Integrated multi objective mixed integer nonlinear programming approach for emission and energy minimization in industrial boiler-turbine networks - Rohman, Fakhrony Sholahudin; Wan Alwi, Sharifah Rafidah; Ahmad Termizi, Siti Nor Azreen; Muhammad, Dinie; Er, Hong An; Azmi, Ashraf; Murat, Muhamad Nazri; Varbanov, Petar Sabev (Energy (Oxford), 2025)
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. •A hybrid GA-SQP method optimizes co-generation systems efficiently.•The hybrid approach reduces CO2 emissions by 10 % and saves 35 % energy.•Maximizing biomass use minimizes emissions, balancing cost and impact.•The hybrid method optimizes energy costs and environmental performance.•Biomass allocation offers a cost-effective, sustainable optimization strategy.