Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study)

Abstract

Wastewater Treatment Plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. In recent decades, Artificial Intelligence (AI) approaches have been used as effective tools in order to investigate environmental engineering issues. In this study, the effluent quality of Tabriz Wastewater Treatment Plant (Tabriz WWTP) was assessed using two intelligence models namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. Three daily, weekly, and monthly time scales were investigated in the modeling process. On the other hand, since applied methods were sensitive to input variables, therefore, the Monte-Carlo uncertainty analysis method was used to investigate the best-applied model dependability. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP.

Fixed-bed column study for the remediation of the bauxite-liquid residue using acid-activated clays and natural clays

“…The quality of the treated samples from the column adsorption experiment was investigated and measured by following the procedures outlined in the literature [ 29 , 30 ]. The quality parameters considered in the study were pH, temperature, electrical conductivity (mS/cm), total suspended solids (mg/L), total dissolved solids (mg/L), biochemical oxygen demand (mg/L), turbidity (NTU), and total alkalinity (mg/L).…”

Enrichment of the Waste Effluents of Handmade Paper Industry for Biofertilizer Use in Green Chili Plants

“…The initial forecast and subsequent analysis of pollutant parameters based on novel methodologies are receiving more attention, given the increase in the number of Wastewater treatment plants. Therefore, managing waste e uents as converting into bio fertilizer and using them for crop production are effective approaches to deal with the non-linearity and intricacy of the problem and have attained successful results (Hejabi et al, 2021) E uents pose a great concern for each and every industry including the paper mills. In fact, the e uents of papermaking always need more attention due to their dark colours which on disposal may destroy the aesthetic value of a water body/land.…”

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

“…These algorithms and tools are simple, adaptable, and efficient for analyzing datasets of small as well as large-scale plants. Some commonly used intelligent algorithms and tools being used lately include Artificial Neural Networks (ANN) 12 – 15 , Artificial Bee Colony (ABC) 16 , 17 , Cat Swarm Optimization (CSO) 18 , 19 , Particle Swarm Optimization (PSO) 20 – 22 , Firefly Algorithm (FA) 23 , Bat Algorithm (BA) 23 , 24 , Whale Optimization Algorithm (WOA) 17 , 25 – 27 , Grey Wolf Optimizer (GWO) 17 , 25 , 28 – 30 Butterfly Optimization Algorithm (BOA) 31 , Ant Lion Optimizer (ALO) 17 , Support Vector Machine (SVM) 18 , 32 , 33 , Response Surface Methodology (RSM) 34 , 35 , Non-Dominated Sorting Genetic Algorithm (NSGA) 36 and their hybrid.…”

“…Hybridization is now the most widely used technology for analyzing a plant’s performance precisely as it combines two algorithms and tools into one and allows them to work synergistically 1 , 2 , 6 . Literature suggests various hybrid models, such as GA-ANN 25 , 38 , PSO-ANN 21 , ABC-ANN 16 , SVM-ANN 33 , PSO-SVM 32 , WOA-ANN 39 , and others, have focused on system model effectiveness in the investigation of the various fields of engineering and in assessing plant’s performance. Among them, ANN with hybrid models is the most extensively utilized technology to investigate plant datasets correctly 5 , 6 .…”