Muthanna Journal of Engineering and Technology
Volume (14), Issue (3), Year (2026), Pages (1-15)
DOI:10.52113/3/eng/mjet/2026-14-03-/1-15
Research Article By:
Mujahed Kareem Oglah
Corresponding author E-mail: Ms2000955@gmail.com
ABSTRACT
Biomass-fired multigeneration systems that utilize gas turbines coupled to organic Rankine cycles (ORC) and absorption chillers (AC) show great potential for providing clean and reliable energy. However, their multi-objective optimization remains challenging. Two main barriers exist: the highly nonlinear coupling between thermodynamic and economic decision variables, and the black-box nature of most machine-learning (ML) surrogates used in this domain. In this paper, we develop a SHAP-explainable ensemble ML framework to enable 4E (energy, exergy, economic, and environmental) analysis and multi-objective optimization of a biomass-fired gas turbine/AC/ORC-based multigeneration system. A dataset of 1,000 Latin Hypercube Sampling (LHS) simulation cases from a validated thermodynamic model is generated and five ensemble models (RF, GBR, XGBoost, Light GBM, Ca tBoost) are trained, compared, and evaluated, with a tuned XGBoost achieving R² > 0.97 for all targets. Multi-level SHAP analysis identified turbine inlet temperature and pressure ratio as the most important design drivers for all performance metrics. Multi-objective optimization with NSGA-II and multi-criteria decision-making with TOPSIS identified the optimal design with an exergy efficiency of 61.8% and SUCP of ~6.10 $/GJ. The presented framework, which overcomes the black-box limitation and achieves high predictive performance, can be used to bridge the gap between predictive accuracy and engineering interpretability in the design of biomass-fueled multigeneration systems.
Keywords:
biomass gasification; ensemble machine learning; SHAP explainability; 4E analysis; multi-objective optimizationز