(657d) Comparative Hybrid Modeling and Optimization of Biomass-Plastic Co-Gasification to Methanol Via Syngas Conversion

Sustainable energy solutions that valorize waste materials—such as biomass, plastic waste, and municipal solid waste—are essential to ensure energy security, improve efficiency, and reduce environmental impact. These materials can be converted into synthesis gas (syngas), a versatile intermediate used as a clean fuel or a feedstock for producing chemicals like methanol (MeOH). However, achieving efficient and scalable syngas conversion requires overcoming several technical challenges, particularly in optimizing reactor configurations and adapting to diverse feedstock compositions. This study presents a hybrid modeling framework for syngas-to-MeOH production that integrates traditional simulation with advanced data-driven meta-modeling techniques. The process begins with steam co-gasification of biomass and plastic waste in a bubbling fluidized bed (BFB) reactor. A detailed kinetic model is developed in Aspen Plus to simulate the gasification process, capturing key thermochemical reactions and enabling the prediction of unconverted char, tar, ash, and syngas composition. To enhance predictive capabilities, a neural network (NN)-based meta-model, trained on experimental data from literature, is employed to predict the syngas composition as a function of gasification temperature. In this approach, the kinetic model is retained for all mass flow calculations, while the NN model is used specifically for syngas composition prediction. The experimental dataset used for training and validation was based on co-gasification studies performed at temperatures between 735 °C and 835 °C with a constant steam-to-biomass ratio (S/B) of 0.8 [1]. The NN model demonstrated high accuracy in capturing temperature effects on syngas quality. Notably, the H₂/CO ratio increased from 1.81 to 1.91, and the corresponding stoichiometric ratio (SR = (H₂ − CO₂)/(CO + CO₂)) increased from 0.94 to 1.07 across this temperature range. These findings indicate the necessity of additional syngas conditioning. A water-gas shift (WGS) reactor is proposed to increase hydrogen content, while Selexol—a physical solvent—is used for selective CO₂ capture, allowing precise adjustment of syngas composition to meet methanol synthesis requirements. Methanol production is modeled using two reactor configurations—an isothermal and an adiabatic reactor—implemented in Aspen Plus. Both kinetic models are based on Langmuir-Hinshelwood and Eley-Rideal mechanisms and are benchmarked for performance under the refined syngas conditions. Process integration and optimization are performed using the OSMOSE platform, which enables evaluation of minimum energy requirements (MER), capital expenditure (CAPEX), operating expenditure (OPEX), and key performance indicators (KPIs) for the virtual methanol plant. Additionally, the quantification of CO₂ emissions, carbon credit assessment, and life cycle analysis (LCA) to evaluate the facility’s overall environmental performance. By combining mechanistic and AI-driven modeling, advanced syngas conditioning strategies, and integrated energy system optimization, this study proposes a robust and scalable framework for converting waste into sustainable methanol. The comparative assessment of kinetic and meta-modeling approaches provides critical insights for developing low-carbon, cost-effective fuel and chemical production systems.

Reference

[1] Pinto F, Franco C, André RN, Miranda M, Gulyurtlu I, Cabrita I. Co-gasification study of biomass mixed with plastic wastes. Fuel. 2002;81(3):291-7.