11/21/2023 0 Comments Wastewater treatment effluentIn recent years, AI models have been successfully applied in different engineering fields such as water resources and the behavior of WWTPs. Data analysis in real-time with data-driven methods in a WWTP can detect process failures that reduce operating costs and control the WWTP's performance ( Newhart et al. AI methods can significantly reduce the complexity of the system, identify the effective parameters and achieve the desired result ( Roushangar et al. Whenever the interrelationships of the related variables are not well understood, it is difficult to find the final solution, and conventional mathematical models cannot present analytic solutions. According to the black-box nature of these models, the learned relationship between input and output is not apparent and can only be tested with a new sample. In AI models, data characterizing analysis of a system is used to capture relationships between the related input and output variables without considering the physics of a modeled process. This modeling mainly relies on archived data in predicting measured variables ( Nadiri et al. The primary advantage of AI models is no need to recognize full physics of a system. Therefore, artificial intelligence (AI) models are effective approaches to deal with the non-linearity and intricacy of the problem, and have attained successful results ( Chan & Huang 2003 Türkmenler & Pala 2017). In these methods as the number of variables and their interactions increases, the accuracy of operation predicting would decrease. These processes illustrate non-linear behaviors that will hardly lead to a description with mathematical models ( Mjalli et al. WWTPs comprise some complex processes such as physical, biological, and chemical processes. Proposing a modeling tool to predict the performance of the WWTP, based on previous observation of main parameters of quality, seems necessary for controlling a WWTP. Considering the rise in the number of WWTPs, the initial prediction and then the analysis of pollutant parameters based on new methods are getting more attention ( Türkmenler & Pala 2017). Reducing the operation cost and improving the effluent quality of a WWTP are the main purpose of the management of the WWTP to reduce the pollutants ( Hao et al. Various factors such as environmental nature, technological and economic affect the operation of wastewater treatment plants (WWTPs). Wastewater consists of water, organic matter, minerals and living organisms ( Nourani et al. Wastewater is a diluted mixture of various wastes of residential, commercial and industrial areas the characteristics of wastewater vary, depending on the source of discharge and the lifestyle of the community. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. On the other hand, since applied methods were sensitive to input variables, the Monte Carlo uncertainty analysis method was used to investigate the best-applied model dependability. Three time scales, daily, weekly, and monthly, were investigated in the modeling process. In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. In this study, the effluent quality of Tabriz WWTP was assessed using two intelligence models, namely support Vector Machine (SVM) and artificial neural network (ANN). In recent decades, artificial intelligence approaches have been used as effective tools in order to investigate environmental engineering issues. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. Wastewater treatment plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential.
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