Biomass Gasification for Waste-to-Energy Conversion: Artificial Intelligence for Generalizable Modeling and Multi-Objective Optimization of Syngas Production

Gema Báez-Barrón, Francisco Javier Lopéz-Flores (), Eusiel Rubio-Castro and José María Ponce-Ortega ()
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Gema Báez-Barrón: Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica S/N, Ciudad Universitaria, Edificio V1, Morelia 58060, Mexico
Francisco Javier Lopéz-Flores: Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica S/N, Ciudad Universitaria, Edificio V1, Morelia 58060, Mexico
Eusiel Rubio-Castro: Chemical and Biological Sciences Department, Universidad Autónoma de Sinaloa, Culiacán 80000, Mexico
José María Ponce-Ortega: Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Av. Francisco J. Múgica S/N, Ciudad Universitaria, Edificio V1, Morelia 58060, Mexico

Resources, 2025, vol. 14, issue 10, 1-26

Abstract: Biomass gasification, a key waste-to-energy technology, is a complex thermochemical process with many input variables influencing the yield and quality of syngas. In this study, data-driven machine learning models are developed to capture the nonlinear relationships between feedstock properties, operating conditions, and syngas composition, in order to optimize process performance. Random Forest (RF), CatBoost (Categorical Boosting), and an Artificial Neural Network (ANN) were trained to predict key syngas outputs (syngas composition and syngas yield) from process inputs. The best-performing model (ANN) was then integrated into a multi-objective optimization framework using the open-source Optimization & Machine Learning Toolkit (OMLT) in Pyomo. An optimization problem was formulated with two objectives—maximizing the hydrogen-to-carbon monoxide (H 2 /CO) ratio and maximizing the syngas yield simultaneously, subject to operational constraints. The trade-off between these competing objectives was resolved by generating a Pareto frontier, which identifies optimal operating points for different priority weightings of syngas quality vs. quantity. To interpret the ML models and validate domain knowledge, SHapley Additive exPlanations (SHAP) were applied, revealing that parameters such as equivalence ratio, steam-to-biomass ratio, feedstock lower heating value, and fixed carbon content significantly influence syngas outputs. Our results highlight a clear trade-off between maximizing hydrogen content and total gas yield and pinpoint optimal conditions for balancing this trade-off. This integrated approach, combining advanced ML predictions, explainability, and rigorous multi-objective optimization, is novel for biomass gasification and provides actionable insights to improve syngas production efficiency, demonstrating the value of data-driven optimization in sustainable waste-to-energy conversion processes.

Keywords: biomass gasification; syngas production; waste-to-energy; artificial intelligence; machine learning; multi-objective optimization; artificial neural networks; sustainable energy systems (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2025
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