Prediction of Slag Viscosity Based on Machine Learning for Molten Gasification of Hazardous Wastes
Prediction of Slag Viscosity Based on Machine Learning for Molten Gasification of Hazardous Wastes
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DOI：
10.3390/min12121525
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发表时间：
2022-11
期刊：
影响因子：
2.5
通讯作者：
Changlun Li;Wenshuai Xi;Caihong Wang;Xiongchao Lin;Deping Xu;Yonggang Wang
中科院分区：
文献类型：
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作者：
Changlun Li;Wenshuai Xi;Caihong Wang;Xiongchao Lin;Deping Xu;Yonggang Wang
124个玻璃渣粘度测量的实验数据用于驱动和开发可用于直接或间接粘度预测的机器学习模型。根据化学成分的含量或通用竞争神经网络对样品进行分类。建立了基于人工神经网络的矿渣黏度直接预测模型。相应模型的预测平均误差和最大绝对误差均明显小于未对样本进行分类的人工神经网络。利用一般公式对各玻璃渣的粘度曲线进行拟合，得到相应的参数。提出了主成分分析(PCA) -粒子群优化(PSO) -反向传播（BP）神经网络参数预测模型。这种间接方法被认为成功地克服了直接预测温度和粘度范围的限制，同时提供了平滑的粘度曲线。
Experimental data from viscosity measurements of 124 glassy slags were used to drive and develop machine learning models that could be used for direct or indirect viscosity prediction. Samples were categorized according to the content of chemical components or general competitive neural network. The direct viscosity prediction using artificial neural network models of different kinds of slag samples was established. The prediction average error and maximum absolute error in the corresponding models were significantly smaller than the artificial neural network without categorizing the samples. Moreover, the viscosity curve for each glassy slag was fitted by a general formula, and the corresponding parameters were obtained. The principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) neural network models for predicting parameters were proposed. This indirect approach was considered to successfully overcome the limitations of temperature and viscosity ranges in direct prediction while delivering smooth viscosity curves.