Model reductions for multiscale stochastic optimization of cooling water system equipped with closed wet cooling towers

Abstract

Incorporation of closed wet cooling tower (CWCT) in the existing circulating water system has been recognized as a viable path to reduce water use in process industry. This paper introduces a specifically tailored framework based on model reductions for multiscale optimization of CWCT-based cooling water system considering environmental variations. An optimal design of experiment is performed for accurate approximation of the multivariate probability distributions by generating a finite set of samples over the entire input space. The probability distributions are propagated via multi-sample CFD simulations for constructing the physics-based and data-driven reduced models of CWCTs. Based on the developed reduced models, a multiscale optimization model is proposed for performing integrated design and management of CWCTs and cooling water system. It employs sampling-based stochastic programming and the heterogeneous integration of reduced models of CWCTs and other shortcut models. Finally, the performance of the proposed approach is illustrated through its comparison with a deterministic approach.