Utilization of Machine Learning to Predict Bio-Oil and Biochar Yields from CoPyrolysis of Biomass with Waste Polymers

Abstract

With 220 billion dry tons available, biomass is one of the world’s most abundant energy source; it also could be a reliable energy source. The human population annual rate of production is 275 million tons of plastic waste as of the year 2019, which has to be managed to facilitate circular carbon economy. Pyrolysis of biomass has emerged as an attractive option for converting waste into bioenergy. Because of its high oxygen content, acidity and viscosity, pyrolysis bio-oil is generally a low-quality product that requires upgrading before being used directly as a drop-in fuel and a fuel additive; this upgrade is achieved by co-pyrolysis of biomass with waste polymers. Since polymers are a rich source of hydrogen, pyrolysis vapors are upgrade; the advantage of co-pyrolysis is that a separate hydroprocessing unit becomes unnecessary after process optimization.

Machine learning is emerging as a growing field to predict and optimize the energy related processes. The process can be finetuned using the models trained on the existing experimental data. In this research, machine learning models were developed to predict product yields from the co-pyrolysis of biomass and polymers. Data from the literature on co-pyrolysis of lignocellulosic biomass and polymer co-pyrolysis provided a tool to predict these outcomes. Machine learning algorithms were examined and trained with datasets acquired for biochar and bio-oil yields, with cross-validation and hyperparameters to fit the ultimate and proximate analysis of the reactants and physical conditions of the reactions. XGBoost predicted a biochar yield with RMSE of 1.77 and R$^2$ of 0.96, and a dense neural network predicted a bio-oil yield with RMSE 2.6 and R$^2$ of 0.96. Proximate analysis features were a necessary addition to the bio-oil model. SHAP (SHapley Additive exPlanations) analysis for the DNN liquid model found biomass fixed carbon, biomass moisture and biomass volatile matter with 0.11, 0.09, and 0.06 mean absolute SHAP values, respectively. The machine learning models provided a convenient and predictive tool for co-pyrolysis reaction within the range of the model’s errors and training features. These models also offered insight into the development of municipal solid waste pyrolysis in a circular carbon economy.