Novel model predictive control by hypothetical stages to improve energy efficiency of industrial cooling tower. (October 2022)

• Record Type: Journal Article
• Authors: Kumari Agarwal, Neha; Biswas, Pinakpani; Shirke, Anand
• Is Part Of: Applied thermal engineering. Volume 215(2022)
• Publication Date: 2022-10
• Subjects: Cooling tower -- Model predictive control -- Aspen plus -- MATLAB -- Simulink -- Energy savings; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2022.118899
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Mathematical model of cooling tower based on analogy to distillation column. Aspen Plus model was developed for cooling tower. Both models were validated with real plant operating data. Controller was designed to control performance of cooling tower with climate. Resulted in 30% reduction in overall power consumption of cooling tower. Abstract: 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.