A meta-heuristic approach for financial risks management in heat exchanger networks

Abstract

Mathematical programming models for heat exchanger networks (HEN) synthesis generally do not take into account the fluctuations in the costs of commodities related to the plant operation. This work proposes to include such features in the HEN synthesis model by assuming utility production costs as uncertain and considering them as stochastic variables. These are discretized via Monte Carlo Simulation (MCS) with the Geometric Brownian Motion (GBM) model, which creates an appropriate number of scenarios throughout plant lifetime based on natural gas and electricity historical price data. The downside risk, which is a target based metric, is used for financial risks management. Multi-objective optimization (MOO) can be performed to better address trade-offs between solutions’ expected total annual costs (ETAC) and the chosen metric. A meta-heuristic two-level approach is adapted to handle such MOO. The developed scheme is based on the ε-constraint method. Two costs targets are tested with downside risk. The number of scenarios with costs higher than the targets is reduced when compared with single ETAC optimization. The developed meta-heuristic solution method was able to efficiently perform MOO in a model with rather large number of scenarios, and is a good option for applying risk metrics including uncertainties in HEN synthesis.