Optimization of circulating cooling water systems based on chance constrained programming

Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously. An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained (total cost can be reduced at least 2%). (C) 2021 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.

Automated summary

This paper investigates the optimization of circulating cooling water systems under uncertain circumstances, proposing a model based on chance constrained programming method. An algorithm using Monte Carlo method is developed to solve the model, which aims to minimize total cost and achieve optimal cooling network configuration. The results show that considering different uncertain parameters can lead to a system with better economy and reliability.