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- Dahlquist, Erik, 1951-, et al.
(author)
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Comparison of Gas Quality from Black Liquor and Wood Pellet Gasification Using Modelica Simulation and Pilot Plant Results
- 2017
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In: 8th International Conference on Applied Energy, ICAE 2016; Beijing; China; 8 October 2016 through 11 October 2016. - Amsterdam : Elsevier. ; 105, s. 992-998, s. 992-998
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Conference paper (peer-reviewed)abstract
- There is a potential to integrate biomass gasification with pulp & paper and CHP plants in order to complement the existing systems with production of chemicals, such as methane, hydrogen, and methanol etc. To perform system analysis of such integration, it is important to gain knowledge of relevant input data on expected synthesis gas composition by gasifying different types of feed stock. In this paper, the synthesis gas quality from wood pellets gasification (WPG) has been compared with black liquor gasification (BLG) through modeling and experimental results at pilot scale. In addition, the study develops regression models like Partial Least Squares (PLS) made from the experimental data. The regression models are then combined with dynamic models developed in Modelica for the investigation of dynamic energy and material balances for integrated plants. The data presented in this study could be used as input to relevant analysis using e.g. ASPEN plus and similar system analysis tools.
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| 2. |
- Dahlquist, Erik, 1951-, et al.
(author)
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Experimental and numerical investigation of pellet and black liquor gasification for polygeneration plant
- 2017
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In: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 204, s. 1055-1064
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Journal article (peer-reviewed)abstract
- It is vital to perform system analysis on integrated biomass gasification in chemical recovery systems in pulp and paper and heat and power plants for polygeneration applications. The proposed integration complements existing pulp and paper and heat and power production systems with production of chemicals such as methane and hydrogen. The potential to introduce gasification-based combined cycles comprising gas turbines and steam turbines to utilize black liquors and wood pellets also merits investigation. To perform such analysis, it is important to first build knowledge on expected synthesis gas composition by gasifying at smaller scale different types of feed stock. In the present paper, the synthesis gas quality from wood pellets gasification has been compared with black liquor gasification by means of numerical simulation as well as through pilot-scale experimental investigations. The experimental results have been correlated into partial least squares models to predict the composition of the synthesis gas produced under different operating conditions. The gas quality prediction models are combined with physical models using a generic open-source modelling language for investigating the dynamic performance of large-scale integrated polygeneration plants. The analysis is further complemented by considering potential gas separation using modern membrane technology for upgrading the synthesis gas with respect to hydrogen content. The experimental data and statistical models presented in this study form an important literature source for future use by the gasification and polygeneration research community on further integrated system analysis.
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| 3. |
- Dahlquist, Erik, et al.
(author)
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Modeling of Black Liquor Gasification
- 2016
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In: Proceedings of 2016 9th EUROSIM Congress on Modelling and Simulation. - : Linköping University Electronic Press. - 9789176853993
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Conference paper (peer-reviewed)abstract
- The energy situation in both process industries andpower plants is changing. It is becoming interesting toperform system analysis on how to integrate gasificationinto chemical recovery systems in the pulp & paperindustry and into the CHP systems in power plantapplications to complement with production ofchemicals aside of heat and power. The potentialchemicals are methane, hydrogen, and methanol. It isalso interesting to estimate the potential to introducecombined cycles with gas turbines and steam turbinesusing both black liquors and other type of biomass likepellets, wood chips etc. To perform such type ofanalysis, it is vital to have relevant input data on whatgas composition we can expect from running differenttypes of feedstock. In this paper, we focus on blackliquors as feedstock for integrated gasification systems.The experimental results are correlated into partial leastsquares models to predict major composition of thesynthesis gas produced under different conditions.These quality prediction models are then combined withphysical models using Modelica for the investigation ofdynamic energy and material balances for completeplants. The data can also be used as input to analysisusing e.g. ASPEN plus and similar system analysistools
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| 4. |
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| 5. |
- Dahlquist, Erik, et al.
(author)
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Modeling of Wood Gasification in an Atmospheric CFB Plant
- 2016
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In: Proceedings of 2016 9th EUROSIM Congress on Modelling and Simulation. - : Linköping University Electronic Press. - 9789176853993 ; , s. 872-877
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Conference paper (peer-reviewed)abstract
- The energy situation in both process industries andpower plants is changing and it is of interest toinvestigate new polygeneration solutions combiningproduction of chemicals with the production of powerand heat. Examples of such chemicals are methane,hydrogen, and methanol etc. Integration of gasificationinto chemical recovery systems in the pulp and paperproduction systems and into the combined heat andpower (CHP) systems in power plant applications areamong the possible polygeneration systems. It is alsointeresting to look at the potential to introduce combinedcycles with gas turbines and steam turbines as acomplement. To perform such analysis, it is importantto have relevant input data on what gas composition wecan expect from running different type of feed stock. Inthis paper, we focus on the wood pellets. Experimentalresults are correlated into partial least squares models topredict major composition of the synthesis gas producedunder different operating conditions. The qualityprediction models then are combined with physicalmodels using Modelica for investigation of dynamicenergy and material balances for large plants. The datacan also be used as input to analysis using e.g. ASPENplus and similar system analysis tools.
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| 6. |
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| 7. |
- Martinsen, Madeleine, et al.
(author)
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Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future
- 2023
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In: Applied Energy. - : ELSEVIER SCI LTD. - 0306-2619 .- 1872-9118. ; 339
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Journal article (peer-reviewed)abstract
- Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s con-fidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO2 emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO2 per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3-5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.
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