Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

Sahar Safarian,Runar Unnthorsson,Christiaan Richter,Seyed Mohammad Ebrahimi Saryazdi

doi:10.3390/fermentation7020071

Sahar Safarian, Runar Unnthorsson + Show 2 more

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https://doi.org/10.3390/fermentation7020071

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Abstract

In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.

Highlights

To overcome challenges regarding increasing carbon concentration and climate change, renewable energies like biomass, solar radiations and wind have been encouraged to be used because they do not emit greenhouse gases such as carbon dioxide, which plays an important function in global warming [1,2,3,4]
The artificial neural network (ANN) models were primarily based on weights and biases that are the learnable parameters of a machine learning model
Only a few studies have reported about biomass gasification modeling according to an ANN-based approach and there is nothing on gasification integrated with a water–gas shift unit and separation unit for hydrogen production

Summary

Introduction

To overcome challenges regarding increasing carbon concentration and climate change, renewable energies like biomass, solar radiations and wind have been encouraged to be used because they do not emit greenhouse gases such as carbon dioxide, which plays an important function in global warming [1,2,3,4]. The procedures relying on thermochemical conversion can be classified as liquefaction, pyrolysis, combustion and gasification [12,13] and, amongst them, biomass gasification has been proved as a promising green technology toward conversion of different feedstocks to various energy products [8,9,10]. Through this complex system, lignocellulosic materials are transformed to a more valuable gas, investigated as syngas by series reactions at high temperatures [14,15,16]

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