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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Andersson, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Andersson, Tim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tomas Aparicio, Elena
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Mälarenergi AB, Sweden.
    Baaz, Hampus
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE SICS Västerås, Sweden.
    Bergström, Albert
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bengtsson, Daniel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Orisio, Daniele
    State Inst Higher Educ Guglielmo Marconi, Dalmine, Italy..
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A Machine Learning Approach for Biomass Characterization2019In: Energy Procedia, ISSN 1876-6102, p. 1279-1287Article in journal (Refereed)
    Abstract [en]

    The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

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  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Axelsson, Jakob
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hatvani, Leo
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Olsson, Anders
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Schwede, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Sjödin, Carina
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dilemmas in designing e-learning experiences for professionals2021In: Proceedings of the European Conference on e-Learning, ECEL, 2021, p. 10-17Conference paper (Refereed)
    Abstract [en]

    The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

  • 3.
    Avelin, Anders
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aulin, Robert
    Swedish University of Agricultural Sciences, Sweden.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Forest biomass for bioenergy production – comparison of different forest species2014In: / [ed] J. Yan, 2014Conference paper (Refereed)
    Abstract [en]

    Forest biomass is a renewable and sustainable source of energy that can be used for producing electricity, heat, and biofuels. The production of biomass for energy is considered to be an important step in developing sustainable communities and managing greenhouse gas emissions effectively. Biomass properties vary and are commonly associated with plant species. Hence, efficient methods to predict biofuel characteristics will greatly affect the utilization and management of feedstock production. In this paper attempt was made to correlate various chemical characteristics with NIR spectra. Wood chips from various plant species was analyzed for lignin content, heating value, ash content and NIR and the results were evaluated with correlation, PCA and PCR. Initial evaluation showed promising results where chemical components in the wood correlate to NIR spectra. A selection of results will be presented in this paper. Further analysis as well as results from PCA and PCR models will be presented in the full paper version.

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  • 4.
    Dahlquist, Erik
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rahman, Moksadur
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    AI Overview: Methods and Structures2021In: AI and Learning Systems - Industrial Applications and Future Directions / [ed] Konstantinos Kyprianidis and Erik Dahlquist, IntechIntechOpen , 2021, 1Chapter in book (Refereed)
    Abstract [en]

    This paper presents an overview of different methods used in what is normally called AI-methods today. The methods have been there for many years, but now have built a platform of methods complementing each other and forming a cluster of tools to be used to build “learning systems”. Physical and statistical models are used together and complemented with data cleaning and sorting. Models are then used for many different applications like output prediction, soft sensors, fault detection, diagnostics, decision support, classifications, process optimization, model predictive control, maintenance on demand and production planning. In this chapter we try to give an overview of a number of methods, and how they can be utilized in process industry applications.

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  • 5.
    Dong, Beibei
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Hu, Changzheng
    Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, Tianjin, 300134, China.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Selecting the approach for dynamic modelling of CO2 capture in biomass/waste fired CHP plants2023In: International Journal of Greenhouse Gas Control, ISSN 1750-5836, E-ISSN 1878-0148, Vol. 130, article id 104008Article in journal (Refereed)
    Abstract [en]

    Integrating CO2 capture with biomass/waste fired combined heat and power (CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass/waste and the dynamic operation of CHP plants result in bigger fluctuations in the properties of flue gas (FG), e.g. CO2 concentration (CO2vol%) and flowrates, and the heat that can be used for CO2 capture, when comparing with coal fired power plants. To address such a challenge, dynamic modelling is essential to accurately estimate the amount of captured CO2 and optimize the operation of CO2 capture. This paper compares three dynamic approaches commonly used in literature, namely using the ideal static model (IST) and using dynamic models without control (Dw/oC) and with control (DwC), for MEA based chemical absorption CO2 capture. The performance of approaches is assessed under the variations of key factors, including the flowrate and CO2vol% of FG, and the available heat for CO2 capture. Simulation results show clear differences. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, DwC gives the highest amount of captured CO2, which is 7.3 % and 22.3 % higher than IST and Dw/oC, respectively. It is also found that the time step size has a clear impact on the CO2 capture amount, especially for DwC. Based on the results, suggestions are also provided regarding the selection of dynamic modelling approaches for different purposes of simulations.

  • 6.
    Gorji, Reyhaneh
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy2024In: Horticulturae, E-ISSN 2311-7524, Vol. 10, no 4, p. 336-336Article in journal (Refereed)
    Abstract [en]

    Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

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  • 7.
    Hawas, Allan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Building Thermography Inspection by using a Low-Cost Visual-Thermal Drone System2021Conference paper (Refereed)
    Abstract [en]

    For decades, thermal imaging technology serves as an effective tool and is implemented in many industrial and commercial applications, including energy and building sectors. Recent trends in the field further show an increased interest in aerial thermal imaging applications that brings new opportunities toward sustainability. Unmanned aerial vehicles (UAVs) (i.e., drones) equipped with thermal cameras are currently used for building thermography inspection, which is a crucial technology to accelerate the identification of CO2 mitigation within the building sector to tackle the global goals (SDG 11 target 11.6 and SDG 7 target 7.3). This study presents an evaluation of a low-cost, visual-thermal drone system for building thermography inspection (SDG 9 target 5.5). The evaluation was limited to the thermal imaging potentials of the system. The UAV system is used to examine its capacity to detect various heat loss, including insulation defects, air/ water leakage, and validation of different suspected energy loss case studies. The examination also involves the evaluation of the cost-effectiveness of the thermal-drone system. The thermography inspection was carried out on several buildings with different sizes, types, and activities they are used for. Therefore, the detection/identification tasks for the thermal-drone system were different from an inspection to another. This study aimed to identify different limitations and advantages of using such a low-cost thermal-drone system for building thermography inspection. The technical evaluation was based on several criteria, including fly duration, stability, image quality, data flexibility, integration potentials, etc. Additionally, the cost-effectiveness and other practical aspects were considered in the evaluation. The results show a combination of both limitations and advantages of adopting such a low-cost drone system. In contrast to the supplier's description, the thermal image data are not a radiometric JPG file that significantly limits quality and opportunities. Accordingly, the thermal image gives a standard JPG file and does not provide a temperature distribution to make any post-analysis processing or post-editing presentations. This issue can be solved partially, as the live thermal images provide a temperature distribution that allows different utilizations, e.g., identifying temperature spots, which can be included in a screenshot of the drone screen controller. Furthermore, the image data's limitations do not allow 3D modelling of the building objects which is possible for the radiometric image files. The image resolution and accuracy are limited; however, short distance inspections provide good image qualities. The results reveal that the thermal drone system can detect common insulation issues such as missing insulation and clear energy loss. However, the capacity is limited in regards to high accuracy demand and more in-depth data analysis. In conclusion, the examined drone system is a cost-effective tool for DIY use and superficial aerial building thermography inspection (SDG 11 target 11.6). Therefore the suggested system is not sufficient for higher demand and more professional inspections. The suggested proposal is an effective method to identify CO2 mitigation potentials within the buildings that are significantly promoting the achievement of some SDGs. Additionally, the inspection method can be conducted remotely, keeping social distancing in the time of pandemics.

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  • 8.
    Kyprianidis, Konstantinos
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Karlsson, Mikael
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tryzell, Robert
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ševcik, Martin
    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.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    On-line Powerplant Control using Near-InfraRed Spectroscopy: OPtiC-NIRS, REPORT 2021:7462021Report (Other academic)
    Abstract [en]

    Near InfraRed Spectroscopy (NIRS) offers rapid on-line analysis of biomass feedstocks and can be utilized for process control of biomass- based combined heat and power plants. Within the OPtiC-NIRS project we have carried out a full-scale on-site testing of different NIRS for online powerplant control at the facilities of Mälarenergi and Eskilstuna Strängnäs Energi och Miljö. 

    The project has been focused on developing and testing robust NIRS soft-sensors for fuel higher heating value and composition (incl. moisture, components such as recycle wood and glass, different type of plastics and ash) and combining them with dynamic models for on-line feed-forward process monitoring and control. Expected benefits include reduced risk of agglomeration and pollutant emissions formation as well as improved production control. A longer-term potential and ambition is to be able to identify the fossil content in municipal waste fuel, which can hopefully be addressed in a follow-up study. 

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    on-line-powerplant-control-using-near-infrared-spectroscopy-energiforskrapport-2021-746
  • 9.
    Kyprianidis, Konstantinos
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, JanMälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Developments in Combustion Technology2016Collection (editor) (Other academic)
    Abstract [en]

    Over the past few decades, exciting developments have taken place in the field of combustion technology. The present edited volume intends to cover recent developments and provide a broad perspective of the key challenges that characterize the field. The target audience for this book includes engineers involved in combustion system design, operational planning and maintenance. Manufacturers and combustion technology researchers will also benefit from the timely and accurate information provided in this work. The volume is organized into five main sections comprising 15 chapters overall: - Coal and Biofuel Combustion - Waste Combustion - Combustion and Biofuels in Reciprocating Engines - Chemical Looping and Catalysis - Fundamental and Emerging Topics in Combustion Technology

  • 10.
    Kyprianidis, Konstantinos
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, JanMälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Developments in Near-Infrared Spectroscopy2017Collection (editor) (Other academic)
    Abstract [en]

    Over the past few decades, exciting developments have taken place in the field of near-infrared spectroscopy (NIRS). This has been enabled by the advent of robust Fourier transform interferometers and diode array solutions, coupled with complex chemometric methods that can easily be executed using modern microprocessors. The present edited volume intends to cover recent developments in NIRS and provide a broad perspective of some of the challenges that characterize the field. The volume comprises six chapters overall and covers several sectors. The target audience for this book includes engineers, practitioners, and researchers involved in NIRS system design and utilization in different applications. We believe that they will greatly benefit from the timely and accurate information provided in this work.

  • 11.
    Mirmoshtaghi, Guilnaz
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    The influence of different parameters on biomass gasification in circulating fluidized bed gasifiers2016In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 126, p. 110-123Article in journal (Refereed)
    Abstract [en]

    The mechanism of biomass gasification has been studied for decades. However, for circulating fluidized bed (CFB) gasifiers, the impacts of different parameters on the gas quality and gasifiers performance have still not been fully investigated. In this paper, different CFB gasifiers have been analyzed by multivariate analysis statistical tools to identify the hidden interrelation between operating parameters and product gas quality, the most influencing input parameters and the optimum points for operation. The results show that equivalence ratio (ER), bed material, temperature, particle size and carbon content of the biomass are the input parameters influencing the output of the gasifier the most. Investigating among the input parameters with opposite impact on product gas quality, cases with optimal gas quality can result in high tar yield and low carbon conversion while low tar yield and high carbon conversion can result in product gas with low quality. However using Olivine as the bed material and setting ER value around 0.3, steam to biomass ratio to 0.7 and using biomass with 3 mm particle size and 9 wt% moisture content can result in optimal product gas with low tar yield.

  • 12.
    Mirmoshtaghi, Guilnaz
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Mälardalen Högskola.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    INVESTIGATION OF EFFECTIVE PARAMETERS ON BIOMASS GASIFICATION IN CIRCULATING FLUIDIZED BED GASIFIERS2015Conference paper (Refereed)
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  • 13.
    Mirmoshtaghi, Guilnaz
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    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.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Investigation of Most Effective Parameters on Biomass Gasufication in Circulating Fluidized bed Gasifiers2015In: Forest and Plant Bioproducts Division 2015 - Core Programming Area at the 2015 AIChE Annual Meeting, 2015, p. 189-200Conference paper (Refereed)
  • 14.
    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).

  • 15.
    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.

  • 16.
    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.

  • 17.
    Sevcik, Martin
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tomas Aparicio, Elena
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Mälarenergi AB, Västerås, Sweden.
    Applications of hyperspectral imaging and machine learning methods for real-time classification of waste stream components2019Conference paper (Refereed)
    Abstract [en]

    Near-infrared (NIR) hyperspectral imaging (HSI) was applied together with machine learning methods to enable classification of typical municipal solid waste (MSW) components such as paper, biomass, food residues, plastics, textile and incombustibles. Classification models were developed using partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and radial-basis neural network (RBNN). The overall accuracy of SVM model calculated from classification sensitivity was 85% in prediction pixel by pixel for external sample set. The model outperformed other models in identifying incombustible material but it had higher computational time requirements. The accuracy of RBNN model reached 85% in prediction while being approx. 10 times faster. Minimum computational time was required by PLS-DA model reaching lower accuracy of 81% in prediction. The result indicate that developed models can be successfully used for real-time MSW component classification. NIR hyperspectral imaging coupled with machine learning methods has a large potential to be used for on-line material identification at waste sorting facilities or for pre-sorting at waste-to-energy powerplants.

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  • 18.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Near-Infrared Spectroscopy and Extractive Probe Sampling for Biomass and Combustion Characterization2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Biomass is characterized by highly variable properties. It can be converted to more valuable energy forms and products through a variety of conversion processes. This thesis focuses on addressing several important issues related to combustion and pulping.

    Experimental investigations were carried out on a biomass-fired industrial fluidized-bed boiler. The observed combustion asymmetry was explained by an imbalance in the fuel feed. Increased levels of carbon monoxide were detected close to boiler walls which contribute significantly to the risk of wall corrosion.

    Moreover, extensive literature analysis showed that near-infrared spectroscopy (NIRS) has a great potential to provide property information for heterogeneous feedstocks or products, and to directly monitor processes producing/processing biofuels in real-time. The developed NIRS-based models were able to predict characteristics such as heating value, ash content and glass content. A study focusing on the influence of different spectra acquisition parameters on lignin quantification was carried out. Spectral data acquired on moving woodchips were found to increase the representativeness of the spectral measurements leading to improvements in model performance.

    The present thesis demonstrates the potential of developing NIRS-based soft-sensors for characterization of biomass properties. The on-line installation of such sensors in an industrial setting can enable feed-forward process control, diagnostics and optimization.

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  • 19.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Sandberg, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    The experimental study of full-scale biomass-fired bubbling fluidized bed boiler2014In: Energy Procedia, ISSN 1876-6102, Vol. 61, p. 643-647Article in journal (Refereed)
    Abstract [en]

    This paper presents experimental data concerning combustion characteristics of full-scale biomass-fired bubbling fluidized bed (BFB) steam boiler with a thermal output of 31 MW. The purpose of the experimental measurements is to show how the values of selected combustion parameters vary in reality depending on measurement position. Experimentation involves specifically a determination of combustion gas temperature and concentration of gas species i.e. O2, CO2, CO and NOX at different positions in the furnace and the flue gas trains. Character of results from the furnace indicates the intermediate stage of thermochemical reactions. Increased levels of CO close to the wall have been found, this may be indicating reducing atmosphere and thereby increased corrosion risk. Results from flue gas trains demonstrate that behavior there is related to the fluid dynamics and heat transfer, the temperature is too low for further combustion reactions. Results show great variations among measured values of all measurands depending on a distance along the line from the wall to the center of the boiler. The measurements from permanently installed fixed sensors are not giving value representing average conditions, but overall profiles can be correlated to online measurements from fixed sensors.

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  • 20.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Khalesimoghadam, Seyedpedram
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Soibam, Jerol
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Application of single-point and hyperspectral imaging near-infrared sensors and machine learning algorithms for real-time biomass characterization2019Conference paper (Refereed)
    Abstract [en]

    Biomass is typically a material with highly variable properties making its use in industrial combustion processes challenging due to requirements on the steady operation. Property such as moisture content has an impact on fuel ignition characteristics and heat release from the biomass. Ash content negatively influences fluidization of the boiler bed and after-burning of small fuel particles, by forming an impermeable layer on the surface resulting in incomplete combustion and formation of harmful emissions.

    The large variability of the properties thus creates undesired process instabilities which need to be addressed in a timely manner by appropriate operational/regulatory measures adjusting e.g. fluidization velocity, distribution of combustion air, under-pressure in the furnace etc. Consequently, there is a need for the implementation of sensors able to measure the properties of interest in real-time. In our previous studies, we demonstrated the ability of a single-point near-infrared sensor to measure fuel properties in real-time in a laboratory environment. However, we found that there is limited representativeness of the single-point measurements as also a cross-sectional variation of the fuel properties on the conveyor belt was apparent.

    Therefore, the implementation of a sensor able to measure also a spatial distribution of the material in the biomass stream is suggested. Literature review shows that it can be achieved by the implementation of a near-infrared hyperspectral imaging camera.

    The aim of the work is to present research activities at the Future Energy Center, Mälardalen University leading towards the installation of a) single-point and b) hyperspectral imaging near-infrared sensors for real-time moisture and ash content measurements. The study further presents the concept of NIR sensors integration for process optimization and the introduction of new advanced control concepts for steam boilers.

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  • 21.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Application of Near Infrared Spectroscopy for Rapid Characterization of Feedstock Material in Pulp and Paper Industry2015In: Book of abstracts, 2015Conference paper (Refereed)
    Abstract [en]

    Pulp digesters can be continuous or batch reactors with significant residence time which are fed with woodchips and cooking chemicals. They deliver the pulp-fibers that are used in the production of paper, as well as black liquor that is combusted in the chemical recovery boiler. The possibility to measure what is happening inside the digester is limited. The most important quality properties of the feedstock material is content of lignin, which is being dissolved during the process, and related material reactivity. Pulp quality after the process is measured by Kappa number which is a measure of residual lignin in the pulp. One of the biggest challenges in pulp production process is the great variability in feedstock material properties. If the process is not adjusted by well-timed and appropriate operational control measures i.e. control of inlet and outlet flows and setting of the cooking recipe, it will result in the large variations in Kappa number, lower fiber quality or excess use of environmentally harmful cooking chemicals. This becomes particularly important during the swing between softwood and hardwood as part of meeting the final paper product quality requirements. Therefore, a rapid method that is capable of continuous feedstock material characterization is required.Near infrared (NIR) spectroscopy can be used for non-destructive characterization of the feedstock material. In this study, both Fourier transform and grating NIR spectrophotometers were used for NIR absorbance spectra acquisition. Each spectrum was recorded in the range between 700 and 2500 nm. During the calibration of spectra of various wood species with known lignin content, wood samples were placed on a tray so that the tray may move horizontally in a reciprocating manner underneath the sensor while maintaining the constant distance between the sensor and sample. This was done in order to simulate the movement of a real conveyor belt as used for transporting feedstock to the digester. In the on-line application the NIR meter is situated above the conveyor belt that wood up to the digester.Spectral data were pretreated with different methods such as normalization, scatter correction, smoothing, first and second derivative (Savitzky-Golay algorithm), selection of different spectral ranges and its combinations. Mathematical models to estimate lignin content were constructed using Partial Least Square Regression (PLS-R) and Principle component regression (PCR) statistical methods. Response data for model build-up were determined in the chemical laboratory according to standardized procedures including test repetitions. Different combinations of NIR instrument used, pre-treatment methods and statistical methods were evaluated in order to find the model with the best prediction performance.Results are promising and demonstrate that it is possible to characterize the lignin content and reactivity of the feedstock material by NIR spectrophotometers with reasonable prediction model performance. Improved prediction can be obtained if only selected spectral ranges are included as an input for statistical modelling; similarly using derivatives is better than using the raw spectrum. In the next step, developed statistical models for rapid lignin content prediction will be used as a feed-forward input for dynamic process control.

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  • 22.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Effect of wood chip moving velocity on NIR spectra acquisition and model calibration for lignin quantificationManuscript (preprint) (Other academic)
  • 23.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fast Determination of Fuel Properties in Solid Biofuel Mixtures by Near Infrared Spectroscopy2017In: Energy Procedia, ISSN 1876-6102, Vol. 105, p. 1309-1317Article in journal (Refereed)
    Abstract [en]

    This paper focuses on the characterization of highly variable biofuel properties such as moisture content, ash content and higher heating value by near-infrared (NIR) spectroscopy. Experiments were performed on different biofuel sample mixtures consisting of stem wood chips, forest residue chips, bark, sawdust, and peat. NIR scans were performed using a Fourier transform NIR instrument, and reference values were obtained according to standardized laboratory methods. Spectral data were pre-processed by Multiplicative scatter correction correcting light scattering and change in a path length for each sample. Multivariate calibration was carried out employing Partial least squares regression while absorbance values from full NIR spectral range (12,000–4000 cm-1), and reference values were used as inputs. It was demonstrated that different solid biofuel properties can be measured by means of NIR spectroscopy. The accuracy of the models is satisfactory for industrial implementation towards improved process control. 

  • 24.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fast Determination of Lignin Content in Feedstock Material for Pulping Process Monitoring and Optimization2015In: ICAVS 8 - Abstracts poster, 2015, p. 556-557Conference paper (Refereed)
    Abstract [en]

    Pulping process is delivering pulp fibers which are further used in the production of paper. The reactor is fed with feedstock material in the form of wood chips. Moreover, cooking chemicals are brought at several points into the reactor. Previous studies have shown that the knowledge of the feedstock material properties which are highly variable is limited. One of the most important parameters is the lignin content, which has to be dissolved, this requires a significant residence time. The residual lignin in the resulting pulp after the process is measured in the form of Kappa number. Inappropriate application of cooking chemicals could lead to large variations in the Kappa number, low fiber quality and other issues. Therefore continuous characterization of the feedstock material is required. One of the available methods for nondestructive characterization of feedstock material is NIR spectroscopy. Presented study is conducted in order to assess the possibility of determining lignin content using NIR method. The spectroscopy workflow consist of four major steps i.e. sample preparation, spectral data acquisition, data pre-processing and multivariate calibration. We used test samples from 13 different tree species, which were tested in the form of wood chips, pulverized wood and mixture of both. Acquired spectral data were pre-processed mainly by second derivative and standard normal variate transformation. PLS regression with full cross validation was used for the development of a calibration model based on selected wavelengths. Acquisition of reference variable has been done according to standardized procedures and it represents the total amount of lignin in the sample.

    The results of lignin characterization in feedstock material by NIR are very promising. The resulting PLS regressionmodel includes 2-factors and uses 16 predicting variables, resulting in R2 = 0,975, RMSE = 0,885 wt%. In the next step, presented work will be improved by applying large amount of samples, independent validation data set and by simulation of conveyor belt movements. The objective of this research is to test the NIR method at a real pulp digester, in order to improve monitoring andoptimization of the process. Furthermore, continuous characterization of the feedstock materials is intended to be used for the improvement of the control process. The measured lignin content will be compared to the content calculated within the pulp digester physical model and the Kappa number. This will be used for improving the digester physical model accuracy and as an input to advanced model based control, where the correlation will be made not only to lignin content but also with the feedstock material reactivity.

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  • 25.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Multivariate analysis models for wood properties combined with Open Modelica model for process performance monitoring2015In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2015, Vol. 48:1, p. 898-899Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    To perform advanced model based control it is important to know what is fed into a system such as a waste or biomass fired boiler or a pulp digester. In this paper, we present correlations between the lignin content of different types of wood chips and their Near-infrared (NIR) spectra. The Principal Component Regression (PCR) method is used for deriving the correlation, as well as selecting certain wave lengths. Analysis is made including different parts of the spectra in the wave length range 700 – 2500 nm. The model is then used as input to an Open Modelica pulp digester model to tune the reactivity constant of the dissolution of lignin. The lignin content of wood-chips is determined on-line through the NIR measurement at the feed to the digester. Simulations are carried out to determine the content of residual lignin on fibers at the exit (continuous digester) or at the end of a cook (batch digester). By comparing the deviation between predicted values and actual measured values the reactivity constant of the lignin is determined. The regression can be made to the NIR spectrum aside of the lignin content as such. The original content of lignin together with reactivity may then be used for optimized on-line control of the digester. It can also be used for diagnostic purposes with regard to process issues like hang-ups or channeling, as well as possible sensor faults and data reconciliation.

  • 26.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rapid Determination of Selected Compounds in Waste-based Fuel by Near Infrared Spectroscopy2015In: Book of abstracts, 2015Conference paper (Refereed)
    Abstract [en]

    Composition of the waste-based fuel intended for incineration has substantial effect on combustion process performance and formation of environmentally harmful emissions. Fuel composition vary significantly depending on the material source, waste sorting and recycling procedures and other waste pretreatment methods. In general, it typically contains paper, plastics, wood, textile, other organic material and further undesired substances including glass and metals. The knowledge of actual composition of the material fed into the boiler is limited to the direct or indirect continuous moisture content measurements and periodic fuel sampling providing elementary composition. This information is not sufficient for process control and performance optimization, particularly when considering strongly heterogeneous fuel feed. Therefore a rapid and reliable technique for fuel characterization is needed.The work presented here is focused to the quantitative determination of selected plastic materials and glass content. Incomplete combustion of different plastics may lead to the formation of carbon monoxide, hydrogen-cyanides, acid compounds and aromatic hydrocarbons etc. If the waste contains chlorine then highly chlorinated polycyclic compounds such as dioxins and furans may be formed. Plastics often contain flame retardants which can also contribute to production of harmful emissions. On the other hand, the highly corrosive deposits of alkali chlorides and other compounds may be formed on the heat exchangers, this lowers the heat transfer and boiler efficiency and decrease life-time of the equipment. Moreover, increased content of glass in the fuel supports the formation of agglomerates in the fuel bed, defluidization of the bed or ash removal problems which result in malfunction or failure of the combustion equipment.Near infrared (NIR) spectroscopy can be used for non-destructive quantitative determination of plastics and glass in waste-based fuel. Experimental work was performed on two types of spectrophotometers i.e. grating and Fourier transform instruments. Samples of known content of glass and different plastics were placed on a moving tray that reciprocated horizontally back and forth underneath the NIR sensor. This was done in order to replicate online application where the NIR spectrophotometer is places above the conveyor belt that transport the fuel to the boiler.Spectra were recorded in the range between 700 and 2500 nm. Acquired spectral data were pretreated with different methods such as normalization, scatter correction, smoothing, first and second derivative (Savitzky-Golay algorithm), selection of different spectral ranges and its combinations. Mathematical models to estimate content of glass and different plastics were constructed using Partial Least Square Regression (PLS-R) and Principle component regression (PCR) statistical methods. Different combinations of spectrophotometer type, pre-treatment methods and statistical methods were evaluated in order to find the model with the best prediction performance.Results prove the potential of the method to quantitatively determine the content of different types of plastics as well as glass with reasonable prediction accuracy. The ultimate goal of this research is to test the method at a real industrial boiler in order to improve process monitoring and control.

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  • 27.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Utilization of Near Infrared (NIR) Spectrometry for Detection of Glass in the Waste-based Fuel2015In: Energy Procedia, ISSN 1876-6102, Vol. 75, p. 734-741Article in journal (Refereed)
    Abstract [en]

    This paper presents the results of experimental measurements and multivariate statistical modeling concerning detection of soda-lime glass using near infrared (NIR) spectrometry technique. The purpose is to test if the glass is quantitatively detectable in a waste-based material and to assess what method of spectral data pretreatment is the most suitable in order to develop prediction models. The experiments were performed on six test samples containing a specific amount of glass distributed in background material. Pretreatment methods such as normalization and first and second derivatives were applied on the acquired absorbance spectral data. Principal component analysis (PCA) was employed in order to describe the relationship between pretreated data and the amount of glass in the test samples. Subsequently, principal component regression (PCR) was utilized for the development of prediction models. The results from the models show strong correlation between the pretreated data and the glass content. The most promising results were obtained from the model based on 1st derivative pretreatment when only absorbance spectral data from selected wavelengths are included. 

  • 28.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Sandberg, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Erik, Dahlquist
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Experimental investigation of part load operation of a full-scale biomass-fired fluidized bed boilerManuscript (preprint) (Other academic)
  • 29.
    Skvaril, Jan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review2017In: Applied spectroscopy reviews (Softcover ed.), ISSN 0570-4928, E-ISSN 1520-569X, Vol. 52, no 8, p. 675-728Article, review/survey (Refereed)
    Abstract [en]

    Biomass used in energy conversion processes is typically characterized by high variability, making its utilization challenging. Therefore, there is a need for a fast and non-destructive method to determine feedstock/product properties and directly monitor process reactors. The near-infrared spectroscopy (NIRS) technique together with advanced data analysis methods offers a possible solution. This review focuses on the introduction of the NIRS method and its recent applications to physical, thermochemical, biochemical and physiochemical biomass conversion processes represented mainly by pelleting, combustion, gasification, pyrolysis, as well as biogas, bioethanol, and biodiesel production. NIRS has been proven to be a reliable and inexpensive method with a great potential for use in process optimization, advanced control, or product quality assurance.

  • 30.
    Wang, Xiaolin
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhang, Shengmin
    Swedish University of Agricultural Sciences,Sweden.
    Li, Haichao
    Swedish University of Agricultural Sciences,Sweden.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Elevated CO2 effects on Zn and Fe nutrition in vegetables: A meta-analysis2024Conference paper (Refereed)
    Abstract [en]

    The atmospheric carbon dioxide (CO2) concentration has been progressively increasing since the onset of the Industrial Revolution and has already reached at around 420 μmol mol⁻¹ nowadays. It is well recognized that elevated CO2 concentration stimulates the yield for C3 crops, but it also simultaneously changes the essential nutrients. However, compared with the main crops, far less attention has been devoted to the effects of elevated CO2 concentration on vegetable growth and quality. Vegetables are highly recommended in daily diets due to their diverse range of beneficial compounds, such as vitamins, antioxidants, minerals, and dietary fiber.  In controlled greenhouse vegetable cultivation, elevated CO2 has been widely adopted as an agricultural practice for enhancing plant growth. Thus, understanding both vegetable growth and nutrient status is crucial to assess the potential impacts of elevated CO2 on future food security in both natural and controlled environments. However, much more attention has been paid to biomass enhancement, and elevated CO2 effects on nutrient quality are less recognized. Among the nutrients, Zinc (Zn) and Iron (Fe) are essential elements in humans. Previous studies have demonstrated a decreasing trend of Zn and Fe in main crops such as wheat and rice with increased CO2, while less is known about whether this alleviation effect on Zn and Fe can apply to vegetables. Therefore, a meta-analysis was conducted in this study to evaluate the influence of elevated CO2 concentration in the atmosphere on vegetable Fe and Zn status, and quantify the potential impact of future climate on nutrition security.

  • 31.
    Winn, Olivia
    et al.
    Mälardalen University.
    Sivaram, Kiran Thekkemadathil
    Mälardalen University.
    Aslanidou, Ioanna
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Near-infrared spectral measurements and multivariate analysis for predicting glass contamination of refuse-derived fuel2017In: Energy Procedia, ISSN 1876-6102, Vol. 142, p. 943-949Article in journal (Refereed)
    Abstract [en]

    This paper investigates how glass contamination in refuse-derived fuel can be quantitatively detected using near-infrared spectroscopy. Near-infrared spectral data of glass in four different background materials were collected, each material chosen to represent a main component in municipal solid waste; actual refuse-derived fuel was not tested. The resulting spectra were pre- processed and used to develop multi-variate predictive models using partial least squares regression. It was shown that predictive models for coloured glass content are reasonably accurate, while models for mixed glass or clear glass content are not; the validated model for coloured glass content had a coefficient of determination of 0.83 between the predicted and reference data, and a root- mean-square error of validation of 0.64. The methods investigated in this paper show potential in predicting coloured glass content in different types of background material, but a different approach would be needed for predicting mixed type glass contamination in refuse-derived fuel. 

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