Work

An intelligent plant-wide decision-support framework for waste valorization: Optimizing hydrochar production and energy recovery

Year: 2025

Type: article

Abstract: This study presents MIRA (Multi-objective Integrated Resource Allocation), an intelligent decision-support framework for optimizing hydrochar production and energy recovery from diverse waste streams. MIRA integrates deep learning, thermodynamic modeling, and particle swarm optimization (PSO) to balance environmental and economic goals. Its hybrid architecture couples artificial neural networks (ANNs), trained on experimental data, with Aspen Plus simulations to ensure mass-energy conservation and thermodynamic consistency. The platform evaluates two waste valorization pathways: (1) direct combustion with energy recovery and (2) hydrothermal carbonization (HTC) followed by electricity generation. Scenario-based optimization was conducted on three representative feedstocks; organic household waste digestate (OHWD), municipal solid waste (MSW), and agricultural residue (AGR), under CO2-focused, revenue-focused, and balanced objectives. AGR showed the highest responsiveness, achieving up to 1.62 MWh and $153.9/ton wet feed. MSW enabled moderate trade-offs but was limited by high ash content, while OHWD showed stable yet constrained outcomes. Optimal hydrochar routing fractions (x_char) varied from 0.1 to 0.5 depending on feedstock and objectives. MIRA demonstrates the capability for accurate, feedstock-specific optimization of waste-to-energy systems and sets the foundation for future extensions to include additional thermochemical routes and integration with logistics and energy networks.

Source: Fuel Processing Technology

DOI: 10.1016/j.fuproc.2025.108320