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Moretti, A., Ivan, H. L. & Skvaril, J. (2024). A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible?. Journal of Water Process Engineering, 60, Article ID 105061.
Åpne denne publikasjonen i ny fane eller vindu >>A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible?
2024 (engelsk)Inngår i: Journal of Water Process Engineering, E-ISSN 2214-7144, Vol. 60, artikkel-id 105061Artikkel i tidsskrift (Fagfellevurdert) Published
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).

sted, utgiver, år, opplag, sider
Elsevier Ltd, 2024
Emneord
advanced sensors, real-time controls, wastewater characterization, wastewater treatment process
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66278 (URN)10.1016/j.jwpe.2024.105061 (DOI)001218917700001 ()2-s2.0-85187565972 (Scopus ID)
Tilgjengelig fra: 2024-03-20 Laget: 2024-03-20 Sist oppdatert: 2024-05-29bibliografisk kontrollert
Gorji, R., Skvaril, J. & Odlare, M. (2024). Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy. Horticulturae, 10(4), 336-336
Åpne denne publikasjonen i ny fane eller vindu >>Determining Moisture Content of Basil Using Handheld Near-Infrared Spectroscopy
2024 (engelsk)Inngår i: Horticulturae, E-ISSN 2311-7524, Vol. 10, nr 4, s. 336-336Artikkel i tidsskrift (Fagfellevurdert) Published
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.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66364 (URN)10.3390/horticulturae10040336 (DOI)001210006600001 ()2-s2.0-85191572101 (Scopus ID)
Tilgjengelig fra: 2024-04-04 Laget: 2024-04-04 Sist oppdatert: 2024-05-08bibliografisk kontrollert
Wang, X., Zhang, S., Li, H., Odlare, M. & Skvaril, J. (2024). Elevated CO2 effects on Zn and Fe nutrition in vegetables: A meta-analysis. In: : . Paper presented at EGU24, Vienna, Austria & Online, 14–19 April 2024.
Åpne denne publikasjonen i ny fane eller vindu >>Elevated CO2 effects on Zn and Fe nutrition in vegetables: A meta-analysis
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2024 (engelsk)Konferansepaper, Oral presentation with published abstract (Fagfellevurdert)
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.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66385 (URN)10.5194/egusphere-egu24-18543 (DOI)
Konferanse
EGU24, Vienna, Austria & Online, 14–19 April 2024
Tilgjengelig fra: 2024-04-08 Laget: 2024-04-08 Sist oppdatert: 2024-04-08bibliografisk kontrollert
Pan, S., Shi, X., Dong, B., Skvaril, J., Zhang, H., Liang, Y. & Li, H. (2024). Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants. Fuel, 359, Article ID 130344.
Åpne denne publikasjonen i ny fane eller vindu >>Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants
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2024 (engelsk)Inngår i: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 359, artikkel-id 130344Artikkel i tidsskrift (Fagfellevurdert) Published
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.

Emneord
Biomass fired combined heat and power plants, CO2 capture, Flue gas flowrate, CO2 concentration, Transformer model, Deep learning
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-64937 (URN)10.1016/j.fuel.2023.130344 (DOI)001125955300001 ()2-s2.0-85178313710 (Scopus ID)
Prosjekter
AI-BECCS
Forskningsfinansiär
Swedish Energy AgencySwedish Energy Agency
Tilgjengelig fra: 2023-12-05 Laget: 2023-12-05 Sist oppdatert: 2024-01-24bibliografisk kontrollert
Paris, B., Michas, D., Balafoutis, A. T., Nibbi, L., Skvaril, J., Li, H., . . . Apostolopoulos, N. (2023). A Review of the Current Practices of Bioeconomy Education and Training in the EU. Sustainability, 15(2), Article ID 954.
Åpne denne publikasjonen i ny fane eller vindu >>A Review of the Current Practices of Bioeconomy Education and Training in the EU
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2023 (engelsk)Inngår i: Sustainability, E-ISSN 2071-1050, Vol. 15, nr 2, artikkel-id 954Artikkel, forskningsoversikt (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
MDPI, 2023
Emneord
bioeconomy, bioeconomy education, bioeconomy learning, higher education, vocational education and training
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-61918 (URN)10.3390/su15020954 (DOI)000918839000001 ()
Tilgjengelig fra: 2023-02-15 Laget: 2023-02-15 Sist oppdatert: 2023-02-15bibliografisk kontrollert
Dong, B., Hu, C., Skvaril, J., Thorin, E. & Li, H. (2023). Selecting the approach for dynamic modelling of CO2 capture in biomass/waste fired CHP plants. International Journal of Greenhouse Gas Control, 130, Article ID 104008.
Åpne denne publikasjonen i ny fane eller vindu >>Selecting the approach for dynamic modelling of CO2 capture in biomass/waste fired CHP plants
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2023 (engelsk)Inngår i: International Journal of Greenhouse Gas Control, ISSN 1750-5836, E-ISSN 1878-0148, Vol. 130, artikkel-id 104008Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
Elsevier Ltd, 2023
Emneord
Bioenergy with carbon capture and storage (BECCS), Biomass/waste fired combined heat and power plants, Dynamic modelling approach, Dynamic performance, MEA based chemical absorption, Biomass, Carbon capture, Coal fired power plant, Cogeneration plants, Ethanolamines, Fossil fuel power plants, Gas plants, More electric aircraft, Bioenergies with carbon capture and storages, Bioenergy with carbon capture and storage, Biomass wastes, Biomass/waste fired combined heat and power plant, Chemical absorption, Dynamic modeling approach, Dynamics models, Static modelling, Carbon dioxide
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-64753 (URN)10.1016/j.ijggc.2023.104008 (DOI)001112149200001 ()2-s2.0-85175621556 (Scopus ID)
Tilgjengelig fra: 2023-11-15 Laget: 2023-11-15 Sist oppdatert: 2024-03-08bibliografisk kontrollert
Dahlquist, E., Rahman, M., Skvaril, J. & Kyprianidis, K. (2021). AI Overview: Methods and Structures (1ed.). In: Konstantinos Kyprianidis and Erik Dahlquist (Ed.), AI and Learning Systems - Industrial Applications and Future Directions: . IntechIntechOpen
Åpne denne publikasjonen i ny fane eller vindu >>AI Overview: Methods and Structures
2021 (engelsk)Inngår i: AI and Learning Systems - Industrial Applications and Future Directions / [ed] Konstantinos Kyprianidis and Erik Dahlquist, IntechIntechOpen , 2021, 1Kapittel i bok, del av antologi (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IntechIntechOpen, 2021 Opplag: 1
Emneord
process industry, artificial intelligence (AI), learning system, soft sensors, machine learning
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-53499 (URN)10.5772/intechopen.90741 (DOI)978-1-78985-877-8 (ISBN)978-1-83968-601-6 (ISBN)
Forskningsfinansiär
EU, Horizon 2020, 723523
Tilgjengelig fra: 2021-02-19 Laget: 2021-02-19 Sist oppdatert: 2021-03-12bibliografisk kontrollert
Hawas, A. & Skvaril, J. (2021). Building Thermography Inspection by using a Low-Cost Visual-Thermal Drone System. In: : . Paper presented at 27th Annual Conference, International Sustainable Development Research Society (pp. 350-351). Östersund, Article ID 405.
Åpne denne publikasjonen i ny fane eller vindu >>Building Thermography Inspection by using a Low-Cost Visual-Thermal Drone System
2021 (engelsk)Konferansepaper, Oral presentation with published abstract (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Östersund: , 2021
Emneord
Building Thermography, Visual-Thermal Drone, Low-Cost Drone, Thermal imaging, Sustainability
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-58088 (URN)
Konferanse
27th Annual Conference, International Sustainable Development Research Society
Tilgjengelig fra: 2022-04-24 Laget: 2022-04-24 Sist oppdatert: 2022-04-29bibliografisk kontrollert
Ahmed, M. U., Aslanidou, I., Axelsson, J., Begum, S., Hatvani, L., Olsson, A., . . . Zaccaria, V. (2021). Dilemmas in designing e-learning experiences for professionals. In: Proceedings of the European Conference on e-Learning, ECEL: . Paper presented at 20th European Conference on e-Learning ECEL, 28 Oct 2021, Berlin, Germany (pp. 10-17).
Åpne denne publikasjonen i ny fane eller vindu >>Dilemmas in designing e-learning experiences for professionals
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2021 (engelsk)Inngår i: Proceedings of the European Conference on e-Learning, ECEL, 2021, s. 10-17Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

Emneord
lifelong learning, higher education, e-learning, online learning, industrial co-production
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-55701 (URN)10.34190/EEL.21.049 (DOI)000755489500002 ()2-s2.0-85121577831 (Scopus ID)
Konferanse
20th European Conference on e-Learning ECEL, 28 Oct 2021, Berlin, Germany
Prosjekter
FuturE
Tilgjengelig fra: 2021-08-30 Laget: 2021-08-30 Sist oppdatert: 2022-11-08bibliografisk kontrollert
Kyprianidis, K., Karlsson, M., Tryzell, R., Skvaril, J., Soibam, J., Ševcik, M., . . . Dahlquist, E. (2021). On-line Powerplant Control using Near-InfraRed Spectroscopy: OPtiC-NIRS, REPORT 2021:746. Energiforsk AB
Åpne denne publikasjonen i ny fane eller vindu >>On-line Powerplant Control using Near-InfraRed Spectroscopy: OPtiC-NIRS, REPORT 2021:746
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2021 (engelsk)Rapport (Annet vitenskapelig)
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. 

sted, utgiver, år, opplag, sider
Energiforsk AB: , 2021
Emneord
NIRS; Biomass; Waste; Combustion; Soft-sensors; Control.
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-54528 (URN)978-91-7673-746-0 (ISBN)
Merknad

These are the results and conclusions of a project, which is part of a research programme run by Energiforsk. The author/authors are responsible for the content.

Tilgjengelig fra: 2021-06-07 Laget: 2021-06-07 Sist oppdatert: 2022-11-25bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-5341-3656