PREDICTIVE MODELLING OF DAILY DRIED SLUDGE PRODUCTION IN FULL-SCALE WASTEWATER TREATMENT PLANT USING DIFFERENT MACHINE LEARNING COMBINED WITH EMPIRICAL MODE DECOMPOSITION

K. ZAIDI, M. DJEDDOU, F. SEKIOU, I.A. HAMEED, M. SHAWAQFAH

Abstract


Sewage sludge has gained importance and become a general significant environmental concern due to the presence of dangerous heavy metals and organic pollutants. In this study, various simple machine learning (ML) models, namely, multilayer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), generalized regression neural network (GRNN), extreme learning machine (ELM), and support vector regression (SVR), were compared with hybrid empirical mode decomposition (EMD-ML) and variational mode decomposition (VMD-ML). The RBFNN model had the best results for the simple ML models because of the best performance parameters compared with other simple models. The EMD-ML models’ results revealed that the EMD-MLPNN model had high performance parameters and lower errors compared with the remaining models, and the VMD-ML models’ findings indicated that the VMD-GRNN model had good statistical indicator parameters compared to other models. The qualitative comparison findings indicated that the EMD-MLPNN method produced the best predictive performance for the training phase with R = 0.9729 and MAE = 2.5521 and during the testing phase with R = 0.9909 and MAE = 2.1144 in comparison to the VMD-GRNN and the RBFNN. The combination of EMD-ML improved ML accuracy, especially for EMD-MLPNN, in predicting daily dried sludge production in WWTPs.


Keywords


Predictive Modeling, Daily Dried Sludge Production, Wastewater Treatment Plant, Machine Learning, Empirical Mode Decomposition, Variational Mode Decomposition.

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References


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