Leveraging multi-effect energy utilization in a biomass-based system for H2 production and urban energy needs: 4E analysis and ANN-based grey wolf optimization

Heng Chen, Sinan Q. Salih, Ahmad Almadhor, Mohammad Nadeem Khan, Zaher Al Barakeh, Raymond Ghandour, Fahad Alturise, Salem Alkhalaf, H. Elhosiny Ali and Hind Albalawi

Energy, 2025, vol. 333, issue C

Abstract: This study presents an innovative biomass-based multi-generation system that simultaneously produces power, freshwater, cooling, and hydrogen, leveraging advanced waste heat recovery and multi-effect energy utilization to maximize efficiency and sustainability. Unlike conventional systems, the proposed design synergistically integrates multiple thermodynamic cycles, including a Brayton cycle, supercritical CO2 cycle, humidification-dehumidification unit, absorption chiller, and proton exchange membrane electrolyzer, enabling highly efficient resource utilization and diversified energy production. A comprehensive energy, exergy, economic, and environmental assessment is conducted to quantify system performance across a range of operating conditions. The results reveal that the gasifier integrated with Bryton cycle has the highest cost rate and exergy destruction among other subsystems. Parametric studies demonstrate that increasing the biomass flow rate from 1 to 2 kg/s boosts grid power by 12.5 % and enhances hydrogen production, although exergy efficiency declines by 14.93 % due to rising irreversibilities. To overcome the computational burden of high-fidelity simulations, a novel hybrid optimization framework is introduced, integrating the multi-objective grey wolf optimizer with an artificial neural network. This approach drastically reduces computational time while preserving accuracy, facilitating efficient multi-objective optimization aimed at maximizing exergy efficiency and minimizing cost. Under optimal conditions, the system achieves an exergy efficiency of 38.67 % and a total cost rate of 157.65 $/h, while significantly reducing CO2 emissions to 0.75 ton/MWh. The findings demonstrate that this innovative multi-generation using biomass offers a scalable and sustainable solution for urban energy demands.

Keywords: Thermal analysis; Advanced waste heat recovery cycles; Biomass-based system; Grey wolf optimization; Hydrogen production; Artificial Neural Network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225029652

DOI: 10.1016/j.energy.2025.137323

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