In many chemical plants, a variety
of cooling systems, including
circulating cooling water systems and air coolers, are widely used
to remove industrial waste heat. Current approaches for the design
and optimization of these systems have focused on the use of constant
parameters, including fixed temperature and energy price. Moreover,
the ever-changing weather conditions play a major role in the design
of cooler water systems and air coolers, leading to an unbalanced
problem formulation that attempts to solve a complex problem with
minimum rigor. Therefore, the concept of bi-multiperiod optimization
is proposed to describe the daily/monthly change of weather conditions,
as well as the fluctuation of electricity price during peak and off-peak
periods within a day. The aim of this work is to obtain an economically
optimal cooling system design under bi-multiperiod conditions by reasonably
configuring both water coolers and air coolers. A mixed integer nonlinear
programming (MINLP) model was established to obtain the optimal configuration.
A case study derived from the literature shows that the optimized
cooling network configuration using the proposed method is more economic
than the deterministic design, the cooling water consumption is reduced
by 5%, and the total cost is reduced by more than 9%.