Wierzbicka, A.
    Lund University, Lund, Sweden.
    Yao, R.
    Chongqing University, Chongqing, China.
    Sadrizadeh, Sasan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH Royal Institute of Technology, Sweden.
    A systematic review of ventilation solutions for hospital wards: Addressing cross-infection and patient safety2024In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 247, article id 110954Article in journal (Refereed)
    Abstract [en]

    Despite various preventive interventions, nosocomial cross-infection remains a significant challenge in healthcare facilities worldwide. Consequently, prolonged hospitalization, elevated healthcare costs, and mortality rates are major concerns. Proper ventilation has been identified as one of the possible interventions for reducing the risk of cross-infection between patients and healthcare workers in hospital wards by diluting infectious agents and their carrying particles. The use of air cleaners in conjunction with the ventilation system further reduces the concentration of indoor pathogens. This article presents a systematic review of the ventilation solutions employed in hospital wards where pathogen removal performance can be enhanced using air-cleaning techniques while maintaining the thermal comfort of patients and healthcare staff. We provide a comparative analysis of the performance of different ventilation strategies adopted in one-, two-, or multi-bed hospital wards. Additionally, we discuss the parameters that influence the aerosol removal efficiency of ventilation systems and review various air-cleaning technologies that can further complement the ventilation system to reduce contaminant concentrations. Finally, we review and discuss the impact of different ventilation strategies on the perceived thermal comfort of patients and healthcare workers. This study provides insights into the cross-contamination risks associated with various hospital ward setups and the vital role of the ventilation system in reducing the adverse effects of infection risk. The findings of this review will contribute to the development of effective ventilation solutions that ensure improved patient outcomes and the well-being of healthcare workers.

  • 227.
    Palm, Anders
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Greater Stockholm Fire Brigade, Malmskillnadsgatan 64, Stockholm, 113 83, Sweden.
    Kumm, Mia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Storm, A.
    Division of Safety and Transport, RISE Research Institutes of Sweden, PO Box 857, Borås, SE-501 15, Sweden.
    Lönnermark, A.
    Division of Safety and Transport, RISE Research Institutes of Sweden, PO Box 857, Borås, SE-501 15, Sweden.
    Breathing air consumption during different firefighting methods in underground mining environment2022In: Fire safety journal, ISSN 0379-7112, E-ISSN 1873-7226, Vol. 133, article id 103661Article in journal (Refereed)
    Abstract [en]

    The paper analyses the breathing air consumption among participating firefighters during full-scale tests performed in the Tistbrottet mine in Sweden 2013. The availability of breathing air during firefighting has in earlier work been identified as a critical tactical factor in underground firefighting. Results from the tests show that there are differences in the breathing air consumption and that this depends on the methods used, equipment and the workload. The use of BA-teams, i.e. firefighters equipped with breathing apparatuses, is a complex group activity where the largest breathing air consumer will set the limits for the whole team. Light equipment and a structured command and control during the activities will enhance the endurance and the firefighting performance. Equipment and methods affect both firefighting performance and the durability of the firefighting activities. Examples of tested methods and equipment during the test series are: different variations of conventional hose lay-out; CAFS; cutting extinguisher; and trolley for equipment and complementary air. The aid of additional air supply and the use of trolleys will support the activities but is dependent on a large degree of preparation and training to function properly. Based on the tests, it is concluded that the larger model of air bottles should be considered for distances longer than 75 m. 

  • 228.
    Pan, Shiyuan
    et al.
    College of artificial intelligence, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China.
    Shi, Xiaodan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dong, Beibei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhang, Haoran
    School of Urban Planning and Design, Peking University, No.2199 Lishui Road, Nanshan District, Shenzhen, Guangdong, 518055, China.
    Liang, Yongtu
    Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, China.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants2024In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 359, article id 130344Article in journal (Refereed)
    Abstract [en]

    Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass, including waste, and the dynamic operation of bio-CHP plants leads to large fluctuations in the flowrate and CO2 concentration of the flue gas (FG), which further affect the operation of post-combustion CO2 capture. To optimize the dynamic operation of CO2 capture, a reliable model to predict the FG flowrate and CO2 concentration in real time is essential. In this paper, a data-driven model based on the Transformer architecture is developed. The model validation shows that the root mean squared error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (PPMCC) of Transformer are 0.3553, 0.0189, and 0.8099 respectively for the prediction of FG flowrate; and 13.137, 0.0318, and 0.8336 respectively for the prediction of CO2 concentration. The potential impact of various meteorological parameters on model accuracy is also assessed by analyzing the Shapley value. It is found that temperature and direct horizontal irradiance (DHI) are the most important factors, which should be selected as input features. In addition, using the near-infrared (NIR) spectral data as input features is also found to be an effective way to improve the prediction accuracy. It can reduce RMSE and MAPE for CO2 concentration from 0.2982 to 0.2887 and 0.0158 to 0.0157 respectively, and RMSE and MAPE for FG flowrate from 4.9854 to 4.7537 and 0.0141 to 0.0121 respectively. The Transformer model is also compared to other models, including long short-term memory network (LSTM) and artificial neural network (ANN), which results show that the Transformer model is superior in predicting complex dynamic patterns and nonlinear relationships.

  • 229.
    Paris, Bas
    et al.
    Ctr Res & Technol Hellas, Inst Bioecon & Agrotechnol, Dimarchou Georgiadou 118, Volos 38333, Greece..
    Michas, Dimitris
    Ctr Res & Technol Hellas, Inst Bioecon & Agrotechnol, Dimarchou Georgiadou 118, Volos 38333, Greece..
    Balafoutis, Athanasios T.
    Ctr Res & Technol Hellas, Inst Bioecon & Agrotechnol, Dimarchou Georgiadou 118, Volos 38333, Greece..
    Nibbi, Leonardo
    Univ Florence, Dept Ind Engn, I-50139 Florence, Italy..
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Pimentel, Duarte
    TERINOV Parque Ciencia & Tecnol Ilha Terceira, P-9700702 Terra Cha, Portugal.;Univ Azores, Ctr Estudos Econ Aplicada Atlant CEEAplA, P-9500321 Ponta Delgada, Portugal..
    da Silva, Carlota
    TERINOV Parque Ciencia & Tecnol Ilha Terceira, P-9700702 Terra Cha, Portugal..
    Athanasopoulou, Elena
    Univ Peloponnese, Dept Business & Org Adm, Kalamata 24100, Greece..
    Petropoulos, Dimitrios
    Univ Peloponnese, Dept Agr, Kalamata 24100, Greece..
    Apostolopoulos, Nikolaos
    Univ Peloponnese, Dept Management Sci & Technol, Tripoli 22100, Greece..
    A Review of the Current Practices of Bioeconomy Education and Training in the EU2023In: Sustainability, E-ISSN 2071-1050, Vol. 15, no 2, article id 954Article, review/survey (Refereed)
    Abstract [en]

    This study conducts a review of the current practices of bioeconomy education and training in the EU; as well as the associated methodologies; techniques and approaches. In recent years; considerable efforts have been made towards developing appropriate bioeconomy education and training programs in order to support a transition towards a circular bioeconomy. This review separates bioeconomy education approaches along: higher education and academic approaches, vocational education and training (VET) and practical approaches, short-term training and education approaches, and other approaches. A range of training methodologies and techniques and pedagogical approaches are identified. The main commonalities found amongst these approaches are that they are generally problem based and interdisciplinary, and combine academic and experiential. Higher education approaches are generally based on traditional lecture/campus-based formats with some experiential approaches integrated. In contrast, VET approaches often combine academic and practical learning methods while focusing on developing practical skills. A range of short-term courses and other approaches to bioeconomy education are also reviewed.

  • 230.
    Pettinari, Matteo
    et al.
    Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy.
    Frate, Guido Francesco
    Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy.
    Tran, A. Phong
    Institute of Low-Carbon Industrial Processes, German Aerospace Center (DLR), 03046 Cottbus, Germany.
    Oehler, Johannes
    Institute of Low-Carbon Industrial Processes, German Aerospace Center (DLR), 03046 Cottbus, Germany.
    Stathopoulos, Panagiotis
    Institute of Low-Carbon Industrial Processes, German Aerospace Center (DLR)The institution will open in a new tab, Cottbus, 03046, Germany.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ferrari, Lorenzo
    Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy.
    Impact of the Regulation Strategy on the Transient Behavior of a Brayton Heat Pump2024In: Energies, E-ISSN 1996-1073, Vol. 17, no 5, article id 1020Article in journal (Refereed)
    Abstract [en]

    High-temperature heat pumps are a key technology for enabling the complete integration of renewables into the power grid. Although these systems may come with several variants, Brayton heat pumps are gaining more and more interest because of the higher heat sink temperatures and the potential to leverage already existing components in the industry. Because these systems utilize renewable electricity to supply high-temperature heat, they are particularly suited for industry or energy storage applications, thus prompting the development of various demonstration plants to evaluate their performance and flexibility. Adapting to varying load conditions and swiftly responding to load adjustments represent crucial aspects for advancing such systems. In this context, this study delves into assessing the transient capabilities of Brayton heat pumps during thermal load management. A transient model of an emerging prototype is presented, comprising thermal and volume dynamics of the components. Furthermore, two reference scenarios are examined to assess the transient performance of the system, namely a thermal load alteration due to an abrupt change in the desired heat sink temperature and, secondly, to a sudden variation in the sink mass flow rate. Finally, two control methodologies—motor/compressor speed variation and fluid inventory control—are analyzed in the latter scenario, and a comparative analysis of their effectiveness is discussed. Results indicate that varying the compressor speed allows for a response time in the 8–20 min range for heat sink temperature regulation (first scenario). However, the regulation time is conditioned by the maximum thermal stress sustained by the heat exchangers. In the latter scenario, regulating the compressor speed shows a faster response time than the inventory control (2–5 min vs. 15 min). However, the inventory approach provides higher COPs in part-load conditions and better stability during the transient phase.

  • 231.
    Piasecki, Adam
    et al.
    Nicolaus Copernicus Univ, Fac Earth Sci, Lwowska 1, PL-87100 Torun, Poland..
    Jurasz, Jakob
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. AGH Univ Sci & Technol, Dept Engn Management, Fac Management, Gramatyka 10, PL-30001 Krakow, Poland..
    Kies, Alexander
    Goethe Univ Frankfurt, Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany..
    Measurements and reanalysis data on wind speed and solar irradiation from energy generation perspectives at several locations in Poland2019In: SN Applied Sciences, ISSN 2523-3963, E-ISSN 2523-3971, Vol. 1, no 8, article id 865Article in journal (Refereed)
    Abstract [en]

    Energy system research requires input data with high temporal and spatial resolution. However, the measurements performed in meteorological stations are only available for selected locations. Currently a growing number of research papers on small and large-scale power systems utilizes data coming from satellite measurements and various reanalyses. Although many authors aimed at comparing various data sources on irradiation and wind speed there is a lack of such studies, which compare both resources simultaneously at the same location. In consequence, many studies which are entirely based on satellite/reanalysis data may not be representative. In this paper 15 locations in Poland have been selected where the National Institute of Meteorology and Water Management performs continuous measurements of wind speed, temperature and global irradiation on a horizontal surface. Hourly time series were obtained for the year 2012 and 2013. The renewable resources were converted into electrical energy, considering the performance of typical PV panels and wind turbines. The findings of this study are as follows: solar radiation (CAMS and ERA5) shows a good agreement with ground measurements, for hourly values the coefficient of correlations is greater than 0.9; for individual locations the energy yield from PV system can differ by up to 9% but on average (all locations) the simulated energy yield based on satellite data is higher by less than 0.5%; simulations for wind energy showed a large variability in results with differences in capacity factors reaching 15 percentage points.

  • 232.
    Puppala, H.
    et al.
    School of Engineering and Technology, BML Munjal University, Gurgaon, India.
    K Jha, S.
    Birla Institute of Technology and Science, Rajasthan, Pilani, India.
    Singh, A. P.
    Birla Institute of Technology and Science, Rajasthan, Pilani, India.
    Madurai Elavarasan, R.
    Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Identification and analysis of barriers for harnessing geothermal energy in India2022In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 186, p. 327-340Article in journal (Refereed)
    Abstract [en]

    The Indian Government envisaged generating 10 GW using geothermal power by 2030. Reaching this milestone is linked with numerous challenges, as geothermal exploitation in India is in the nascent stages. In this work, possible barrier categories and barriers to harness geothermal energy in India are identified with the help of literature review and questionnaire-based surveys. Fuzzy Delphi method is used to find the significant barriers among the listed. Subsequently, Fuzzy Analytical Hierarchy Process (Fuzzy AHP) is used to determine the relative dominance of each barrier category and the barriers within each category. Outcomes of this research show that the resource barrier category obtained highest priority. This category includes various barriers such as (i) conceptualization of geothermal reservoir, (ii) estimation of theoretical heat energy, (iii) determination of extractable power, and (iv) selection of suitable extraction schemes. Results suggest that a comprehensive conceptual model presenting the subsurface variation of thermo-hydro-geological parameters with depth at a geothermal field can support the accurate depiction of the available and extractable thermal potential. Stability of the obtained hierarchy is examined by sensitivity analysis. Findings of this study help to identify the barriers that can be reasonably encountered and to propose developmental activities to harness geothermal energy. 

  • 233.
    Qi, L.
    et al.
    School of Mechanical Engineering, Guizhou University, Guiyang Guizhou, 550025, China.
    Wang, Y.
    School of Mechanical Engineering, Guizhou University, Guiyang Guizhou, 550025, China.
    Song, J.
    School of Mechanical Engineering, Guizhou University, Guiyang Guizhou, 550025, China.
    Yin, C.
    School of Mechanical Engineering, Guizhou University, Guiyang Guizhou, 550025, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hongkong, China.
    Zhang, Z.
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
    Techno-economic assessment of implementing photovoltaic water villas in Maldives2023In: iScience, ISSN 2589-0042, Vol. 26, no 5, article id 106658Article in journal (Refereed)
    Abstract [en]

    Solar energy is considered to be an effective measure to alleviate the shortage of power supply in the Maldives. In this paper, a roof photovoltaic (PV) system integrated into water villas in the Maldives was investigated. Three islands—Ayada Maldives, Angaga Island Resort, and JA Manafaru, located in the southern, central, and northern parts of Maldives—were selected for a case study. The potential of PV installations in Ayada Maldives, Angaga Island Resort, and JA Manafaru reaches 1,410, 445, and 742 kW, with corresponding annual power generation of 2.04, 0.64, and 1.12 GWh, respectively. The profits over the life cycle of 25 years of the above three studied islands are 4.86, 1.52, and 2.90 million USD, respectively, with payback periods in the range of 6–7 years. 

  • 234.
    Qi, Lingfei
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
    Jiang, Mingkun
    Mälardalen University, School of Business, Society and Engineering. Key Laboratory of Pressure Systems and Safety (MOE), School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zero-energy snow removal system for track switch based on air forced field2021In: Energy Proceedings, Scanditale AB , 2021, Vol. 15Conference paper (Refereed)
    Abstract [en]

    With the rapid development of railway traffic in China's alpine regions, real-time self-powered snow removal is an important route for cleaning track switch, which currently still use manual sweeping or electrical heating. These two traditional snow removal methods require large amounts of manpower or electricity. Here we propose a zero-energy snow clearing device that can continuously remove snow for switch only by track vibration. This device mainly contains a motion conversion mechanism and an air compression component. The motion conversion mechanism can amplify the micro-vibration of the rail and act as a mechanical engine (ME) that drives the air compression component to generate high-pressure air to blow off the snow at the track switch. A prototype was manufactured to demonstrate the feasibility of the design. From the high-pressure air generated by rail vibration to the process of snow removal, it is a nature cycle of no external energy consumption.

  • 235.
    Qi, Lingfei
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.
    Pan, H.
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.
    Pan, Y.
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.
    Luo, D.
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhang, Z.
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.
    A review of vibration energy harvesting in rail transportation field2022In: iScience, ISSN 2589-0042, Vol. 25, no 3, article id 103849Article, review/survey (Refereed)
    Abstract [en]

    In this paper, we review, compare, and analyze previous studies on vibration energy harvesting and related technologies. First, the paper introduces the basic aspects of vibration energy acquisition in the railway environment, including vibration frequency, train speed, energy flow in the train, and vibration energy harvesting potential. Generally, the methods for scavenging vibration energy caused by passing trains can be divided into four categories: electromagnetic harvesters, piezoelectric harvesters, triboelectric harvesters, and hydraulic harvesters. The structure, output performance, merits, and disadvantages of different energy harvesting strategies are summarized and compared. The application of vibration energy harvesters is explained as supplying power to monitoring sensors on the line side and the vehicle side. Finally, the paper addresses the challenges and difficulties that have not been completely resolved in the current research literature, including system stability, durability, and economy. Some recommendations to fill these research gaps are put forward for further investigation.

  • 236.
    Qian, F.
    et al.
    Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China.
    Bogle, D.
    Department of Chemical Engineering, University College London, United Kingdom.
    Wang, M.
    Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield, S1 3JD, United Kingdom.
    Pistikopoulos, S.
    Texas A&M University, United States.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Artificial intelligence for smart energy systems in process industries2022In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 324, article id 119684Article in journal (Refereed)
  • 237.
    Qian, Kun
    et al.
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, Sønderborg, Denmark.
    Fachrizal, Reza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Munkhammar, Joakim
    Department of Civil and Industrial Engineering, Uppsala University, Sweden.
    Ebel, Thomas
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, Sønderborg, Denmark.
    Adam, Rebecca
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, Sønderborg, Denmark.
    Large-scale EV charging scheduling considering on-site PV generation by combining an aggregated model and sorting-based methods2024In: Sustainable cities and society, ISSN 2210-6707, Vol. 107, article id 105453Article in journal (Refereed)
    Abstract [en]

    Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

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  • 238.
    Qian, Zhen
    et al.
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Chen, Min
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.;Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China..
    Yang, Yue
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Zhong, Teng
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Zhang, Fan
    MIT, Senseable City Lab, Cambridge, MA 02139 USA..
    Zhu, Rui
    Hong Kong Polytech Univ, Dept Land Surveying & Geo Informat, Kowloon, Hong Kong, Peoples R China..
    Zhang, Kai
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Zhang, Zhixin
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;Nanjing Univ, Coll Geog & Marine, POB 2100913, Nanjing, Peoples R China..
    Sun, Zhuo
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Ma, Peilong
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Lu, Guonian
    Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China..
    Ye, Yu
    Tongji Univ, Dept Architecture, Coll Architecture & Urban Planning, Shanghai, Peoples R China..
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. KTH Royal Inst Technol, Dept Chem Engn, S-10044 Stockholm, Sweden..
    Vectorized dataset of roadside noise barriers in China using street view imagery2022In: Earth System Science Data, ISSN 1866-3508, E-ISSN 1866-3516, Vol. 14, no 9, p. 4057-4076Article in journal (Refereed)
    Abstract [en]

    Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6 x 10(6) Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F-1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08% +/- 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).

  • 239.
    Qiu, J.
    et al.
    Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Belgium.
    De Souza, M. F.
    Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Belgium.
    Wang, Xiaolin
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ok, Y. S.
    Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, South Korea.
    Meers, E.
    Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Belgium.
    Influence of biochar addition and plant management (cutting and time) on ryegrass growth and migration of As and Pb during phytostabilization2024In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 913, article id 169771Article in journal (Refereed)
    Abstract [en]

    Phytostabilization of metal-contaminated soils can be enabled or improved by biochar application. However, biochar-aided effects vary on biochar types, and little attention has been paid to plant management (time and cutting) to enhance phytostabilization efficiency in synergy with biochar. Therefore, biochars derived from pig manure (PM), Japanese knotweed (JK), and a mixture of both (P1J1) were applied to Pb and As mining soil with ryegrass cultivation to assess the biochar-induced effects on plant growth, dissolved organic matter (DOM), As and Pb mobility, and bioaccumulation within a phytostabilization strategy. Additional treatments involving the combined biochar (P1J1) and ryegrass were conducted to explore the influence of sequential cutting and growing time on facilitating phytostabilization efficacy. Biochar applications promoted plant growth, progressively increasing over time, but were not enhanced by cutting. Short and long-wavelength humic-like DOM substances identified in the soil pore water after biochar application varied depending on the biochar types used, providing evidence for the correlation among DOM changes, biochar origin, and metal immobilization. Biochar-treated soils exhibited reduced Pb availability and enhanced As mobility, with P1J1 stabilizing Pb significantly similar to PM while causing less As mobilization as JK did. The mobilized As did not result in increased plant As uptake; instead, all biochar-added plants showed a significant decrease in As and Pb concentrations compared to those without biochar. Soil available As decreased while available Pb increased with time, and cutting did not influence soil As behavior but did reduce soil Pb release. Nevertheless, plant As and Pb concentrations decreased over time, whereas those in multiple-cut plants were generally higher than those without cuts. Biochar, especially P1J1, along with growth time, holds promise in promoting plant biomass, reducing plant Pb and As concentrations, and minimizing the migration of Pb–As within the soil.

  • 240.
    Qiu, J.
    et al.
    Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Belgium.
    Fernandes de Souza, M.
    Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Belgium.
    Wang, Xiaolin
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Chafik, Y.
    INRA USC1328, LBLGC EA 1207, University of Orleans, France.
    Morabito, D.
    INRA USC1328, LBLGC EA 1207, University of Orleans, Rue de Chartres, BP 6759, Orléans Cedex 2, 45067, France.
    Ronsse, F.
    Thermochemical Conversion of Biomass Research Group, Department of Green Chemistry and Technology, Ghent University, Belgium.
    Ok, Y. S.
    Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, South Korea.
    Meers, E.
    Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Belgium.
    Dynamic performance of combined biochar from co-pyrolysis of pig manure with invasive weed: Effect of natural aging on Pb and As mobilization in polluted mining soil2024In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 935, article id 173424Article in journal (Refereed)
    Abstract [en]

    Due to the natural biochar aging, the improvement of soil quality and immobilization of soil pollutants achieved by biochar may change; understanding the dynamic evolution of the in situ performance of biochar in these roles is essential to discuss the long-term sustainability of biochar remediation. Therefore, in this study, combined biochar from co-pyrolysis of pig manure and invasive Japanese knotweed – P1J1, as well as pure pig manure – PM – and pure Japanese knotweed – JK – derived biochar were applied to investigate their remediation performance in a high As- and Pb-polluted soil with prolonged incubation periods (up to 360 days). Biochar application, especially P1J1 and PM, initially promoted soil pH, dissolved organic carbon, and EC, but the improvements were not constant through time. The JK-treated soil exhibited the highest increase of soil organic matter (OM), followed by P1J1 and then PM, and OM did not change with aging. Biochar, especially P1J1, was a comprehensive nutrient source of Ca, K, Mg, and P to improve soil fertility. However, while soluble cationic Ca, K, and Mg increased with time, anionic P decreased over time, indicating that continuous P availability might not be guaranteed with the aging process. The total microorganism content declined with time; adding biochars slowed down this tendency, which was more remarkable at the later incubation stage. Biochar significantly impeded soil Pb mobility but mobilized soil As, especially in PM- and P1J1-treated soils. However, mobilized As gradually re-fixed in the long run; meanwhile, the excellent Pb immobilization achieved by biochars was slightly reduced with time. The findings of this study offer fresh insights into the alterations in metal(loid)s mobility over an extended duration, suggesting that the potential mobilization risk of As is reduced while Pb mobility slightly increases over time.

  • 241.
    Qiu, R.
    et al.
    China University of Petroleum-Beijing, Beijing, China.
    Liang, Y.
    China University of Petroleum-Beijing, Beijing, China.
    Liao, Q.
    China University of Petroleum-Beijing, Beijing, China.
    Wei, X.
    China University of Petroleum-Beijing, Beijing, China.
    Zhang, H.
    China University of Petroleum-Beijing, Beijing, China.
    Jiao, Y.
    China University of Petroleum-Beijing, Beijing, China.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, Japan.
    A model-experience-driven method for the planning of refined product primary logistics2022In: Chemical Engineering Science, ISSN 0009-2509, E-ISSN 1873-4405, Vol. 254, article id 117607Article in journal (Refereed)
    Abstract [en]

    Logistics planning is regarded as the most complex part of supply chain management for refined products. A vital knowledge gap still exists in understanding the trade-offs between the economy and the practicability of logistics schemes. Focus on this issue, this paper proposes a model-experience-driven method for the planning of refined product primary logistics. The method couples three sub-modules: (1) use coordinator's preference information and convex function interpolation to construct satisfaction indicator; (2) set up a multi-objective model for logistics coordination and optimization considering supply adjustment and secondary delivery; (3) adopt the augmented ɛ-constraint method to obtain the Pareto solutions and balance the economy and satisfaction indicators. The method is verified by a small-scale system, where the satisfaction degree increases by 77% while the logistics cost remains unchanged. The method is also successfully applied to a large-scale system with 29 refineries and 196 market depots, where Pareto logistics schemes are obtained and the supply–demand imbalance is greatly eased. The proposed method can help provide theoretical guidance for real-world logistics planning.

  • 242.
    Qiu, R.
    et al.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Liao, Q.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Klemeš, J. J.
    Sustainable Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology – VUT Brno, Technická 2896/2, Brno, 616 69, Czech Republic.
    Liang, Y.
    National Engineering Laboratory for Pipeline Safety/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing, 102249, China.
    Guo, Z.
    Sinopec Engineering Incorporation, No.21, Anhui North Li'an Garden, Chaoyang District, Beijing, 100101, China.
    Chen, J.
    Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan.
    Zhang, Haoran
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan.
    Roadmap to urban energy internet with wind electricity-natural gas nexus: Economic and environmental analysis2022In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 245Article in journal (Refereed)
    Abstract [en]

    Electrolysis hydrogen generation technology is one of the feasible ways to alleviate the problem of wind electricity curtailment. One promising hydrogen value-added application is to blend hydrogen into the natural gas grid and sell it as the heat energy carrier. This paper aims to discuss the feasibility of a roadmap to urban energy internet with wind electricity-natural gas nexus. Firstly, a framework is raised to integrate wind electricity generation, electrolysis hydrogen generation, and hydrogen-natural gas blending systems. Secondly, a series of reasonable hydrogen supply profiles are provided based on annual electricity curtailment and realistic natural gas scheduling. Then, an energy optimisation model and a techno-economic model are applied to simulate the generation of electricity and hydrogen, as well as determine the most economical hydrogen supply scheme. Finally, a case study in the Beijing-Tianjin-Hebei region of China is taken to validate the benefits of the proposed roadmap. The preferred scheme is worked out with the net present value of 88.8 M$, including the economy configurations of the electricity-hydrogen hybrid generation system, as well as the hydrogen-natural gas blending plan. The results also indicate that annual electricity curtailment and annual carbon emission are decreased by 204 GWh (48.8%) and 40.2 kt (49.9%).

  • 243.
    Qiu, Rui
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. China Univ Petr, Natl Engn Lab Pipeline Safety, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China..
    Liao, Qi
    China Univ Petr, Natl Engn Lab Pipeline Safety, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China..
    Tu, Renfu
    China Univ Petr, Natl Engn Lab Pipeline Safety, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China..
    Jiao, Yingqi
    China Univ Petr, Natl Engn Lab Pipeline Safety, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China..
    Yang, An
    China Oil & Gas Pipeline Network Corp, Mkt Dept, Dongtucheng Rd 5, Beijing 100013, Peoples R China..
    Guo, Zhichao
    Sinopec Engn Inc, 21, Anhui North Lian Garden, Chaoyang Dist, Beijing 100101, Peoples R China..
    Liang, Yongtu
    China Univ Petr, Natl Engn Lab Pipeline Safety, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Beijing 102249, Peoples R China..
    Pipeline pricing and logistics planning in the refined product supply chain based on fair profit distribution2023In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 175, article id 108840Article in journal (Refereed)
    Abstract [en]

    The pipeline is an economical, safe and environmentally friendly way to deliver refined products, but the un-coordinated decisions of the pipeline carrier and the oil shipper can lead to low pipeline turnover and high cross -regional logistics costs. This paper intends to remedy this problem through pipeline pricing and logistics plan-ning. First, a framework is designed to coordinate the operational decisions of the pipeline carrier and the oil shipper. Then, a bi-level programming model is customized to characterize the decentralized decision-making process of both stakeholders, including pipeline pricing and logistics planning. The upper-level model maxi-mizes the transportation revenue of the pipeline carrier, and the lower-level model minimizes the logistics cost of the oil shipper. The model constraints supply and demand capacity, transportation capacity, transportation network structure and mass balance. Next, to realize the coordination of both stakeholders, a negotiation mechanism based on fair profit distribution is customized. Ultimately, the method is tested on a large-scale logistics system of refined products in China. The results reveal that: (1) the pipeline turnover is increased by 127 million ton-kilometers, (2) the economic benefits of both stakeholders are maximized with an increase of 13 million CNY, (3) a fairer profit distribution is provided compared with the centralized decision-making. It is proved that the proposed method has a satisfactory coordination effect on the pricing of the pipeline carrier and the logistics planning of the oil shipper.

  • 244.
    Qiu, Rui
    et al.
    China University of Petroleum-Beijing, China.
    Zhang, Haoran
    Peking University, China.
    Wang, Guotao
    The Hong Kong Polytechnic University, Hong Kong.
    Liang, Yongtu
    China University of Petroleum-Beijing, China.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong.
    Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 350, article id 121716Article in journal (Refereed)
    Abstract [en]

    Power-to-gas (P2G) is a promising solution to the issue of non-dispatchable renewable power generation. However, the high investment costs and low energy efficiency of P2G systems pose challenges. This study designs a green hydrogen-based Energy Storage as a Service (ESaaS) mode to improve the economic efficiency of P2G systems. In this ESaaS mode, the P2G system acts as an energy trading hub. The ESaaS operator manages the system and enables microgrids to access energy storage services. In return, the ESaaS operator generates revenue through electricity and hydrogen trading. To characterize stakeholders' behaviors, we develop a cost-minimizing model for multi-energy microgrid operation and a revenue-maximizing model for P2G investment and operation. To coordinate stakeholders involved in cooperation, we employ Nash bargaining-based model reformulation to determine investment and operation decisions and benefit distribution. Then, logarithm transformation and trading price discretization are introduced to efficiently solve the non-convex and nonlinear reformulated model. Simulation experiments using Shanghai's electricity market and meteorological data demonstrate that the ESaaS mode achieves 100% recovery of surplus renewable electricity and increases the proportion of renewable energy in microgrids from 59% to 83%. The results suggest that green hydrogen-based ESaaS is a feasible solution to increase the utilization rate of renewable energy and bring considerable economic and environmental benefits to all stakeholders.

  • 245.
    Rabhi, Achref
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Numerical Modelling of Subcooled Nucleate Boiling for Thermal Management Solutions Using OpenFOAM2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Two-phase cooling solutions employing subcooled nucleate boiling flows e.g. thermosyphons, have gained a special interest during the last few decades. This interest stems from their enhanced ability to remove extremely high heat fluxes, while keeping a uniform surface temperature. Consequently, modelling and predicting boiling flows is very important, in order to optimise the two-phase cooling operation and to increase the involved heat transfer coefficients. 

    In this work, a subcooled boiling model is implemented in the open-source code OpenFOAM to improve and extend its existing solver reactingTwoPhaseEulerFoam dedicated to model boiling flows. These flows are modelled using Computational Fluid Dynamics (CFD) following the Eulerian two-fluid approach. The simulations are used to evaluate and analyse the existing Active Nucleation Site Density (ANSD) models in the literature. Based on this evaluation, the accuracy of the CFD simulations using existing boiling sub-models is determined, and features leading to improve this accuracy are highlighted. In addition, the CFD simulations are used to perform a sensitivity analysis of the interfacial forces acting on bubbles during boiling flows. Finally, CFD simulation data is employed to study the Onset of Nucleate Boiling (ONB) and to propose a new model for this boiling sub-model, with an improved prediction accuracy and extended validity range.

    It is shown in this work that predictions associated with existing boiling sub-models are not accurate, and such sub-models need to take into account several convective boiling quantities to improve their accuracy. These quantities are the thermophysical properties of the involved materials, liquid and vapour thermodynamic properties and the heated surface micro-structure properties. Regarding the interfacial momentum transfer, it is shown that all the interfacial forces have considerable effects on boiling, except the lift force, which can be neglected without influencing the simulations' output. The new proposed ONB model takes into account convective boiling features, and it able to predict the ONB with a very good accuracy with a standard deviation of 2.7% or 0.1 K. This new ONB model is valid for a wide range of inlet Reynolds numbers, covering both regimes, laminar and turbulent, and a wide range of inlet subcoolings and applied heat fluxes.

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  • 246.
    Rabhi, Achref
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bel Fdhila, Rebei
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A One-Dimensional Thermo-Hydraulic Steady-State Modelling Approach For Two-Phase Loop Thermosyphons2022In: 16th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, 2022Conference paper (Refereed)
    Abstract [en]

    The interest in using Two-Phase Loop Thermosyphons(TPLT) for heat recovery and energy saving within different in-dustrial processes has been in rise on the last few decades. Thesedevices are characterized by geometrical flexibility, as well asenhanced heat exchange rates. However, TPLT operation in-volves complex physical mechanisms, where different flow andheat transfer regimes are encountered. These regimes are crucialto be assessed and understood, in order to successfully predictand optimize the TPLT operation.

    In this paper, a comprehensive one-dimensional thermo-hydraulic modelling approach is developed and presented in or-der to simulate the TPLT operation. The novelty of this modellies in the exhibition of the different experienced complex flowpatterns, heat transfer regimes and physical mechanisms, includ-ing the dry-out prediction and reporting. This modelling frame-work is based on the separated two-fluid model coupled withmass, momentum and energy balances as well as relevant ther-modynamic constraints. The obtained results are compared to theavailable experimental measurements from literature, and a goodagreement is found with a maximum prediction error of 7%.

    Furthermore, a sensitivity analysis is performed aiming todetermine the effect of the operating saturation temperature, andtherefore the filling ratio, on the average heat transfer coefficientof the TPLT’s evaporator. Optimal values leading to enhance theheat removal are proposed and discussed at the end of this paper.

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    A ONE-DIMENSIONAL THERMO-HYDRAULIC STEADY-STATE MODELLING APPROACH FOR TWO-PHASE LOOP THERMOSYPHONS
  • 247.
    Renuke, Avinash
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Reggio, F.
    University of Genoa, Genoa, Italy.
    Traverso, A.
    University of Genoa, Genoa, Italy.
    Pascenti, M.
    University of Genoa, Genoa, Italy.
    Silvestri, P.
    Sit Technologies Srl. Genoa, Italy.
    High-Efficient Bladeless Expander Concept2023In: E3S Web of Conferences, EDP Sciences , 2023Conference paper (Refereed)
    Abstract [en]

    Tesla bladeless expanders are promising in energy harvesting and small-scale power generation applications due to their lower cost and simplicity in design. Although such expanders exhibit very high rotor efficiency (analytical total to static efficiency ~ 90%), it shows poor performance when coupled with a stator (experimental total to static efficiency ~30%) due to losses present in the stator and stator-rotor interaction. This paper presents the design and experiment of a novel, high-efficient Tesla bladeless expander concept. The concept arises from the loss phenomena in the stator-rotor interaction in conventional bladeless expanders, which are among the main causes of the low performance. This concept is believed to bring the bladeless expanders to the same performances as the traditional ones with vanes, compared to which however the bladeless machines boast greater simplicity, robustness, and the absence of performance decay as the size decreases, competing even in the contexts for traditional turbomachinery. The high-efficient bladeless expander prototype with water as a working fluid is designed and developed, representing the similitude case for a liquid butane heat pump. The available isentropic power across the throttling process in the butane case is 3.3 kW @10000 rpm. The turbine consists of 24 nozzles and 150 disks separated by 0.1 mm spacers. The turbine shaft is connected to the high-speed electric generator. The performance test on the expander is carried out at rotational speeds ranging from 3000 rpm to 6200 rpm and with differential pressure across the expander up to 14 bar. Experimental ventilation loss is characterised and its effect on the performance of the expander is discussed. The preliminary results of the expander under investigation showed satisfactory production of power with an acceptable efficiency range. It is also shown that the present concept is promising and able to address the major i.e., stator-rotor interaction which is the major source of loss in the traditional bladeless expander. 

  • 248.
    Renuke, Avinash
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Traverso, A.
    University of Genoa, Genoa, Italy.
    Kalfas, A.
    Aristotle University of Thessaloniki, Thessaloniki, Greece.
    An Updated Design Procedure for Tesla Turbines2023In: E3S Web of Conferences, EDP Sciences , 2023Conference paper (Refereed)
    Abstract [en]

    Tesla turbine rotor, a special case of the flow between two corotating disks, has been studied in the past analytically and the performance is discussed both qualitatively and quantitatively. However, there is no systematic design criteria/process given to design the rotor of a Tesla expander in the peer-reviewed literature. Such design procedure, presented in this article, allows researchers and engineers to design and optimise the rotor for a given fluid and design condition (Power, flow and rotational speed). In this article, we present a 0-D design methodology to calculate rotor design parameters such as disk diameters, the gap between disks, the number of disks and the rotational speed of the expander, and efficiency and power estimation. This design procedure is based on the correlations and optimal ranges present in the literature. The 0-D model discussed in this article is a promising design approach to the preliminary design of the Tesla rotor and then further fine-tuning could be done based on the CFD simulations when coupled with the stator. A case study is presented with a 3-kW air bladeless expander prototype in which the rotor is designed using the 0-D model approach and compared with 2D Computational Fluid Dynamics results. 

  • 249.
    Renuke, Avinash
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Traverso, A.
    University of Genoa, Genoa, Italy.
    Pascenti, M.
    SIT Technologies srl, Genoa, Italy.
    Reggio, F.
    University of Genoa, Genoa, Italy.
    Silvestri, P.
    University of Genoa, Genoa, Italy.
    Performance Investigation Of Stator-Less And Blade-Less Radial Expander2023In: Proc. ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2023Conference paper (Refereed)
    Abstract [en]

    Interests in small-scale turbomachinery are gaining momentum, particularly around waste heat recovery using Organic Rankine Cycle (ORC), energy harvesting, pico-hydro, refrigeration and heat pumps and small-scale power generation. These applications demand to have economical, simple construction, and reasonably efficient machines. The performance of bladed turbomachine at a small scale is poor mainly due to viscous losses and relatively large clearances. In some cases, like ORC, it requires a lubrication system, making it complex and costly. Bladeless or Tesla turbomachinery is seen as one of the solutions for these applications due to its simple construction and cost-effectiveness. However, the experimental efficiency of the bladeless turbines/compressors is found in the low region, < 40%. In this article, the performance of a bladeless turbine is investigated using a vaneless volute configuration, making the turbine stator-less (vaneless volute) and bladeless (vaneless rotor). This study presents numerical and experimental performance investigation with a volute as a stator of the bladeless rotor. 3D Numerical results show very promising performance of the turbine with total to static efficiencies calculated above 65%. In the second part of the article, turbine prototype components, assembly and test set-up are discussed. Experimental maximum efficiency of 41.5±0.88% at 3.5 kg/s@5000 rpm and power of 915 W is obtained. This is the highest recorded efficiency for the Tesla turbine in peer-reviewed research. The overall turbine performance from 3D numerical simulation with ventilation and mechanical losses is compared with experimental results. This work demonstrates that the proposed stator-less/volute configuration provides an efficient way for bladeless or Tesla turbines, particularly for low-head applications.

  • 250.
    Rezazadeh, M. R.
    et al.
    School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
    Dastan, A.
    Department of Mechanical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
    Sadrizadeh, Sasan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Department of Civil and Architectural Engineering, KTH University, Stockholm, Sweden.
    Abouali, O.
    School of Mechanical Engineering, Shiraz University, Shiraz, Iran. Department of Civil and Architectural Engineering, KTH University, Stockholm, Sweden.
    A quasi-realistic computational model development and flow field study of the human upper and central airways2024In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444Article in journal (Refereed)
    Abstract [en]

    The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka’s deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka’s model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15 l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract. Graphical Abstract: (Figure presented.)

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