An improved algorithm for synthesis of heat exchanger network with a large number of uncertain parameters

Abstract

This paper presents an improved method for the Mixed Integer Nonlinear Programming (MINLP) synthesis of flexible Heat Exchanger Network with a large number of uncertain parameters. Typically, such a problem is written as a multi-scenario two-stage stochastic model with recourse which is difficult to solve because the size of the model grows exponentially with the number of uncertain parameters. The exponential growth could be avoided by decomposing the model into simpler problems that are solved sequentially in a small number of scenarios.In this work, the determination of the first-stage variables (process topology and unit sizes) and the second-stage variables (operating and control variables) is decomposed into several steps where they are determined separately. This is achieved by iteratively solving smaller two-scenario MINLP and one-scenario Nonlinear Programming (NLP) subproblems. In this way, the size of the problem remains independent of the number of uncertain parameters.The innovation of this study is the introduction of correction factors for the second-stage variables into the objective function when determining the first-stage variables. In this way, better trade-offs are found that mitigate the unpleasant effect of decomposition. The synthesis of the heat exchanger network shows that the implementation of correction factors improves the optimal result by 7.6%.