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.

Load optimisation within the cogeneration system is crucial in enhancing energy efficiency. Instead of constructing the mathematical optimisation model or applying the commercial utility optimisation software with a licensing fee, this study proposes a holistic P-graph method to model and optimise the cogeneration system using the free and user-friendly software, P-graph Studio. To consider actual performance of unit operations, novel slope-constant element is introduced in the P-graph structure to adapt the variable output-input ratios in the form of linear performance model with non-zero constant. This overcomes the functionality of the conventional P-graph structure that only considers fixed output-input ratio. A case study of an industrial cogeneration system is optimised using the proposed P-graph method, resulting in 1.24 % reduction of operating cost and CO2 emission: equivalent to savings of RM 12,822,300/year and 4,300 tonnes CO2 emission/year. Two operating strategies are proposed to revise the optimal operating method by modifying the P-graph superstructure to ensure adequacy of the utility margin in meeting the potential maximum utility demand. The operating cost saving of 0.53 % is achieved after revision to meet both operational efficiency and reliability of the cogeneration system which results in savings of RM 5,454,900/year and 1,800 tonnes CO2 emission/year.