Credibility-based cascading approach to achieve net-zero emissions in energy symbiosis networks using an Organic Rankine Cycle

Industrial symbiosis (IS) integrates multiple industries to share resources (e.g., energy, water, by-materials) for economic and environmental reasons. Energy demand is dynamic, imposing financial and logistical barriers to achieving Circular Energy (CEn). This paper addresses this by proposing a new framework that integrates central heating networks of an Organic Rankine Cycle (ORC) system to recover unused low-temperature energy. The framework is formulated as a stochastic programming problem to capture the uncertainty of some parameters to reduce the costs arising from fluctuations in energy supply and demand and, subsequently, wasted energy. Disruption management is also applied to handle the risk of suppliers becoming unavailable due to unforeseen events. The proposed multi-objective mixed-integer linear programming model optimizes the conflicting sustainability value preferences in the IS-based CEn network. The paper presents a novel robust possibilistic programming technique to control the epistemic uncertainty of the developed mode and develops a robust variant of the augmented ε-constraint algorithm (AUGMECON-R) to obtain the Pareto optimal solutions. The results provide a new platform for reducing the undesirable effects of worst-case energy symbiosis scenarios. The results show that using ORCs in an IS network increases the economic benefits of such a system by about 20% and reduces customer dissatisfaction from energy supply disruptions by about 27%.