Research on the Optimization of Chemical Waste Heat Recovery Device Operation Based on GA-BP Neural Network

Utilizing industrial waste heat as a heat source for centralized heating is a viable measure for energy saving and carbon reduction, which is of great significance for enhancing the social reputation and market competitiveness of enterprises. This paper proposes a study on the operation optimization of chemical waste heat recovery devices based on GA (genetic algorithm)-BP (back propagation) neural network technology: firstly, a heat load forecasting model is established utilizing the BP neural network, which is subsequently optimized through the application of a genetic algorithm; subsequently, the model's accuracy is thoroughly assessed using a meticulously prepared test set of data; finally, an optimization operation plan supported by quantitative data is generated in combination with chemical engineering calculations. The findings of this paper can guide production personnel to make precise adjustments and anticipatory regulation of water supply temperature and flow, provide clean and reliable heating services for the city, improve user experience, reduce carbon emissions, and enhance the market competitiveness of enterprises.