Search Results (11)

We propose an adaptive multi-timescale particle filter (AMTS-PF) for nonlinear state estimation in wastewater treatment plants (WWTPs) to address multi-scale temporal dynamics. The AMTS-PF decouples the problem into minute-level state updates and hour-level parameter refinements, integrating adaptive noise tuning, multi-scale entropy-driven feature extraction, and dual-timescale particle weighting. It dynamically adjusts noise covariances via Bayesian fusion and uses wavelet-based entropy analysis for adaptive resampling. The method interfaces seamlessly with existing WWTP control systems, providing real-time state estimates and refined parameters. Implemented on a heterogeneous computing architecture, it combines edge-level parallelism and cloud-based inference. Experimental validation shows superior performance over extended Kalman filters and single-timescale particle filters in handling nonlinearities and time-varying dynamics. The proposed AMTS-PF significantly enhances the accuracy of state estimation in WWTPs compared to traditional methods. Specifically, during the 14-day evaluation period using the Benchmark Simulation Model No. 1 (BSM1), the AMTS-PF achieved a root mean square error (RMSE) of 54.3 mg/L for heterotroph biomass (XH) estimation, which is a 37% reduction compared to the standard particle filter (PF) with an RMSE of 68.9 mg/L. For readily biodegradable substrate (Ss) and particulate products (Xp), the AMTS-PF also demonstrated superior performance with RMSE values of 7.2 mg/L and 9.8 mg/L, respectively, representing improvements of 24% and 21% over the PF. In terms of slow parameters, the AMTS-PF showed a 37% reduction in RMSE for the maximum heterotrophic growth rate (μH) estimation compared to the PF. These results highlight the effectiveness of the AMTS-PF in handling the multi-scale temporal dynamics and nonlinearities inherent in WWTPs. This work advances the state-of-the-art in WWTP monitoring by unifying multi-scale temporal modeling with adaptive Bayesian estimation, offering a practical solution for improving operational efficiency and process reliability. Full article

Accurate monitoring of complex industrial plants is crucial for ensuring safe operations and reliable management of desired quality. Early detection of abnormal events is essential to preempt serious consequences, enhance system performance, and reduce manufacturing costs. In this work, we propose a novel methodology for fault detection based on Slow Feature Analysis (SFA) tailored for time series models and statistical process control. Fault detection is critical in process monitoring and can ensure that systems operate efficiently and safely. This study investigates the effectiveness of various multivariate statistical methods, including Slow Feature Analysis (SFA), Kernel Slow Feature Analysis (KSFA), Dynamic Slow Feature Analysis (DSFA), and Principal Component Analysis (PCA) in detecting faults within the Tennessee Eastman (TE), Benchmark Simulation Model No. 1 (BSM 1) datasets and Beijing wastewater treatment plant (real world). Our comprehensive analysis indicates that KSFA and DSFA significantly outperform traditional methods by providing enhanced sensitivity and fault detection capabilities, particularly in complex, nonlinear, and dynamic data environments. The comparative analysis underscores the superior performance of KSFA and DSFA in capturing comprehensive process behavior, making them robust, cutting-edge choices for advanced fault detection applications. Such methodologies promise substantial improvements in industrial plant monitoring, contributing to heightened system reliability, safety, and overall operational efficiency. Full article

Assessing the quality of a hospital wastewater treatment process and plant is essential, especially if the presence of chemical and biological toxic compounds is considered. There is less literature on hospital wastewater treatment in developing countries because of a lack of managerial awareness and stakeholder cooperation, accompanied by the limited capacity of investment meant to upgrade the existing infrastructures. Limited access to data further hampers the reliable analysis of hospital wastewater treatment plants (WWTPs) in developing countries. Thus, based on the possibility of collecting a sufficient amount of primary (i.e., field) data, this study performed an assessment of the potential impacts generated by the WWTP of Quaid-Azam International Hospital in Islamabad (Pakistan) considering its construction and operational phases. The major identified impacts were attributed to the energy mix used to operate the plant. Marine ecotoxicity was the most impactful category (34% of the total potential impacts accounted for), followed by human carcinogenic toxicity (31%), freshwater toxicity (18%), terrestrial ecotoxicity (7%), and human non-carcinogenic toxicity (4%). An analysis of potential impacts was combined with an assessment of potential damage according to an endpoint approach. In particular, the endpoint analysis results indicated that human health damage (quantified as DALY) was mainly dependent on the “fine PM (particulate matter) formation” category (51%), followed by “global warming and human health” (43%). Other categories related to human health impacts were human carcinogenic toxicity (3%), water consumption (2%), and human non-carcinogenic toxicity (1%). The other impact categories recorded a percentage contribution lower than 1%. With respect to ecosystem damage, “global warming and terrestrial ecosystems” played a major role (61%), followed by terrestrial acidification (24%), ozone formation (10%), water consumption (5%), and freshwater eutrophication (1%). This study’s findings support an increase in awareness in the hospital management board while pointing out the need to further implement similar studies to improve the quality of decision-making processes and to mitigate environmental impacts in more vulnerable regions. Finally, this research evidenced the need to overcome the existing general constraints on data availability. Consequently, further field work, supported by hospital managers in developing countries, would help in enhancing managerial procedures; optimizing treatment plant efficiency; and facilitating the implementation of circular options, such as sludge management, that often remain unexplored. Full article

The objective of this study was to model the operation of a vertical-flow constructed wetland (VF-CW) for domestic wastewater, using Monte-Carlo simulations and selected probability distributions of various random variables. The analysis was based on collected wastewater quality data, including the values of the pollutant indicators BOD₅ (biochemical oxygen demand), COD_Cr (chemical oxygen demand), and TSS (total suspended solids), in the 2017–2020 period. Anderson–Darling (A–D) statistics were applied to assess the fit of the theoretical distributions to the empirical distributions of the random variables under study. The selection of the best-fitting statistical distributions was determined using the percentage deviation (PBIAS) criterion. Based on the analyses that were performed, the best-fitting statistical distributions for the pollution indicators of the raw wastewater were the generalised extreme value distribution for BOD₅, the Gaussian distribution for COD_Cr, and the log-normal distribution for TSS. For treated effluent, the log-normal distribution was the best fit for BOD₅ and COD_Cr; the semi-normal distribution, for TSS. The new data generated using the Monte-Carlo method allowed the reliability of the VF-CW operation to be assessed by determining the reliability indices, i.e., the average efficiency of the removal of pollutants (η), the technological efficiency index (R), the reliability index (CR), and the risk index of the negative control of the sewage treatment plant operation (Re). The obtained results indicate that only in the case of COD_Cr, the analysed treatment facility may fail to meet the requirements related to the reduction of organic pollutants to the required level, which is evidenced by the values of the indicators CR = 1.10, R = 0.49, and η = 0.82. In addition, the risk index of the negative operation of the facility (Re) assumes a value of 1, which indicates that during the period of its operation, the VF-CW system will not operate with the required efficiency in relation to this indicator. The novelty of this work is the implementation of the indicated mathematical simulation methods for analysing the reliability of the operation of the domestic wastewater treatment facility. Full article

Reliable and continuous operation of the equipment is expected in the wastewater treatment plant, as any perturbations can lead to environmental pollution and the need to pay penalties. Optimization and minimization of operating costs of the pump station cannot, therefore, lead to a reduction in reliability but rather should be based on preventive works, the necessity of which should be foreseen. The purpose of this paper is to develop an accurate model to predict a pump’s mean time to failure, allowing for rational planning of maintenance. The pumps operate under the supervision of the automatic control system and SCADA, which is the source of historical data on pump operation parameters. This enables the research and development of various methods and algorithms for optimizing service activities. In this case, a multiple linear regression model is developed to describe the impact of historical data on pump operation for pump maintenance. In the literature, the least squares method is used to estimate unknown regression coefficients for this data. The original value of the paper is the application of the genetic algorithm to estimate coefficient values of the multiple linear regression model of failure-free time of the pump. Necessary analysis and simulations are performed on the data collected for submersible pumps in a sewage pumping station. As a result, an improvement in the adequacy of the presented model was identified. Full article

Algae-based wastewater treatment technologies are promising green technologies with huge economical potential and environmental co-benefits. However, despite the immense research, work, and achievement, no publications were found wherein these technologies have been successfully applied in an operational environment for nitrogen and phosphorus removal of secondary treated effluent in municipal wastewater treatment plants. Based on a literature review and targeted comprehensive analysis, the paper seeks to identify the main reasons for this. The reliability (considering inlet wastewater quality variations, operating conditions and process control, algae harvesting method, and produced biomass) as well as the technology readiness level for five types of reactors are discussed. The review shows that the reactors with a higher level of control over the technological parameters are more reliable but algal post-treatment harvesting and additional costs are barriers for their deployment. The least reliable systems continue to be attractive for research due to the non-complex operation and relieved expenditure costs. The rotating biofilm systems are currently undertaking serious development due to their promising features. Among the remaining research gaps and challenges for all the reactor types are the identification of the optimal algal strains, establishment of technological parameters, overcoming seasonal variations in the effluent’s quality, and biomass harvesting. Full article

Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are emerging as a viable alternative for real-time monitoring of parameters that either lack a reliable measuring principle or are measured using expensive online sensors. This paper presents the development, implementation, and validation of a hybrid soft sensor used to estimate Total Phosphorus (TP) and Chemical Oxygen Demand (COD) in the influent and effluent streams of a full-scale WWTP. A systematic method for cleaning and processing sensor data, identifying statistically significant correlations, and developing a mathematical model, is discussed. A non-intrusive Industrial Internet of Things (IIoT) infrastructure for soft-sensor deployment and a web-based GUI for data visualization are also presented in this work. The values of TP and COD estimated by the soft sensor are validated by comparing the estimated values to the daily average of their corresponding lab measurements. The data validation results demonstrate the potential of soft sensors in providing real-time values of essential wastewater quality parameters with an acceptable degree of accuracy. Full article

Methane is a major greenhouse gas and a precursor of tropospheric ozone, and most of its sources are linked to anthropogenic activities. The sources of methane are well known and its monitoring generally involves the use of expensive gas analyzers with high operating costs. Many studies have investigated the use of low-cost gas sensors as an alternative for measuring methane concentrations; however, it is still an area that needs further development to ensure reliable measurements. In this work a low-cost platform for measuring methane within a low concentration range was developed and used in two distinct environments to continuously assess and improve its performance. The methane sensor was the Figaro TGS2600, a metal oxide semiconductor (MOS) based on tin dioxide (SnO₂). In a first stage, the monitoring platform was applied in a small ruminant barn after undergoing a multi-point calibration. In a second stage, the system was used in a wastewater treatment plant together with a multi-gas analyzer (Gasera One Pulse). The calibration of low-cost sensor was based on the relation of the readings of the two devices. Temperature and relative humidity were also measured to perform corrections to minimize the effects of these variables on the sensor signal and an active ventilation system was used to improve the performance of the sensor. The system proved to be able to measure low methane concentrations following reliable spatial and temporal patterns in both places. A very similar behavior between both measuring systems was also well noticeable at WWTP. In general, the low-cost system presented good performance under several environmental conditions, showing itself to be a good alternative, at least as a screening monitoring system. Full article

Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%. Full article

The paper presents modelling of wastewater treatment plant (WWTP) operation work efficiency using a two-stage method based on selected probability distributions and the Monte Carlo method. Calculations were carried out in terms of sewage susceptibility to biodegradability. Pollutant indicators in raw sewage and in sewage after mechanical treatment and biological treatment were analysed: BOD₅, COD, total suspended solids (TSS), total nitrogen (TN) and total phosphorus (TP). The compatibility of theoretical and empirical distributions was assessed using the Anderson–Darling test. The best-fitted statistical distributions were selected using Akaike criterion. Performed calculations made it possible to state that out of all proposed methods, the Gaussian mixture model (GMM) for distribution proved to be the best-fitted. Obtained simulation results proved that the statistical tools used in this paper describe the changes of pollutant indicators correctly. The calculations allowed us to state that the proposed calculation method can be an effective tool for predicting the course of subsequent sewage treatment stages. Modelling results can be used to make a reliable assessment of sewage susceptibility to biodegradability expressed by the BOD₅/COD, BOD₅/TN and BOD₅/TP ratios. New data generated this way can be helpful for the assessment of WWTP operation work and for preparing different possible scenarios for their operation. Full article

The aim of the present work was the modeling of the wastewater treatment plant operation work using Monte Carlo method and different random variables probability distributions modeling. The analysis includes the following pollutants indicators; BOD₅ (Biochemical Oxygen Demand), COD_Cr (Chemical Oxygen Demand), Total Suspended Solids (SS_t), Total Nitrogen (TN), and Total Phosphorus (TP). The Anderson–Darling (A–D) test was used for the assessment of theoretical and empirical distributions compatibility. The selection of the best-fitting statistical distributions was performed using peak-weighted root mean square (PWRMSE) parameter. Based on the performed calculations, it was stated that pollutants indicators in treated sewage were characterized by a significant variability. Obtained results indicate that the best-fitting pollutants indicators statistical distribution is Gauss Mixed Model (GMM) function. The results of the Monte Carlo simulation method confirmed that some problems related to the organic and biogenic pollutants reduction may be observed in the Wastewater Treatment Plant, in Jaworzno. Full article