关键词: 碳基固废, 热解, 预测, FUSION模型, 数学模拟

Abstract:

Low-temperature pyrolysis is a promised method for cleanly converting carbon-based solid waste, achieving carbon sequestration and reducing emissions. By establishing a mathematical model for predicting the yields of carbon-based solid waste pyrolysis products, the time for scientific research and exploration can be greatly shortened, and the pyrolysis reaction process can be optimized and controlled. In this paper, 80 sets of pyrolysis experimental data as samples were used. The neural network (ML), support vector machine (SVM) and linear regression (LR) models were firstly trained and tested to analyze the effectiveness of machine learning, and then the three models through algorithm fusion, an adaptive FUSION model (FUSION) was established. At last, the model was further trained and tested with experimental data to form a data model suitable for predicting carbon-based solid waste pyrolysis products. The FUSION model can effectively solve the problem that a single model is affected by the interaction of pyrolysis in the process of predicting the distribution of carbon-based solid waste pyrolysis products, and the prediction accuracy fluctuates. Meanwhile, the predicted value of this model has high accuracy, and the relative error between the predicted value and the experimental value is less than 2%.

Key words: carbon-based solid waste, pyrolysis, prediction, FUSION model, mathematical modeling