Research On Energy Consumption Optimization And Application Of Air Compressor Station Based On Machine Learning Algorithm

Air compressor stations are common power systems in public works,which consums a large amount of electrical energy,and at the same time there is a certain degree of waste of electric energy.The conventional way to save energy in air compressor stations is mainly to install frequency converters.Even after the implementation of conventional energy saving methods,there is still a large room for energy saving in air compressor stations--that is,the implementation space of the optimized energy saving technology in this paper.Optimized energy saving is a new generation of energy-saving technology after management energy-saving,equipment energy-saving and process energy-saving,that is,using artificial intelligence algorithm to optimize the energy consumption of multiple devices under the condition of variable load.In the method of optimizing energy saving,there are many factors that can affect the energy saving effect,such as process,equipment,control system and so on.This paper takes a domestic cigarette factory air compressor station energy saving optimization project as the background,using machine learning algorithms,machine learning for each air compressor operation parameters,such as pressure,electricity consumption,through continuous learning and analysis of these data,later for the air compressor at a certain point in time or time period of the operation status information,always find the smallest electricity consumption operation combination of the most energysaving operation,and through the newly added PLC reverse intelligent control of each air compressor start and stop,to ensure that the exhaust pressure to meet the normal working requirements to achieve the minimum electricity consumption.The energy consumption prediction model of the air compressor station is established based on the BP neural network algorithm,and the energy consumption optimization model of the air compressor station is established based on the particle swarm optimization algorithm.The final control point of the machine learning algorithm lies in the combination scheduling of air compressor sets.According to the air pressure of the storage tank and the demand of gas consumption,the optimization energy-saving algorithm of artificial intelligence is combined with the optimization energy-saving algorithm to select the appropriate combination of machines and adjust the loading and unloading,start and stop of air compressors in real time to realize the optimization energy-saving of air compressor sets.During the implementation of the project,the AI optimization algorithm can independently learn from the collected data,so that it can understand the actual situation of each machine better than a human.Thus,the most suitable machine or combination of machines can be selected according to the actual situation.Through the analysis of the operation results,the machine learning algorithm can optimize the energy consumption of the air compressor station with good energy saving effect.