(340n) Simultaneous Economic Optimization of Organic Rankine Cycle with Working Fluids Selection and Heat Integration

In order to reduce the waste heat emission and environmental impact of industrial processes, Organic Rankine Cycle (ORC) is gradually applied to energy recovery. ORC is regarded as the most promising measure to convert low-grade heat into electricity but commercial applications are still limited because of the high investments and poor economic returns. However, the simultaneous optimization of ORC and heat integration can improve system economy. Hence, this work proposes a techno-economic optimization model involving the area estimate of heat exchanger based on vertical heat transfer for simultaneous optimization of ORC and heat integration, and it is suitable for the large-scale industrial processes. This model determines the selection of working fluids, the optimal operating parameters of ORC including temperatures, pressures and flowrate of working fluids, and the matches of hot and cold streams. Meanwhile, both of the supercritical and subcritical conditions can be considered in our model. To solve this optimization problem, we develop a bi-level optimization approach, where the outer level uses the 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 necessary of simultaneous optimization of Organic Rankine Cycle and heat integration.