| 1. |
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| 2. |
- Skvaril, Jan, 1982-, et al.
(författare)
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The experimental study of full-scale biomass-fired bubbling fluidized bed boiler
- 2014
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Ingår i: Energy Procedia. - : Elsevier BV. - 1876-6102. ; 61, s. 643-647
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 3. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A machine learning approach for biomass characterization
- 2019
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
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Konferensbidrag (refereegranskat)abstract
- 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 (SG1) 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. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
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| 4. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Dilemmas in designing e-learning experiences for professionals
- 2021
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Ingår i: Proceedings of the European Conference on e-Learning, ECEL. ; , s. 10-17
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Avelin, Anders, et al.
(författare)
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Forest biomass for bioenergy production – comparison of different forest species
- 2014
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Ingår i: Energy Procedia. - : Elsevier BV. - 1876-6102.
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Konferensbidrag (refereegranskat)abstract
- 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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| 6. |
- Dahlquist, Erik, 1951-, et al.
(författare)
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AI Overview : Methods and Structures
- 2021. - 1
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Ingår i: AI and Learning Systems - Industrial Applications and Future Directions. - : IntechIntechOpen. - 9781789858778 - 9781839686016
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Bokkapitel (refereegranskat)abstract
- 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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| 7. |
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Developments in Combustion Technology
- 2016
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- 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
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| 8. |
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Developments in Near-Infrared Spectroscopy
- 2017
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 9. |
- Dong, Beibei
(författare)
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Dynamic modeling of MEA-based CO2 capture in biomass-fired CHP plants
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Global warming is a significant threat to our planet. Adopting the Paris Agreement is a global action that aims to reduce greenhouse gas emissions. An extensive deployment of negative emission technologies (NETs) is required to achieve the targets set by the Paris Agreement. Bioenergy with carbon capture and storage (BECCS) is emerging as one of the most promising NETs. Among different biomass utilization processes, integrating BECCS with biomass-fired and waste-fired combined heat and power (bio-CHP and w-CHP) plants has been considered the most feasible solution. Bio/w-CHP plants are characterized by high fluctuations in operation, which can result in more dynamic variations of flue gas (FG) flowrates and compositions and available heat for CO2 capture. Such changes can clearly affect the performance of CO2 capture; therefore, doing dynamic simulations becomes crucial.This thesis aims to investigate the performance of different dynamic physical model-based approaches and provide suggestions for approach selection. In addition, the data-driven modeling approach, which is an emerging technology, has also been tested.Three physical model-based approaches include the ideal static model (IST), the dynamic approach without control (Dw/oC), and the dynamic approach with control (DwC). To compare their performance, the operating data from an actual waste CHP plant is employed. Various cases have been defined considering different critical operating parameters, including the FG flowrate, the CO2 concentration (CO2vol%), and the available heat for CO2 capture. Apparent differences can be observed in the results from different approaches. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, the difference in the captured CO2 can be up to 22% between DwC and Dw/oC. It is worth noting that when there are both increases and decreases in the variations of parameters, the differences become smaller. Based on the comparison, the recommendations on approaches have been summarized. Dw/oC is recommended for checking the boundary of safety operation by the response analysis. DwC is recommended for designing the control system, observing the flexible dynamic operation, estimating the short-term CO2 capture potential, and optimizing the hourly dynamic operation. IST is recommended for estimating the long-term CO2 capture potential, and optimizing the long-term dynamic operation when the input parameters vary not as often as hourly.A data-driven model, Informer, is developed to model CO2 capture dynamically. The dataset is generated by using a physical model. The FG flowrate, the CO2vol%, the lean solvent flowrate, and the available heat for CO2 capture are employed as input parameters, and the CO2 capture rate and the energy penalty are chosen as outputs. The results show that Informer can accurately predict dynamic CO2 capture. The mean absolute percentage error (MAPE) was found to be 6.2% and 2.7% for predicting the CO2 capture rate and energy penalty, respectively.
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| 10. |
- Dong, Beibei, et al.
(författare)
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Selecting the approach for dynamic modelling of CO2 capture in biomass/waste fired CHP plants
- 2023
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Ingår i: International Journal of Greenhouse Gas Control. - : Elsevier Ltd. - 1750-5836 .- 1878-0148. ; 130
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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