Diagnosis and green emission reduction of power plant equipment based on machine learning classification algorithm

The failure and unreasonable operation of power plant equipment can lead to energy waste and increased environmental pollution; therefore, effective diagnosis and emission reduction methods are needed to improve the performance of power plant equipment. This article is based on machine learning classification algorithms to develop a diagnostic and green emission reduction method for power plant equipment, in order to improve the performance of power plant equipment and reduce environmental pollution. The article collected a large amount of operational data of power plant equipment and preprocessed and extracted features from it. Then, machine learning classification algorithms are used to diagnose and classify equipment faults and unreasonable operation. By comparing and selecting these algorithms, the most suitable algorithm for diagnosing power plant equipment is found. Through experiments and validation, the method developed in this article has achieved good results. This method can accurately diagnose the fault types and operating status of power plant equipment and provide corresponding solutions. By optimizing the operating parameters and control strategies of power plant equipment, the emission of environmental pollution has been effectively reduced, achieving the goal of green emission reduction.