Hierarchical thermal utilization in an integrated biomass–LNG poly-generation system with hydrogen liquefaction: ANN-assisted thermo-environ-economic optimization
International Communications in Heat and Mass Transfer, vol.178, no.P5, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 178 Issue: P5
- Publication Date: 2026
- Doi Number: 10.1016/j.icheatmasstransfer.2026.111896
- Journal Name: International Communications in Heat and Mass Transfer
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Keywords: Biomass gasification, Hydrogen production and liquefication, LNG cold energy recovery, Poly-generation system, Surrogate-based optimization
- Istanbul Gelisim University Affiliated: Yes
Abstract
Liquefied green hydrogen is a paramount zero-carbon fuel essential for deep decarbonization, yet its cryogenic production imposes considerable thermodynamic penalties. This study develops a biomass-driven poly-generation system designed around hierarchical thermal utilization to overcome these inefficiencies and simultaneously produce liquefied hydrogen, electricity, freshwater, heating, and cooling. The architecture couples thermochemical biomass gasification with LNG regasification, strategically leveraging high-temperature exergy from biomass and deep cryogenic heat sinks from LNG. A biomass-fed Brayton cycle is integrated with multi-tier bottoming networks, including steam and organic Rankine cycles, a transcritical CO₂ cycle, and a thermoelectric generator, to maximize multi-grade waste-heat recovery. A complete energy-exergy-economic-environmental assessment is conducted, with performance evaluated across key operating parameters, including biomass feed rate, gas turbine inlet temperature, Brayton compressor pressure ratio, steam turbine inlet pressure, and heat exchanger pinch-point temperature difference. Results indicate that increasing the biomass feed rate enhances system outputs but reduces exergy efficiency. Similarly, increasing the gas turbine inlet temperature improves power output, hydrogen production, and cooling capacity, while increasing overall cost due to higher material and maintenance requirements. An ANN-based surrogate model is coupled with the NSGA-II algorithm for multi-objective optimization, substantially reducing computational burden while preserving excellent fidelity. Under optimal conditions in Scenario 1, the system achieves 40.83% exergy efficiency, 6.81 MW net power, and 14.89 kg/h liquefied hydrogen at a cost rate of 134.86 $/h. Overall, the proposed integration provides a scalable and thermo-economically viable pathway for sustainable fuel production.