Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application

A Deep Learning Algorithm for Multi-Source Data Fusion to Predict Effluent Quality of Wastewater Treatment Plant

The operational complexity of wastewater treatment systems mainly stems from the diversity of influent characteristics and the nonlinear nature of the treatment process. Together, these factors make the control of effluent quality in wastewater treatment plants (WWTPs) difficult to manage effectively. To address this challenge, constructing accurate effluent quality models for WWTPs can not only mitigate these complexities, but also provide critical decision support for operational management. In this research, we introduce a deep learning method that fuses multi-source data. This method utilises various indicators to comprehensively analyse and predict the quality of effluent water: water quantity data, process data, energy consumption data, and water quality data. To assess the efficacy of this method, a case study was carried out at an industrial effluent treatment plant (IETP) in Anhui Province, China. Deep learning algorithms including long short-term memory (LSTM) and gated recurrent unit (GRU) were found to have a favourable prediction performance by comparing with traditional machine learning algorithms (random forest, RF) and multi-layer perceptron (MLP). The results show that the R2 of LSTM and GRU is 1.36%~31.82% higher than that of MLP and 9.10%~47.75% higher than that of traditional machine learning algorithms. Finally, the RReliefF approach was used to identify the key parameters affecting the water quality behaviour of IETP effluent, and it was found that, by optimising the multi-source feature structure, not only the monitoring and management strategies can be optimised, but also the modelling efficiency of the model can be further improved.

Fit-for-purpose WWTP unmanned aerial systems: A game changer towards an integrated and sustainable management strategy

In the ongoing Anthropocene era, air quality monitoring constitutes a primary axis of European and international policies for all sectors, including Waste Water Treatment Plants (WWTPs). Unmanned Aerial Systems (UASs) with proper sensing equipment provide an edge technology for air quality and odor monitoring. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry has been used in civil engineering, environmental (water) quality assessment and lately for industrial facilities monitoring. This study constitutes a systematic review of the late advances and limitations of germane equipment and implementations. Despite their unassailable flexibility and efficiency, the employment of the aforementioned technologies in WWTP remote monitoring is yet sparse, partial, and concerns only particular aspects. The main finding of the review was the lack of a tailored UAS for WWTP monitoring in the literature. Therefore, to fill in this gap, we propose a fit-for-purpose remote monitoring system consisting of a UAS with a platform that would integrate all the required sensors for air quality (i.e., emissions of H2S, NH3, NOx, SO2, CH4, CO, CO2, VOCs, and PM) and odor monitoring, multispectral and thermal cameras for photogrammetric structural health monitoring (SHM) and wastewater/effluent properties (e.g., color, temperature, etc.) of a WWTP. It constitutes a novel, supreme and integrated approach to improve the sustainable management of WWTPs. Specifically, the developments that a fit-for-purpose WWTP UAS would launch, are fostering the decision-making of managers, administrations, and policymakers, both in operational conditions and in case of failures, accidents or natural disasters. Furthermore, it would significantly reduce the operational expenditure of a WWTP, ensuring personnel and population health standards, and local area sustainability.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

Industrial Process Monitoring Based on Dynamic Overcomplete Broad Learning Network

Most industrial processes feature high nonlinearity, non-Gaussianity, and time correlation. Models based on overcomplete broad learning system (OBLS) have been successfully applied in the fault monitoring realm, which may relatively deal with the nonlinear and non-Gaussian characteristics. However, these models barely take time correlation into full consideration, hindering the further improvement of the monitoring accuracy of the network. Therefore, an effective dynamic overcomplete broad learning system (DOBLS) based on matrix extension is proposed, which extends the raw data in the batch process with the idea of "time lag" in this article. Subsequently, the OBLS monitoring network is employed to continue the analysis of the extended dynamic input data. Finally, a monitoring model is established to tackle the coexistence of nonlinearity, non-Gaussianity, and time correlation in process data. To illustrate the superiority and feasibility, the proposed model is conducted on the penicillin fermentation simulation platform, the experimental result of which illustrates that the model can extract the feature of process data more comprehensively and be self-updated more efficiently. With shorter training time and higher monitoring accuracy, the proposed model can witness an improvement of average monitoring accuracy by 3.69% and 1.26% in 26 process fault types compared to the state-of-the-art fault monitoring methods BLS and OBLS, respectively.

Development of a Soft Sensor Using Machine Learning Algorithms for Predicting the Water Quality of an Onsite Wastewater Treatment System

Developing advanced onsite wastewater treatment systems (OWTS) requires accurate and consistent water quality monitoring to evaluate treatment efficiency and ensure regulatory compliance. However, off-line parameters such as chemical oxygen demand (COD), total suspended solids (TSS), and Escherichia coli (E. coli) require sample collection and time-consuming laboratory analyses that do not provide real-time information of system performance or component failure. While real-time COD analyzers have emerged in recent years, they are not economically viable for onsite systems due to cost and chemical consumables. This study aimed to design and implement a real-time remote monitoring system for OWTS by developing several multi-input and single-output soft sensors. The soft sensor integrates data that can be obtained from well-established in-line sensors to accurately predict key water quality parameters, including COD, TSS, and E. coli concentrations. The temporal and spatial water quality data of an existing field-tested OWTS operated for almost two years (n = 56 data points) were used to evaluate the prediction performance of four machine learning algorithms. These algorithms, namely, partial least square regression (PLS), support vector regression (SVR), cubist regression (CUB), and quantile regression neural network (QRNN), were chosen as candidate algorithms for their prior application and effectiveness in wastewater treatment predictions. Water quality parameters that can be measured in-line, including turbidity, color, pH, NH4+, NO3-, and electrical conductivity, were selected as model inputs for predicting COD, TSS, and E. coli. The results revealed that the trained SVR model provided a statistically significant prediction for COD with a mean absolute percentage error (MAPE) of 14.5% and R2 of 0.96. The CUB model provided the optimal predictive performance for TSS, with a MAPE of 24.8% and R2 of 0.99. None of the models were able to achieve optimal prediction results for E. coli; however, the CUB model performed the best with a MAPE of 71.4% and R2 of 0.22. Given the large fluctuation in the concentrations of COD, TSS, and E. coli within the OWTS wastewater dataset, the proposed soft sensor models adequately predicted COD and TSS, while E. coli prediction was comparatively less accurate and requires further improvement. These results indicate that although water quality datasets for the OWTS are relatively small, machine learning-based soft sensors can provide useful predictive estimates of off-line parameters and provide real-time monitoring capabilities that can be used to make adjustments to OWTS operations.

Online rapid total nitrogen detection method based on UV spectrum and spatial interval permutation combination population analysis

Rapidly and accurately detect the total nitrogen (TN) concentration is enormously important for surface water protection considering the critical role it plays in reflecting the eutrophication of surface water. However, traditional TN detection methods have to experience a tedious oxygen digestion process, which tremendously limits the detection speed of TN. To solve this problem, we propose a novel online rapid TN detection method. The transformations of nitrogenous substances during the oxidative digestion process are observed by using ultraviolet (UV) spectroscopy and the concentration of TN can be predicted by only using the variation of spectrum in the early oxygen digestion process. To select the most informative variables hidden in the collected three-dimension spectrum, a new wavelength selection algorithm called spatial interval permutation combination population analysis (siPCPA) is proposed, which considers the spatial-temporal relationships among each variable in the spectrum. By using the real surface water samples collected from Houhu Lake, Changsha, China, the effectiveness of our proposed new detection and selection methods are verified and compared with other state-of-the-art methods. As a result, the practical application experiment shows that our methods can determine the concentration of TN in 5 min with a relative error of less than 5%.