Integrated multi objective mixed integer nonlinear programming approach for emission and energy minimization in industrial boiler-turbine networks

Fakhrony Sholahudin Rohman, Sharifah Rafidah Wan Alwi, Siti Nor Azreen Ahmad Termizi, Dinie Muhammad, Hong An Er, Ashraf Azmi, Muhamad Nazri Murat and Petar Sabev Varbanov

Energy, 2025, vol. 335, issue C

Abstract: This study investigates the optimization of a co-generation system involving multiple steam boilers and turbines, aiming to minimize CO2 emissions and energy consumption while maintaining reliable energy delivery. A hybrid Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) method is implemented within a Multi-Objective Mixed-Integer Nonlinear Programming (MOO-MINLP) framework. The approach effectively captures the nonlinear behavior of efficiency and operational constraints. The results show a reduction of up to 10 % in CO2 emissions and over 35 % in energy savings compared to GA-only approaches. Maximizing biomass usage at Extreme Point A achieves the lowest emissions (554.29 kg) and an energy cost of 4253.69 GJ, while minimizing energy consumption at Extreme Point C leads to 3532.67 GJ but higher emissions (708.86 tons). This study demonstrates the hybrid GA-SQP method's potential to optimize both CO2 emissions and energy consumption, offering decision-makers a balanced approach between cost and environmental impact. The results underscore the significance of fuel allocation, especially biomass, in reducing emissions despite lower efficiency, presenting a cost-effective and sustainable solution for co-generation system optimization.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s036054422503645x

DOI: 10.1016/j.energy.2025.138003

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