Algorithm Development for Prediction and Control of Pollutant Emissions in Chemical Production

With the rapid development of chemical production, the negative impacts of pollutant emissions on the environment are becoming more and more serious, and the prediction and control of pollutant emissions in chemical production has become a key issue in the field of environmental protection. The purpose of this paper is to develop an efficient pollutant emission prediction and control algorithm, through data acquisition and processing, model construction and optimization, combined with real-time monitoring and feedback mechanisms, to achieve effective prediction and control of pollutant emissions in the chemical production process. Firstly, this paper introduces in detail the main pollutant sources and their emission characteristics in chemical production; secondly, a pollutant emission prediction model based on multiple algorithms (such as linear regression, neural network and support vector machine) is proposed, and the model is trained and validated by actual data; finally, a set of pollutant emission control strategies based on the prediction results is designed, and its validity is verified in a typical case. The results show that the algorithm can significantly improve the accuracy of pollutant emission prediction and effectively reduce the pollutant emissions in chemical production, providing important technical support for environmental protection.