首页|South China University of Technology Reports Findings in Machine Learning (Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods)
South China University of Technology Reports Findings in Machine Learning (Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Guangzhou, People's Republic of China, by NewsRx correspondents, research stated, “The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features.” Our news editors obtained a quote from the research from the South China University of Technology, “The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination ® increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification.”
GuangzhouPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNitrogen
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