Operation Of Treatment Plants Research Articles

Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant

This paper presents the use of a machine learning approach to predict the performance of a Gaza wastewater treatment plant.

Operators Need to Know the History of US Water Quality Regulation and Analysis: Part 2

Water treatment plant operators need to understand federal and state laws as well as the standards that apply to water treatment systems. They should also understand how drinking water regulations are administered and why compliance is essential to providing safe drinking water to the public.

The wastewater treatment plant operation estimation through the use of the water quality index – the case study of WWTP-Montana, Bulgaria

Wastewater treatment plants (WWTPs) are designed to treat the used water by improving its quality, so it is no longer harmful to the environment when discharged. The long-term mandatory monitoring of wastewater produces large amounts of data. It can be converted to a unitless number – the Water Quality Index (WQI) and used to assess the wastewater quality by checking compliance with the set regulations. It has gained increasing popularity among decision-makers, wastewater professionals, and environmental agencies. Operation assessment of the WWTP-Montana for a period of 12 years (2011-2022) was performed using the Canadian Council of Ministers Water Quality Index (CCME WQI) calculation for the raw water (influent) and the treated water (effluent) of the WWTP and applying time series analysis of CCME WQI and water quality indicators – chemical oxygen demand (COD), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) in the influent. For the entire period, the calculated CCME WQI for the treated waters shows the perfect score of 100 (excellent water quality). The calculated CCME WQI at the inlet, on the other hand, classifies the raw water’s quality as “poor” (64% of the CCME WQI values), “marginal” (32%) and “fair” (4%). The time series analysis reveal that higher water quality of the inlet wastewater is detected in summer (August) due to the lower concentrations of the five mandatory physicochemical indicators.

Quaternary ammonia compounds in disinfectant products: evaluating the potential for promoting antibiotic resistance and disrupting wastewater treatment plant performance

Quaternary ammonium compounds (QACs) are a class of compounds that were widely used as disinfectants during the COVID-19 pandemic and continue to be used as disinfecting agents.

Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study

This paper proposes a novel targeted blend of machine learning (ML) based approaches for controlling wastewater treatment plant (WWTP) operation by predicting distributions of key effluent parameters of a biological nutrient removal (BNR) process. Two years of data were collected from Plajyolu wastewater treatment plant in Kocaeli, Türkiye and the effluent parameters were predicted using six machine learning algorithms to compare their performances. Based on mean absolute percentage error (MAPE) metric only, support vector regression machine (SVRM) with linear kernel method showed a good agreement for COD and BOD5, with the MAPE values of about 9% and 0.9%, respectively. Random Forest (RF) and EXtreme Gradient Boosting (XGBoost) regression were found to be the best algorithms for TN and TP effluent parameters, with the MAPE values of about 34% and 27%, respectively. Further, when the results were evaluated together according to all the performance metrics, RF, SVRM (with both linear kernel and RBF kernel), and Hybrid Regression algorithms generally made more successful predictions than Light GBM and XGBoost algorithms for all the parameters. Through this case study we demonstrated selective application of ML algorithms can be used to predict different effluent parameters more effectively. Wider implementation of this approach can potentially reduce the resource demands for active monitoring the environmental performance of WWTPs.

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant

Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making support to facilitate their operations and management. In this study, we developed a novel hybrid deep learning model by combining the temporal convolutional network (TCN) model with the long short-term memory (LSTM) network model to improve the simulation of hourly total nitrogen (TN) concentration in WWTP effluent. The developed model was tested in a WWTP in Jiangsu Province, China, where the prediction results of the hybrid TCN-LSTM model were compared with those of single deep learning models (TCN and LSTM) and traditional machine learning model (feedforward neural network, FFNN). The hybrid TCN-LSTM model could achieve 33.1 % higher accuracy as compared to the single TCN or LSTM model, and its performance could improve by 63.6 % comparing to the traditional FFNN model. The developed hybrid model also exhibited a higher power prediction of WWTP effluent TN for the next multiple time steps within eight hours, as compared to the standalone TCN, LSTM, and FFNN models. Finally, employing model interpretation approach of Shapley additive explanation to identify the key parameters influencing the behavior of WWTP effluent water quality, it was found that removing variables that did not contribute to the model output could further improve modeling efficiency while optimizing monitoring and management strategies.

Increase metabolic heat to compensate for low temperature in activated sludge systems

The efficient operation of activated sludge systems is frequently hindered by low temperatures, and extensive research has been conducted to overcome this difficulty. However, the effect of varying temperatures on heat generation during substrate degradation remains unclear. In this study, results from laboratory-scale reactors show that sludge generated 5.36 ± 0.58 J/mg COD, 4.45 ± 0.24 J/mg COD, and 4.22 ± 0.26 J/mg COD at 10 °C, 20 °C, and 30 °C under aerobic conditions, respectively. Similarly, the sludge generated 4.05 ± 0.31 J/mg COD, 2.37 ± 0.15 J/mg COD, and 2.89 ± 0.18 J/mg COD under anoxic conditions. Despite the decreased respiration rates and hence reduced pollutant removal efficiency, sludge exhibited effective heat generation at low temperatures. Results from the full-scale plant also show a negative correlation between the heat generation capacity of microorganisms and the temperatures. 14.2 °C is considered the critical wastewater temperature for microorganisms' heat generation to offset the investigated plant's heat dissipation. This observation verified that thermal compensation for low temperatures was also significant in the full-scale plant. The mechanism of low-temperature compensation is attributed to non-growth processes being less dependent on temperature than growth processes, resulting in slow microbial growth but high heat generation at low temperatures. These findings provide valuable insights into the design and sustainable operation of wastewater treatment plants.

Minimization of residues generated during the operation of treatment facilities

The activity of sewage treatment plants for the treatment of domestic wastewater is not limited only to wastewater treatment. The main problem in this activity is the treatment and disposal of the resulting sediments at sewage treatment plants, where the main task is to reduce the volume of sediment and then transform it into a useful product that does not cause environmental pollution. Currently, representatives of landfills refuse to accept this type of precipitation, explaining that this type of precipitation does not belong to the category of solid municipal waste. The issue of using sediments from sewage treatment plants as raw materials for manufacturing remains open, but in our opinion this type of sediment can be recycled in wastewater treatment technology. The practical value lies in conducting industrial tests of the converter of sewage sludge into liquid effluents, including all waste generated during the operation of sewage treatment plants with subsequent post-treatment of liquid effluents at wastewater treatment plants. These tests will reduce the amount of sediment by up to 90%, which will minimize the intensive accumulation of precipitation from sewage treatment plants and improve the state of the environment.

Implementation of an activated sludge model to simulate Thriasio Wastewater Treatment Plant operation in West Attica, Greece

Implementation of an activated sludge model to simulate Thriasio Wastewater Treatment Plant operation in West Attica, Greece

Phycoremediation - A Clean Technology for Water Pollution Abatement

Phycoremediation has been utilised to remediate wastewater and successfully lower nutrient levels. There isn’t many research, though, on how well phycoremediation works to lower nitrogen levels in eutrophic lakes. This review concentrates on the process and variables involved in using algae. Before considering the possibility of algal-based approaches in remediating lake eutrophication, to remove nutrients and contaminants from wastewater. Algal biofilm, algal grass scrubbers, high-rate algal ponds (HRAP), and immobilised algae are methods used in phycoremediation of wastewater. A wastewater treatment method based on microalgae lowers BOD, prevents coli forms, eliminates pollutants and nutrients, and removes heavy metals. The sixth sustainable development target of the UNDP cannot be attained without phytoremediation. Microalgae can effectively treat wastewater at a very low cost, and the biomass they produce can be used to produce biofuels. The cost-effective growing of microalgae and contaminants with wastewater can be replaced by the successful coupling of microalgae with wastewater, which can also scale up production of high-value products. This review mainly focused on the potential of algae and their specific mechanisms involved in wastewater treatment and energy recovery systems leading to important industrial precursors. The review is highly beneficial for scientists, wastewater treatment plant operators, freshwater managers, and industrial communities to support the sustainable development of natural resources. Keywords: Algae, Biodegradation, Pollutant removal, Phycoremediation, Wastewater treatment.

Studying the impact of monitoring strategy on disinfection performance evaluation with computational fluid dynamics

ABSTRACT Disinfection is a critical process component of drinking water treatment to protect human health. The disinfection performance is highly dependent on the hydraulic efficiency of the disinfection contactor. Accurately evaluating the contactor hydraulic efficiency is important to water treatment plant designers and operators. Tracer analysis is the most used approach for evaluating hydraulic efficiency. Many factors that can impact tracer analysis have been well studied, while the monitoring strategy is not. This study investigates the impact of different monitoring strategies, including single-point, multiple-point, and surface-average monitoring, for hydraulic efficiency evaluation. It is found that: residence time distribution and characteristic times could vary significantly when monitoring two points near to each other, especially at a location with an uneven velocity distribution; the multi-point matrix monitoring approaches proposed in this study could be an alternative to accurately evaluate the contactor hydraulic performance even at the places with an uneven velocity distribution.

Review of climate change and drinking water supply systems: employee perspectives and potential tools for adaptation

Climate change (CC) causes extreme meteorological events such as floods, droughts, water scarcity, heat waves, wildfires, and extreme cold. CC may also damage public infrastructures such as drinking water systems (DWSs) and reduce water quality from source to tap. The objective of this review paper is to provide a critical analysis of (1) the impact of CC on the three components of the DWSs: source, treatment, and distribution; (2) the perspectives of DWS employees on CC impacts and adaptation solutions for maintaining water quality; and (3) decision support systems (DSSs) that could be used by DWS employees for CC adaptation. A bibliographic research was conducted using international databases and search engines. The search for scientific articles resulted in the selection of 5234 articles, 78 of which were analyzed in more detail for the elaboration of this scoping review. This review shows that the impacts of CC on water quality are significant, but DWS employees are not enthusiastic about developing adaption measures. This review also shows that DSSs can help DWS employees facilitate the CC adaptation process for the protection of source water, improve water treatment plant operations, and enhance routine management of water quality in the distribution system. Further research is needed to identify strategies that might increase the awareness of and interest in climate change impacts, adaptation, and the use of DSSs among DWS employees.

Impact of Corona virus pandemic on wastewater characteristics, treatment, and water reuse in a municipal plant

The COVID-19 pandemic is considered one of the most significant threats to health. The effect of the pandemic on performance of wastewater treatment plants has not received much attention in the literature. This study assessed the impact of Coronavirus pandemic on the wastewater quantity, characteristics, treatment processes, and water reuse in Kuwait. It focused on three municipal wastewater treatment plants (WWTPs) with different design capacities, namely Sulaibiya (600,000 m3/d), Kabd (180,000 m3/d), and Umm Al-Hayman (27,000 m3/d), all using tertiary treatment. Daily data records were obtained on influent and effluent over the years 2018–2020 (before and during the pandemic). In addition, influent and effluent samples were collected from the Sulaibiya and Kabd plants for laboratory analysis to determine the effect of increase in the use of home disinfectants. The results indicate an increase (<50%) in the average daily flow rates received in WWTPs during the 2020 home quarantine lockdowns. Statistical analysis showed stability of the Sulaibiya in BOD5, COD, and TSS removal efficiency while a decrease in the Kabd and UAH plants removal efficiency was observed during the pandemic. During the lockdown period, GC-MS analysis showed a high probability of chloroxylenol (>90), one of the main components of Dettol disinfectant, existing in the raw wastewater samples while the GC-FID analysis indicated the presence of (PAH) as emerging contaminants, at higher concentrations in the influent and effluent samples. The pandemic adversely affected wastewater treatment plant performance and excessive use of Dettol disinfectant by homes resulted in the presence of objectionable organic pollutants in the tertiary-treated effluent which would impair water reuse. Reverse osmosis process used in Sulaibiya plant proved to be effective in removing residual organics and improving effluent quality for reuse during the pandemic.

Subsurface Bioreactors as a Possible Addition to Enhance WW Treatment Plant Operation: A Brief Review

Prior to the establishment of the United Sates Environmental Protection Agency and the Clean Water Act signed into law in 1972, water treatment was done on a minimal level or was not present at all. Since then, scientists strive to improve and advance new and existing water treatment processes that water can be discharged safely into water bodies.
 Constructed wetlands as engineered waste water treatment systems are used today as a final waste water treatment before the treated waste water is released into a water body.
 Surface flow and sub terranean flow constructed wetland options exist and are used for the treatment of municipal, agricultural and industrial waste water containing pollutants such as human waste, soap, fats, chemicals, and pharmaceutical compounds.
 They can be operated all year round (24/7) at temperatures that might exceed 35°C (95°F) and as low as around -26°C (-15°F).
 Constructed wetland systems in general depend on the quality of pretreatment for the inflow waste water, the hydrology and the carrier material as well as the climate, the vegetation and the physical and technical properties of the plant-based sewage treatment plants themselves, as well as the operational principle applied to the constructed wetland system.
 Constructed wetland operated as surface and or sub terranean flow mode can be a cost-effective and low-energy consuming alternative that requires only minimal operational effort. Experience shows that sub-terranean flow surface constructed wetland can be a long-term cost-effective solution that operates stable in various hot and cold weather conditions for decades with minimal operational and maintenance effort.

Comprehensive study on the potential environmental risk of temporal antibiotic usage through wastewater discharges

Antibiotic residues can reach aquatic ecosystems through urban wastewater discharges, posing an ecotoxicological risk for aquatic organisms and favoring the development of bacterial resistance. To assess the emission rate and hazardousness of these compounds, it is important to carry out periodic chemical monitoring campaigns that provide information regarding the actual performance of wastewater treatment plants (WWTPs) and the potential impact of the treated wastewater in the aquatic environment. In this study, 18 of the most widely consumed antibiotics in Spain were determined by liquid chromatography-tandem mass spectrometry in both influent (IWW) and effluent wastewater (EWW) samples collected over four seasons along 2021–2022. Eleven antibiotics were detected in EWW with azithromycin, ciprofloxacin and levofloxacin showing the highest concentration levels (around 2 μg L−1 of azithromycin and 0.4 μg L−1 of quinolone compounds). Data showed that only 4 out of the 11 compounds were removed by more than 50 % in the WWTP, with sulfamethoxazole standing out with an average removal efficiency >80 %. The risk that treated water could pose to the aquatic environment was also assessed, with 6 compounds indicating a potential environmental risk by exceeding established ecotoxicological and resistance thresholds. Based on the risk assessment, the WWTP removal efficiency required to reduce such risk for antibiotics was estimated. In addition, pooled wastewater samples were screened by LC coupled to high resolution mass spectrometry with ion mobility separation, searching for metabolites and transformation products of the antibiotics investigated to widen future research. Studies like this are crucial to map the impact of antibiotic pollution and to provide the basis for designing water quality and risk prevention monitoring programs.

Data-driven analysis and prediction of wastewater treatment plant performance: Insights and forecasting for sustainable operations

This study presents a comprehensive performance and forecasting analysis of the As-Samra wastewater treatment plant (WWTP) in Jordan, with two main objectives. Firstly, a thorough evaluation of the plant’s performance is conducted. The analysis involves independently assessing historical operational conditions, plant production, and their statistical correlations using various statistical techniques. The second objective focuses on developing a data-driven forecasting approach to predict the plant's production one month in advance, using multiple machine learning models. The results highlight the effectiveness of principal component analysis (PCA) in simplifying operational data, revealing distinct operational clusters, and identifying seasonal production patterns while showing correlations between operational conditions and overall power production. The support vector machine (SVM) forecasting model emerged as the top performer, showcasing the potential of a hybrid forecasting approach. The findings offer valuable perspectives for enhancing operational efficiency, refining production planning, and ultimately improving the environmental impact of the plant.

Performance of electro-Fenton process for the treatment of synthetic sulphidic spent caustic waste stream generated from petroleum refineries

Sulphidic spent caustic (SSC) is an alkaline waste stream which is generated during caustic scrubbing of liquefied petroleum gas and ethylene products. Due to presence of high concentrations of sulphides and phenols, the waste stream requires proper treatment before mixing with the low strength wastewater streams produced from other refinery operations. Electrochemical process is an emerging treatment method that can work efficiently at ambient conditions. The present study reports performance of electro-Fenton (EF) process for the treatment of synthetic SSC wastewater (sulphides = 10 g L−1, phenol = 2 g L−1 and pH = 12.9). The EF runs were carried out for 2 h duration in a reactor equipped with iron electrodes. The effects of H2O2 dose (0.26–1.3 M), current density (1–20 mA cm−2), pH (4.5–12.9) and stirring speed (100–1000 rpm) were investigated on removal of pollutants. The H2O2 was rapidly consumed in initial 30 min during which the significant fraction of the pollutants was degraded or removed. The optimum conditions for EF process were found to be as follows: pH = 4.5, H2O2 dose = 1.05 M, current density = 5 mA cm−2 and stirring speed = 500 rpm. At these conditions, the maximum sulphide and phenol removals from the wastewater were 98% and 91%, respectively. The results will be helpful to the wastewater treatment plant operators worldwide dealing with high concentrations of such pollutants.

Wastewater treatment plant performance assessment using time-function-based effluent quality index and multiple regression models: the case of Bahir Dar textile factory

Extensive water and chemicals are used in the textile industry processes. Therefore, treatment of textile wastewater is vital to protect the environment, maintain the public health, and recover resources. However, due to poor operation and plant performance the partially treated textile wastewater was directly discharged to a nearby river. Thus, the aim of this study was to characterize the wastewater physicochemical properties and evaluate the performance of the textile factory-activated sludge process wastewater treatment plant (WWTP) in Bahir Dar, Ethiopia. In inlet and outlet of the WWTP, samples were collected for 6 months and analyzed on-site and in a laboratory for parameters including, dissolved oxygen, pH, temperature, total Kjeldhal nitrogen (TKN), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP), nitrite, nitrate, and metallic compounds. The TSS, BOD5, COD, TP, nitrite, ammonia, and total chromium result were above the discharge limit with 73.2 mg/L, 48.45 mg/L, 144.08 mg/L, 7.9 mg/L, 1.36 mg/L, 1.96 mg/L, and 0.16 mg/L, respectively. Multiple regression models were developed for each overall, net moving average, and instantaneous effluent quality index (EQI). The predictor parameters BOD5, TN, COD, TSS, and TP (R2 = 0.995 to 1.000) estimated the net pollution loads of all predictors as 492.55 kg/day and 655.44 kg/day. Except TN, TKN, and NO3, the remaining six performance parameters were violating the permissible limit daily. Furthermore, the overall plant efficiency was predicted as 38 % and 42 % for the moving average and instantaneous EQI, respectively. Our study concluded that the integrated regression models and EQI can easily estimate the plant efficiency and daily possible pollution load.

Application of the Monte-Carlo Method to Assess the Operational Reliability of a Household-Constructed Wetland with Vertical Flow: A Case Study in Poland

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), CODCr (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 CODCr, and the log-normal distribution for TSS. For treated effluent, the log-normal distribution was the best fit for BOD5 and CODCr; 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 CODCr, 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.

Optimal Operation of Domestic and Industrial Sewage Treatment Plants Using Machine Learning Methods

Purpose: This study aims to determine the economic and technical feasibility of operating and leasing sewage treatment plants through an application that uses mathematical modeling and Machine Learning techniques for process optimization. Theoretical Framework: Efficient operation of sewage treatment plants (STPs) is crucial to ensure water quality, minimize environmental impacts, and optimize costs. This study explores how Machine Learning (ML) can model and optimize sewage treatment processes, adapting to real-time conditions. Method/Design/Approach: The BSM1 model is combined with Machine Learning techniques to create simplified metamodels, enabling optimized results and the development of an application for evaluating economic and technical outcomes. Results and Conclusion: The reduced metamodel successfully reproduced the Simulink model, achieving satisfactory optimization. Research Implications: This research benefits water quality improvement, cost reduction, sustainability, innovation, water resource management, awareness, and resilience to extreme weather events, as well as influencing informed policies. Originality/Value: Efficiency, sustainability, economy, and quality of life are core values in this research, benefiting society, the environment, and the economy.