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Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation

Sahar Safarian (), Seyed Mohammad Ebrahimi Saryazdi (), Runar Unnthorsson and Christiaan Richter
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Sahar Safarian: University of Iceland
Seyed Mohammad Ebrahimi Saryazdi: Sharif University of Technologies
Runar Unnthorsson: University of Iceland
Christiaan Richter: University of Iceland

Biophysical Economics and Resource Quality, 2021, vol. 6, issue 1, 1-13

Abstract: Abstract This paper explores the construction and validation of an artificial neural network (ANN) model to accurately and efficiently predict the performance of a downdraft biomass gasification integrated with syngas fermentation plant for ethanol production. The study aims to predict the specific mass flow rate of bioethanol product from the systems derived by various kinds of biomass feedstocks under atmospheric pressure and various operating conditions. The input parameters used in the models are elemental analysis compositions (C, O, H, N and S), proximate analysis compositions (moisture, ash, volatile material and fixed carbon) and operating parameters (gasifier temperature and air to fuel ratio). The architecture of the model consisted of one input, one hidden and one output layer. 1008 simulated data from 84 different types of biomasses in various operating conditions were used to train the ANN. The developed ANN shows agreement with simulated data with Root Mean Square Error (RMSE) less than 0.05 in the case of product bioethanol. Moreover, the relative influence of biomass characteristics and some specific operating parameters on output are determined. Finally, to have a more detailed assessment, the variations of all input variables with respect to carbon content are compared and analyzed together. The suggested integrated ANN based model can be applied as a very useful tool for optimization and control of the process through the downdraft biomass gasification integrated with bioethanol production unit.

Keywords: Biomass gasification; Artificial neural network; Bioethanol production; Downdraft; Simulation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s41247-020-00083-2

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