Improved prediction of biomass gasification models through machine learning

Gasification of lignocellulosic biomass can be used to produce syngas used as a biorefinery feedstock. To facilitate the commercialisation of the gasification process, models are used to predict the outputs, simulate the impacts of irregular circumstances, and analyse process feasibility. This paper presents a hybrid model combining Aspen Plus and machine learning (ML) algorithms to enhance the prediction of gasification outputs. A base case gasification process flowsheet simulation was implemented in Aspen Plus based on assumed thermodynamic equilibrium conditions which can lead to inaccurate results. To address this, six ML algorithms were applied to collected experimental data and analysed for accuracy and efficiency. The feature importance, accuracy improvement, and the effect of implementing the ML predictions in the gasification block on the rest of the flowsheet were investigated. This paper emphasises the need of higher accuracy models and the great potential of ML approaches to offer high accurate predictions.