Maximum solid concentrations of coal wastewater slurries predicted by optimized neural network based on wastewater composition data. (4th June 2021)

• Record Type: Journal Article
• Authors: Li, Dedi; Liu, Jianzhong; Chen, Cong; Liu, He; Lv, Hanjing; Cheng, Jun
• Is Part Of: Canadian journal of chemical engineering. Volume 100:Number 3(2022)
• Page Start: 465
• Page End: 475
• Publication Date: 2021-06-04
• Subjects: coal wastewater slurry -- maximum solid concentration -- neural network -- wastewater composition; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.24159
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: A variety of wastewaters can be generated in the coal chemical industry, and their treatment processes are complicated and have difficulty meeting standards. Using wastewater to prepare coal water slurry is an efficient and convenient new approach. The concentration of coal wastewater slurry is related to the content of the main wastewater components. A backpropagation neural network is developed to predict the maximum slurry concentration and analyze the mechanism at the data level according to the main component indicators, and a particle swarm algorithm is used to improve the neural network. The results are as follows: (a) it is feasible to predict the maximum concentration of coal wastewater slurry by a neural network, and a particle swarm algorithm can effectively improve the prediction accuracy in different models, reducing mean absolute error by up to 0.44%; (b) different input factors have different impacts on model prediction results—organic matter, ammonia nitrogen, and monovalent metal ions content as input factors to predict the maximum slurry concentration can get the most accurate results, obtaining a mean absolute error of 0.16% for the optimized backpropagation neural network and the lowest mean square error; and (c) divalent metal ions and phenols content are not suitable as input factors for predicting, as they all cause an increase in model error due to their weak or complex effects on the slurryability.