Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

Abstract

An equilibrium-based steady-state simulator model that predicts and optimizes hydrogen production from steam gasification of biomass is developed using ASPEN Plus software and artificial intelligence techniques. Corn cob’s chemical composition was characterized to ensure the biomass used as a gasifier and with potential for production of hydrogen. Artificial intelligence is used to examine the effects of the significant input variables on response variables, such as hydrogen mole fraction and hydrogen energy content. Optimizing the steam-gasification process using response surface methodology (RSM) considering a variety of biomass-steam ratios was carried out to achieve the best results. Hydrogen yield and the impact of main operating parameters were considered. A maximum hydrogen concentration is found in the gasifier and water-gas shift (WGS) reactor at the highest steam-to-biomass (S/B) ratio and the lowest WGS reaction temperature, while the gasification temperature has an optimum value. ANFIS was used to predict hydrogen of mole fraction, 0.5045 with the input parameters of S/B ratio of 2.449 and reactor pressure and temperature of 1 bar and 848°C, respectively. With the steam-gasification model operating at temperature (850°C), pressure (1 bar), and S/B ratio of 2.0, an ASPEN simulator achieved a maximum of 0.5862 mole fraction of hydrogen, while RSM gave an increase of 19.0% optimum hydrogen produced over the ANFIS prediction with the input parameters of S/B ratio of 1.053 and reactor pressure and temperature of 1 bar and 850°C, respectively. Varying the gasifier temperature and S/B ratio have, on the other hand, a crucial effect on the gasification process with artificial intelligence as a unique tool for process evaluation, prediction, and optimization to increase a significant impact on the products especially hydrogen.