Optimization of Organic Rankine Cycle Involving Operating Conditions and Work Fluids and Heat Integration

Abstract

In order to reduce the waste heat emission and environmental impact of industrial processes, Organic Rankine Cycle (ORC) is gradually used for energy recovery. ORC is regarded as the most promising measure for converting low-grade heat into electricity, but commercial applications are still limited due to the high investments and poor economic returns. However, the simultaneous optimization of ORC and Heat Integration can improve system economy. This work proposes a techno-economic optimization model involving the area estimate of heat exchanger based on vertical heat transfer for optimization of ORC and Heat Integration. This model determines the selection of working fluids, the optimal operating parameters of ORC including temperatures, pressures, and flowrate of working fluids. Both of the supercritical and subcritical conditions can be considered in this model. To solve this optimization problem, a bi-level optimization approach is developed, where the outer level uses Genetic Algorithm to identify the promising working fluids and optimize the temperatures and pressures of ORC, and the inner level is an NLP model to find the optimal flowrate of ORC and the vertical matches of streams by minimizing total annual cost. The results represent the necessity of simultaneous optimization of Organic Rankine Cycle and Heat Integration.