Novel model predictive control by hypothetical stages to improve energy efficiency of industrial cooling tower

Cooling towers are unit operations that are used in almost every industry to cool down process water either by natural draft or forced draft. The energy intensive components of cooling towers are draft fans and circulating pumps which in traditional practise run at full rated capacities throughout the year ignoring the underlying opportunities for energy savings. The efficiency of a cooling tower is a function of ambient air temperature, relative humidity and wet bulb temperature. This work brings out a novel model of cooling tower along with an energy harnessing control system based on prevailing climatic conditions thus improving the overall efficiency of the system. Based on analogy to a distillation column with hypothetical stages a model for cooling tower is developed. Then a model predictive control is designed to control the draft fan speed and pump flow rate of cooling tower based on climatic conditions. Both the model and control strategy was developed using Aspen Plus (V12.1), MATLAB (R2018b) and Simulink softwares and has been validated and trained based on plant operating data. The developed model was then tested at a pilot cooling tower facility of capacity 1 Ton of Refrigeration and was observed to attain approximately 30% reduction in energy consumption compared to the traditional operation.

Automated summary

This study proposes a novel model of cooling tower and an energy harnessing control system to improve overall efficiency based on prevailing climatic conditions. By employing model predictive control, the draft fan speed and pump flow rate of the cooling tower are controlled according to climatic conditions. Experimental results show that energy consumption is reduced by approximately 30% compared to traditional operation.