A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications

An optimization model based on the use of Neural Network surrogate models for the multi-objective optimization of small scale Organic Rankine Cycles is presented, which couples the optimal selection of the thermodynamic parameters of the cycle with the main design parameters of In-Flow Radial turbines. The proposed approach proved well suited in the resolution of the highly non-linear constrained optimization problems, typical of the design of energy systems. Indeed the use of a surrogate model allows to adopt gradient based methods that are computationally more efficient and accurate than conventional derivative-free optimization algorithms. The intensive numerical experiments demonstrate that assuming a constant efficiency for the In-Flow Radial turbine leads to an error in the evaluation of the performance of the system of up to 50% and that the optimization approach proposed improves the accuracy of the solution and it reduces the computational time required to reach it by two orders of magnitude. An holistic approach in which the turbine and the thermodynamic cycle are designed simultaneously and the use of multi-objective optimization proved to be essential for the design of Organic Rankine cycles that satisfy both size and performance criteria.