Data analysis and machine learning aided integrated catalyst activity and process modelling for selective H2 production from biomass gasification

Abstract

Hydrogen energy derived through biomass gasification is considered as one of the most sorted sustainable sources of renewable energy. This process enhances the H2 production from biomass in the presence of specific catalysts. Among different kinds of models that have been employed for this process, ML models adept at approximating non-linear functions and facilitate outcome prediction without detailed mathematical descriptions. Thus, the current work focuses on understanding structural-composition-operating-target property relationships, and integrated catalyst and process modelling using ML framework for thermo-catalytic biomass gasification to H2 production, and demonstrates outliers handling, data normalization for efficient handling of data-driven modelling with non-linear database. Linear, tree-based, kernel-based, and ANN models were developed with 589 datapoints screened from the 59 relevant papers with 24 inputs and 4 outputs (H2, CO, CO2, and CH4 as vol. %). Performance of these models are evaluated through 5-fold cross-validation and test data with the help of statistical measures. ANN with Basian-regularization learning algorithm using tan-sigmoid activation function in both layers, resulted superior performance in prediction of H2 production (RMSE = 6.85 & R2 = 0.80) and other output gases with high accuracy (i.e., minimum deviation from experimental data) compared to other ML models. Further, using the best ML model, input contribution and PDP analysis were performed to interpret the significance of predominate input parameters affecting on the product composition. Feature contribution analysis reveals that temperature, S/B ratio, catalyst support type, and sulphur content in biomass are significant parameters for enhancing H2 production from catalytic-biomass gasification, and PDP analysis discloses their optimal operating region.