A systematic approach to modeling organic Rankine cycle systems for global optimization

The demand for organic Rankine cycle (ORC) systems to be efficient and economically competitive drives the need for a reliable and robust modeling approach that is suitable for optimization. However existing commercial simulation software is not typically tailored for optimization and they generally cannot guarantee a global optimum. This paper proposes a modeling approach to approximate a rigorous simulation model that is suitable for global optimization. This involves a combination of regression and thermodynamic analysis, in addition to integer programming techniques. Three different solvers, namely COBYLA, SCIP, and BARON, are used to optimize the ORC model and are compared against each other to demonstrate the prospect of achieving the global optimum using this approach. In addition, this paper also presents a technique to improve the model accuracy by using a piecewise fit to approximate the output characteristic of the ORC unit operations.