Application of BP Neural Network in Pyrolysis Treatment of Organic Solid Waste

The improper management of organic solid waste can precipitate many environmental issues. Pyrolysis, characterized by its capacity for rapid reduction and resource utilization of organic solid waste, has garnered considerable attention. Machine learning, an autonomous system capable of knowledge acquisition and integration, holds promise for predicting the thermochemical conversion products of organic matter. The backpropagation (BP) neural network, owing to its simplistic model structure, high prediction accuracy, and robust self-learning capability, has seen extensive application in the predictive modeling of organic solid waste pyrolysis treatment in recent years. Despite the breadth of research, a comprehensive summary of the BP neural network’s research outcomes in organic solid waste pyrolysis and a comparative analysis of the models are conspicuously absent. This study systematically overviews the application of BP neural network in organic solid waste pyrolysis. Initially, the specific structure of the BP neural network model and parameter optimization are delineated. Subsequently, an evaluation of the BP neural network’s practical application in organic solid waste pyrolysis treatment is provided, along with a comparison of the parameter selection across different models. Finally, the efficacy of the BP neural network model in predicting the scenario of organic solid waste pyrolysis treatment is expounded upon, and potential directions for future development are discussed.