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

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.

Bioethanol Production via Herbaceous and Agricultural Biomass Gasification Integrated with Syngas Fermentation

“…In this paper, 20 biomass feedstocks from the H&AB group were used as input to be fed to the gasifier. The results of proximate and elemental analyses of the considered biomasses are summarized in Table 1 [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

“…Of the reviewed studies that applied data-driven methods to model RRCC technologies, 20% (36% statistical and 64% ML methods) represented models that predicted syngas yield through the gasification of various feedstocks (Figure a and Table S9). Gasification was most frequently modeled by applying MPR ,− using primary data and ANN ,− using both primary and secondary data. These MPR and ANN models respectively, comprised 33% and 36% of the data-driven gasification models (Figure a and Table S9).…”

A Review on Recent Progress in the Integrated Green Hydrogen Production Processes

“…Biomass gasification is another mature technology for hydrogen generation by converting biomass or fossil-based carbonaceous materials to syngas without combustion and under a controlled process. Compared with fossil fuels, biomass is less expensive and more widely available, reducing greenhouse gas emissions and high energy efficiency [65,66]. Biomass gasification involves a thermochemical process at high temperatures (~900 • C) and low pressure.…”