Mathematical modelling and optimization of a liquid separation condenser-based organic Rankine cycle used in waste heat utilization

The organic Rankine cycle (ORC) is a promising method of generating power that utilizes low-enthalpy renewable energy and industrial waste heat. Given that the exergy loss and capital investment cost of heat exchangers account for a large proportion of the components in the ORC, research on the development of novel heat exchangers and configuration optimization coupling with other components and cycle parameters is essential to improve the ORC performance. In this study, an innovative liquid separation condenser (LSC) is incorporated into a waste heat-driven ORC system. A non-convex mixed integer non-linear programming model is formulated to simultaneously optimize the component configurations and system parameters of the LSC-based ORC. The objective is to minimize the electricity production cost of the ORC. The pressure drops of the heat exchangers are directly incorporated into the cycle model instead of ignoring them or constraining them by upper bounds. The physical property parameters of the working fluid are regressed using the Refprop 9.0 database to ensure the optimization of cycle operating variables. A case study is presented to test the proposed methodology and the formulated model. The results achieved using the simultaneous optimization method are compared with the results achieved using the sequential optimization method. The optimization results of the LSC-based ORC are also compared with those of the parallel flow condenser-based ORC. Then the influences of the key structure variables on the optimization results are studied. Finally, a sensitivity analysis of heat source parameters and environmental parameters on the optimization results are conducted. •A novel liquid separation condenser-based ORC is proposed and validated.•A MINLP model is formulated for the optimization of component configurations and cycle parameters.•A SIO algorithm is developed and validated using a SEO algorithm.•A case study is presented to demonstrate the proposed methodology and the formulated model.•The influences of key component structure parameters on the cycle performance are conducted.