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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.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE SICS, 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.
    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.
    Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach2019In: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2019, Vol. 158, p. 1279-1287Conference 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.

  • 2.
    Starfelt, Fredrik
    et al.
    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.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dotzauer, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Integration of torrefaction in CHP plants - A case study2015In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 90, p. 427-435Article in journal (Refereed)
    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.

  • 3.
    Starfelt, Fredrik
    et al.
    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.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ericson, V.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Simultaneous dynamic and quasi-steady state simulations to optimize combined heat and power plant operation2012Conference paper (Refereed)
  • 4.
    Tomas Aparicio, Elena
    et al.
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Li, Hailong
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Starfelt, Fredrik
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Dahlquist, Erik
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Dynamic Simulation of Torrefaction2012Conference paper (Refereed)
1 - 4 of 4
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