Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation - Mujtaba, M. A.; Munir, Muhammad Adeel; Akhtar, Muhammad; Mahmood, Bilal; Ansar, Talha; Khawar, Zeeshan; Khalid, Shayan; Basit, Abdul; Jamil, Saud; Kalam, M. A.; Hussain, Fayaz; Bhowmik, Chiranjib (Frontiers in Energy Research, 2025)
United Nations Sustainable Development Goal 7 is about ensuring access to clean and affordable energy, which is a key factor in the development of society. The power generation sector majorly consists of thermal power plants. Cooling towers are a significant part of any power plant to cool steam to be reused again. Hence, the efficiency of power plants can be increased by optimizing the performance of cooling towers. This research paper aims to increase the efficiency of cooling towers by investigating the effect of ambient parameters (changing with climate) on the efficiency of cooling towers for the best site selection. Ambient parameters cannot be controlled after the installation of power plants. Therefore, proper site selection, keeping ambient parameters and their expected change before the installation of power plants, effectively increases the efficiency of the cooling tower and, ultimately, the power plant. For this purpose, data is collected from the 1140 MW combined cycle power plant in Sheikhupura, Pakistan district. A machine learning (Ada boost regressor) model has been used to quantify the effect of ambient parameters on cooling tower efficiency. After tuning the hyperparameters, an R-square score of 0.983 and a root mean squared error of 0.57 are achieved. Afterwards, a sensitivity analysis of relative humidity (%), turned out to be the most important feature, with a contribution of 12%. The novelty of this research lies in its mathematical model for power plant site selection, which optimizes cooling tower efficiency, reduces pollution, and promotes environmental sustainability.