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Tomas Aparicio, ElenaORCID iD iconorcid.org/0000-0002-9508-1733
Publications (4 of 4) Show all publications
Ahmed, M. U., Andersson, P., Andersson, T., Tomas Aparicio, E., Baaz, H., Barua, S., . . . Zambrano, J. (2019). Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach. In: Elsevier (Ed.), “Innovative Solutions for Energy Transitions”: . Paper presented at International Conference on Applied Energy, 2018 (pp. 1279-1287). , 158
Open this publication in new window or tab >>Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach
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2019 (English)In: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2019, Vol. 158, p. 1279-1287Conference paper, Published paper (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 (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.

Series
Energy Procedia, ISSN 1876-6102
Keywords
Artificial Neural Netwrok; Chemometrics; Gaussian Process Regression; Multiplicative Scatter Correction; Standard Normal Variate; Support Vector Regression; Partial Least Squares; Savitzky-Golay derivatives
National Category
Environmental Engineering Environmental Biotechnology Industrial Biotechnology
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-40396 (URN)2-s2.0-85063865772 (Scopus ID)
Conference
International Conference on Applied Energy, 2018
Projects
FUDIPO
Funder
EU, Horizon 2020, 723523
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-04-25Bibliographically approved
Starfelt, F., Tomas Aparicio, E., Li, H. & Dotzauer, E. (2015). Integration of torrefaction in CHP plants - A case study. Energy Conversion and Management, 90, 427-435
Open this publication in new window or tab >>Integration of torrefaction in CHP plants - A case study
2015 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 90, p. 427-435Article in journal (Refereed) Published
Abstract [en]

Torrefied biomass shows characteristics that resemble those of coal. Therefore, torrefied biomass can be co-combusted with coal in existing coal mills and burners. This paper presents simulation results of a case study where a torrefaction reactor was integrated in an existing combined heat and power plant and sized to replace 25%, 50%, 75% or 100% of the fossil coal in one of the boilers. The simulations show that a torrefaction reactor can be integrated with existing plants without compromising heat or electricity production. Economic and sensitivity analysis show that the additional cost for integrating a torrefaction reactor is low which means that with an emission allowance cost of 37 €/ton CO2, the proposed integrated system can be profitable and use 100% renewable fuels. The development of subsidies will affect the process economy. The determinant parameters are electricity and fuel prices.

Keywords
Biomass, Combined heat and power (CHP), District heating, Polygeneration, Torrefaction, Carbon dioxide, Coal, Cost benefit analysis, Costs, Sensitivity analysis, Additional costs, Combined heat and power, Combined heat and power plants, Electricity production, Emission allowances, Integrated systems, Poly-generation, Cogeneration plants
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-26943 (URN)10.1016/j.enconman.2014.11.019 (DOI)000348886800040 ()2-s2.0-84915745084 (Scopus ID)
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2017-12-05Bibliographically approved
Tomas Aparicio, E., Li, H., Starfelt, F. & Dahlquist, E. (2012). Dynamic Simulation of Torrefaction. In: : . Paper presented at International Conference on Applied Energy.
Open this publication in new window or tab >>Dynamic Simulation of Torrefaction
2012 (English)Conference paper, Published paper (Refereed)
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-16536 (URN)
Conference
International Conference on Applied Energy
Available from: 2012-12-11 Created: 2012-12-11 Last updated: 2018-02-27Bibliographically approved
Starfelt, F., Tomas Aparicio, E., Thorin, E. & Ericson, V. (2012). Simultaneous dynamic and quasi-steady state simulations to optimize combined heat and power plant operation. In: : . Paper presented at International Conference on Applied Energy, July 5-8, 2012, Suzhou, China.
Open this publication in new window or tab >>Simultaneous dynamic and quasi-steady state simulations to optimize combined heat and power plant operation
2012 (English)Conference paper, Published paper (Refereed)
National Category
Engineering and Technology Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-24098 (URN)
Conference
International Conference on Applied Energy, July 5-8, 2012, Suzhou, China
Available from: 2014-01-02 Created: 2014-01-02 Last updated: 2018-01-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9508-1733

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