A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms

This article presents the results of a study on the oestrogenicity and androgenicity of urban wastewater in Lodz, and the possibility of their removal by the Group Wastewater Treatment Plant (GWWTP). Wastewater samples were taken at five points of the sewer system in the city and at the inlet and outlet of the GWWTP. The study was conducted using Yeast Oestrogen Screen (YES)/Yeast Androgen Screen (YAS) tests, which allow a general assessment of the content of compounds with (anti-)oestrogenic and (anti-)androgenic effects in wastewater, without identifying specific substances. Wastewater samples taken from the sewage network did not show (anti-)oestrogenic activity, while oestrogenic and antagonistic properties to androgens were detected in most of them. In the influent of the treatment plant, oestrogen agonistic activity was detected only in one sample (oestrogen equivalent—EEQ equal to 1.31 × 105 ng 17 β–oestradiol/L) and was 100% removed. The purification efficiencies in GWWTP for oestrogen and androgen antagonistic activity were 51.5–99.2% and 39.4–47.1%, respectively. Although no oestrogenic activity was detected in general wastewater in Lodz, observed high-antagonistic–androgenic activities may adversely affect the water body and cause, among others, the feminization of fish, especially in the case of discharge of untreated wastewater by combined sewer overflows.

Section: Resultssupporting

confidence: 77%

Analysis of (Anti-)Oestrogenic and (Anti-)Androgenic Activities in Wastewater from the Lodz Sewer System

Brzezińska

Sakson

Olejnik

2023

“…According to previous studies [33][34][35][36][37], a dynamic kernel extreme learning machine was proposed, including 170 samples and eight variables, to predict the COD proportion of industrial wastewater, and achieved a 10-fold cross-validation R 2 of 0.708 [38]. Alavi et al [39] proposed a novel computing algorithm that integrates an intelligent optimization algorithm with a KELM for the prediction of inlet COD concentrations in WWTPs. This study also compared the performance of different algorithms for optimizing real-time COD prediction.…”

Section: Introductionmentioning

confidence: 99%

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

Zhang,

Guo,

Luo

et al. 2024

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

“…As a result, most WWTPs are equipped with many redundant hardware facilities to ensure plant safety. Thus, timely monitoring and fault diagnosis of effluent water quality have become crucial issues, with significant research outcomes [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Wastewater Treatment Plants Based on an Improved Kernel Extreme Learning Machine Method

Zhou

Zhang

Jing

et al. 2023

In order to ensure the stable operation, improve efficiency, and enhance sustainability of wastewater treatment systems, this paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method. Firstly, a kernel extreme learning machine (KELM) model optimized by an improved mutation bald eagle search (IMBES) optimizer is proposed to generate point predictions of effluent quality parameters. Then, based on the point prediction results, the confidence interval of effluent quality parameters is calculated using kernel density estimation (KDE) method. This interval represents the bounds of system uncertainty and unknown disturbance at normal conditions and can be treated as the threshold for fault diagnosis. Finally, the effectiveness of the proposed method is illustrated by two datasets obtained from the BSM1 wastewater simulation platform and an actual water platform. Experimental results show that compared with other methods such as CNN, LSTM, and IBES-LSSVM, this method has a significant improvement in prediction accuracy, and at the same confidence level, it ensures fault detection rate while generating smaller confidence intervals.