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.

Despite the Malaysian government's efforts to promote cogeneration technology, it constitutes a mere 4.2 % of the country's total electricity generation. To address this issue, this review article aims to identify and benchmark global best practices for bolstering cogeneration implementation in Malaysia. While previous studies in this domain have centered on examining government policies within the specific regions, this paper underscores the imperative for an updated and comprehensive literature review to systematically collect and categorise best practices from around the world. This work examines the adoption of cogeneration energy systems within nations characterised by a substantial presence in their energy composition, simultaneously exploring the roles of regulatory frameworks in promoting their uptake. The scope of the literature review includes statistical data, reports, and other relevant scholarly works on cogeneration policies. Based on the findings, the review paper concludes that well-crafted policies are instrumental in encouraging rapid technology advocacy. The legislative landscape pertaining to cogeneration implementation in Malaysia is scrutinised, followed by a comparative analysis against global best practices to identify opportunities for improvement. Three crucial elements emerge as paramount to expediting the adoption: (i) administrative recognition, (ii) financial initiatives, and (iii) regulatory improvements. Several recommendations for the Malaysian cogeneration roadmap are presented. Overall, this paper offers insights into the chronological evolution of cogeneration policy development and provides actionable guidelines for developing effective policies in Malaysia and other South-East Asian countries.

Industrial symbiosis has gained prominence in pursuing sustainable industrial practices, aiming to optimise resource utilisation and reduce environmental impacts. A critical aspect of this endeavour is the efficient management of energy resources within an industrial ecosystem. This paper presents a novel approach to enhance Total Site Heat Integration (TSHI) implementations by employing Blockchain as a facilitator for decentralised energy management. TSHI is an efficient and widely applied method for industrial symbiosis concerning energy flows, which employs the steam mains in site utility systems as the platform for exchanging heat between industrial processes at various temperature levels. It is shown that the synergy of Pinch Analysis and Smart Contract technologies is capable of facilitating energy integration of processes belonging to independent market actors, compared with the currently dominant integration inside a single company. The proposed framework leverages Blockchain as a distributed ledger to enable secure, transparent, and automated energy management across multiple industrial entities in an industrial symbiotic network. The integration of Pinch Analysis principles ensures that the Heat Integration process is optimised to improve the overall energy efficiency. Smart contracts enable automatic negotiation and execution of energy transactions based on predefined rules, minimising the time lag for concluding deals on energy resource exchange and conservation. This paper examines several scenarios to illustrate the implementation of the proposed Blockchain-based TSHI concept within an industrial symbiosis network. It is demonstrated that up to 16 % cost savings are possible by simply enabling transparency via Blockchain. The results could drive innovative development to revolutionise decentralised energy management in a complex industrial ecosystem, especially by synchronising energy exchanges in time.