Water Treatment Operations Research Articles

Industrial Testing of a Hybrid Technology for Water Purification from the Oka River Using the Method of Charcoalization with Powdered Sorbent

The development of the industrial sector and the increase in production capacity have a positive effect on the economic situation of the country, but not on environmental safety. Insufficiently purified and polluted wastewater is constantly discharged into water sources, the amount of which directly depends on the industrial development of the region. Thus, the anthropogenic load on the Oka River affects the composition of water in the source, there are often bursts of instantaneous emissions, as well as the flowering of phytoplankton in the summer. The latter further significantly reduce the efficiency of water purification to drinking quality. Therefore, the development of technologies that will be able to maintain purification at the required level is an urgent task. The article presents the method of charcoalization, which allows to neutralize pollution in a short time and improve the water treatment quality. An industrial experiment of the developed technology was carried out at an operating water treatment plant with testing of two extreme do-sages of sorption material. The optimal dose of coal pulp was determined, the impact on significant purification performance was described, and the technological solutions have been developed to determine the point of coal pulp input and the dosage multiplicity. The conducted research has made it possible to expand knowledge in the field of drinking water preparation and to determine significant water parameters that are affected by the sorption purification method.

Lithium Chloride-Mediated enhancement of dye removal capacity in Borneo bamboo derived nanocellulose-based nanocomposite membranes (NCMs)

Demand for sustainable materials for membrane fabrication is rapidly increasing, particularly in Sarawak coastal areas. nanocellulose emerges as a promising alternative for membrane development due to its availability, intrinsic biodegradability, and high strength. However, the hydrophobicity of nanocellulose limits its applicability in water treatment operations. Hence necessitating the need for innovative solutions. Herein, we examine the potential of bamboo-derived nanocellulose to enhance nanocomposite membrane-based water treatment systems by incorporating lithium chloride as a pore-forming additive. Nanocomposite membranes (NCMs) were synthesised using a LiCl-doped solution of nanocellulose and polyvinylidene fluoride (PVDF) through phase inversion and controlled solidification methods. The results show that nanocellulose incorporation induces structural alterations in NCMs, with reduced size in cellulose microfibrils. The crystallinity of nanocellulose was enhanced through interactions between PVDF and nanocellulose. The integration of nanocellulose along with lithium chloride was observed to have a significant impact on water permeation flux, attaining 104 L/m2h due to improved hydrophobicity and reduced fouling. In addition, the developed NCMs recorded 93 % methylene blue (MB) removal within 10 min. Revealing high potential of NCMs as an alternative and sustainable material for the development of nanocomposite membranes to mitigate contemporary challenges of water scarcity.

Fusing Design and Machine Learning for Anomaly Detection in Water Treatment Plants

Accurate detection of process anomalies is crucial for maintaining reliable operations in critical infrastructures such as water treatment plants. Traditional methods for creating anomaly detection systems in these facilities typically focus on either design-based strategies, which encompass physical and engineering aspects, or on data-driven models that utilize machine learning to interpret complex data patterns. Challenges in creating these detectors arise from factors such as dynamic operating conditions, lack of design knowledge, and the complex interdependencies among heterogeneous components. This paper proposes a novel fusion detector that combines the strengths of both design-based and machine learning approaches for accurate detection of process anomalies. The proposed methodology was implemented in an operational secure water treatment (SWaT) testbed, and its performance evaluated during the Critical Infrastructure Security Showdown (CISS) 2022 event. A comparative analysis against four commercially available anomaly detection systems that participated in the CISS 2022 event revealed that our fusion detector successfully detected 19 out of 22 attacks, demonstrating high accuracy with a low rate of false positives.

Aquaporin provides membranes for innovative water treatment technology project

Danish water technology company Aquaporin A/S is part of the RESURGENCE project that is aiming to significantly contribute to EU climate neutrality, circularity and competitiveness by exploring synergies between urban water treatment and industrial operations through the use of innovative technology.

Machine learning for water demand forecasting: Case study in a Brazilian coastal city

ABSTRACT Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case.

MAO-DBN based membrane fouling prediction

Due to the complexity of the factors influencing membrane fouling in membrane bioreactors (MBR), it is difficult to accurately predict membrane fouling. This paper proposes a multi-strategy of integration aquila optimizer deep belief network (MAO-DBN) based membrane fouling prediction method. The method is developed to improve the accuracy and efficiency of membrane fouling prediction. Firstly, partial least squares (PLS) are used to reduce the dimensionality of many membrane fouling factors to improve the algorithm’s generalization ability. Secondly, considering the drawbacks of deep belief network (DBN) such as long training time and easy overfitting, piecewise mapping is introduced in aquila optimizer (AO) to improve the uniformity of population distribution, while adaptive weighting is used to improve the convergence speed and prevent falling into local optimum. Finally, the prediction of membrane fouling is carried out by utilizing membrane fouling data as the research object. The experimental results show that the method proposed in this paper can achieve accurate prediction of membrane fluxes, with an 88.45% reduction in RMSE and 87.53% reduction in MAE compared with the DBN model before improvement. The experimental results show that the model proposed in this paper achieves a prediction accuracy of 98.61%, both higher than other comparative models, which can provide a theoretical basis for membrane fouling prediction in the practical operation of membrane water treatment.

Simulation and Modeling of Water Treatment Plant with GPS-X Software for Health and Water Quality

Background: Waterborne diseases are infectious in nature, significantly impacting global health related to water quality and disease prevalence globally. In this study, GPS-X was used to design and simulate water treatment plants to determine the effectiveness of different mechanisms in water treatment. Method: The GPS-X water treatment software was utilized to evaluate raw and treated water performance at the Al-Kadhmaya Water Treatment Plant. Three scenarios were designed to assess the impact of varying flow rates and water parameters such as pH, alkalinity, turbidity, total dissolved solids (TDS), and total suspended solids (TSS) on the plant's performance. Results: The evaluation of the operational performance of the water treatment plant revealed that the concentrations of all contaminants in the effluent water consistently met the established Iraqi and WHO standards across all scenarios. This indicated the effectiveness of the plant's treatment processes in producing safe drinking water. The findings of this study held significance for water authorities, decision-makers in Iraq, and national and international environmental agencies, providing valuable information for improving water treatment practices and ensuring compliance with water quality standards. Conclusion: The study demonstrated the effectiveness of the GPS-X water treatment software in assessing the performance of the Al-Kadhmaya Water Treatment Plant. The results highlighted the plant's ability to consistently meet water quality standards, underscoring the importance of robust water treatment processes. These findings contributed to the body of knowledge regarding water treatment plant design and operation, offering insights that can inform decision-making processes to enhance water quality and ensure public health.

Sustainable management of produced water supply chain system: the validation of model-independent parameters for Kuwait oil company case study

ABSTRACT A substantial amount of wastewater is generated during the extraction of oil and gas. Wastewater that is generated as a result of oil and gas extraction activities is known as produced water (PW), and managing PW is one of the greatest challenges faced by the oil and gas industry. Effective management of PW requires an understanding of the complex impact it may have on both oil production and water treatment operations. Here, a numerical model has been applied to a real-world case study, that of Kuwait Oil Company (KOC), to examine the relationships among various input and output factors in PW supply chain management. Specifically, the numerical model is a multi-objective optimisation model for the management of PW supply chains that takes into account economic as well as environmental objectives. A global sensitivity analysis has also been performed to assess the economic and operational factors that can influence the management of PW in KOC. The findings of this study show that the operational costs, treatment costs and environmental impacts can be assessed and used to guide the management of the PW supply chain in KOC.

Opflow Celebrates 50 Years of Operators Teaching Operators

After 50 years of publication, Opflow continues to share practical advice and wisdom with water treatment and distribution system operators. Operators sharing what they’ve learned is one of the most effective ways to ensure successful continuity in any utility.

An assessment of a biosensor system for the quantification of microcystins in freshwater cyanobacterial blooms

Microcystin-producing cyanobacterial blooms are a global issue threatening drinking water supplies and recreation on lakes and beaches. Direct measurement of microcystins is the only way to ensure waters have concentrations below guideline concentrations; however, analyzing water for microcystins takes several hours to days to obtain data. We tested LightDeck Diagnostics’ bead beater cell lysis and two versions of the quantification system designed to give microcystin concentrations within 20 min and compared it to the standard freeze-thaw cycle lysis method and ELISA quantification. The bead beater lyser was only 30 % effective at extracting microcystins compared to freeze-thaw. When considering freeze-thaw samples analyzed in 2021, there was good agreement between ELISA and LightDeck version 2 (n = 152; R2 = 0.868), but the LightDeck slightly underestimated microcystins (slope of 0.862). However, we found poor relationships between LightDeck version 2 and ELISA in 2022 (n = 49, slopes 0.60 to 1.6; R2 < 0.6) and LightDeck version 1 (slope = 1.77 but also a high number of less than quantifiable concentrations). After the quantification issues are resolved, combining the LightDeck system with an already-proven rapid lysis method (such as microwaving) will allow beach managers and water treatment operators to make quicker, well-informed decisions.

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.

Application of PDCA Cycle in Safety Management of Water Treatment Operation Project

Application of PDCA Cycle in Safety Management of Water Treatment Operation Project

First of its kind in Hong Kong - innovative reuse of treated effluent and enhanced energy efficiency for air-conditioning systems

In Hong Kong, air-conditioning (A/C) systems are major consumers of electricity. Although water-cooled air-conditioning systems (WACSs) are more energy efficient, the need for water towers, water treatment and other operation and maintenance (O&amp;M) demands restrain their application in most buildings. This paper reviews a pilot application of treated effluent from sewage treatment works as a cooling medium for the Direct Expansion type of A/C system within the treatment Plant. The Drainage Services Department (DSD) installed a WACS with a self-assembled effluent-cooled condenser to study the energy efficiency, cooling performance and actual O&amp;M requirements of the new system over a one-year pilot. It revealed that an energy saving of up to 27.6% per year could be achieved when compared with a conventional air-cooled air-conditioning system (AACS). This innovative effluent cooling A/C system achieves innovative reuse of treated effluent as well as reduction in carbon footprint as a result of higher energy efficiency for sustainable development.

Assess the formation of disinfection by-products from pyrogenic dissolved organic matter (pyDOM): impact of wildfire on the water quality of forest watershed

Wildfires can release pyrogenic dissolved organic matter (pyDOM) into the forest watershed, which may pose challenges for water treatment operations downstream due to the formation of disinfection by-products (DBPs). In this study, we systematically assessed the physio-chemical properties of pyDOM (e.g., electron-donating and -accepting capacities; EDC and EAC) and their contributions to DBP formation under different disinfection scenarios using (1) ten lab samples produced from various feedstocks and pyrolysis temperatures, and (2) pre- and post-fire field samples with different burning severities. A comprehensive suite of DBPs—four trihalomethanes (THMs), nine haloacetic acids (HAAs), and seven N-nitrosamines—were included. The formations of THM and HAA showed an up to 5.7- and 8.9-fold decrease as the pyrolysis temperature increased, while the formation of N-nitrosamines exhibited an up to 6.6-fold increase for the laboratory-derived pyDOM. These results were supported by field pyDOM samples, where the post-fire samples consistently showed a higher level of N-nitrosamine formation (i.e., up to 5.3-fold), but lower THMs and HAAs compared to the pre-fire samples. To mimic environmental reducing conditions, two field samples were further reduced electrochemically and compared with Suwannee River natural organic matter (SRNOM) to evaluate their DBP formation. We found increased DBP formation in pyDOM samples following electrochemical reduction but not for SRNOM, which showed increased N-nitrosamines but decreased THMs and HAAs post-electrochemical reduction. Furthermore, this study reported for the first time the formation of two previously overlooked N-nitrosamines (i.e., nitrosodiethylamine (NDEA), N-nitrosodi-n-propylamine (NDPA)) in both laboratory and field pyDOM samples, raising concerns for drinking water safety given their higher toxicity as compared to the regulated counterparts. Results from this study provide new insights for DBP mitigation during post-fire recovery, which are particularly relevant to communities that rely on forest watersheds as their drinking water sources.

Process of Quality Control of Bottled Water at PT. Tirta Purbalingga Adijaya, Bogor

Quality control is important in the drinking water production process. Drinking water sourced from nature, of course, contains a variety of minerals, ions, and even microorganisms, so it needs to be processed first so that it meets the needs of the human body and is safe for consumption. PT. Tirta Purbalingga Adijaya, Bogor in producing bottled drinking water refers to SNI 3553: 2015 concerning requirements for drinking water quality, as well as in processing it refers to the Regulation of the Minister of Health No.32 of 2017 concerning Standards for Quality Standards for Environmental Health and Water Health Requirements for Sanitation Hygiene Purposes , Swimming Pools, Solus Per Aqua, And Public Baths. PT. Tirta Purbalingga Adijaya, Bogor implements the Hazard Analysis Critical Control Point (HACCP) system where both raw water, processing and finished products are quality controlled every hour by water treatment operators and the results are verified by Quality Control analysts. Important things that become critical control points are filtering with carbon filters and ozone injection. If these two things are not controlled, it will affect the quality content of the drinking water produced and cause hazards such as residual chlorine and microbial contamination so that the product does not meet standards and cannot be marketed. The tests carried out were physical tests such as organoleptic tests, chemical tests such as chlorine content, ozone, metals and others as well as microbiological tests such as total microbes, fungi, coliform and pseudomonas aeruginosa. Based on the analysis that was carried out for 1 week on January 9-14 2023, it was concluded that the Prim-A brand bottled drinking water produced by PT. Tirta Purbalingga Adijaya, Bogor has met the standards set in Indonesia and is safe for human consumption.

Comparing artificial and deep neural network models for prediction of coagulant amount and settled water turbidity: Lessons learned from big data in water treatment operations

Machine learning has been applied to the modeling of water treatment processes. While machine learning models have a great ability to handle nonlinear relationships in the process, changes in raw water quality and process operations can make predictions difficult. This study investigated the use of machine learning models, including traditional and deep learning approaches, for predicting both coagulant dosage and settled water turbidity in the water treatment process using six years of operating data. The study found that deep learning models, which process temporal sequential data, significantly improved prediction accuracies in response to changing dynamics of water treatment processes. The results emphasize the importance of collecting large datasets for modeling water treatment processes to capture rapid changes in raw water quality, thereby increasing prediction accuracies. The modeling results provide suggestions for model selection, data collection, and monitoring implementation in water treatment plants, which can enhance the accuracy of predictions and ensure high-quality treated water.

Forecasting bacteriological presence in treated drinking water using machine learning

A novel data-driven model for the prediction of bacteriological presence, in the form of total cell counts, in treated water exiting drinking water treatment plants is presented. The model was developed and validated using a year of hourly online flow cytometer data from an operational drinking water treatment plant. Various machine learning methods are compared (random forest, support vector machines, k-Nearest Neighbors, Feed-forward Artificial Neural Network, Long Short Term Memory and RusBoost) and different variables selection approaches are used to improve the model's accuracy. Results indicate that the model could accurately predict total cell counts 12 h ahead for both regression and classification-based forecasts—NSE = 0.96 for the best regression model, using the K-Nearest Neighbors algorithm, and Accuracy = 89.33% for the best classification model, using the combined random forest, K-neighbors and RusBoost algorithms. This forecasting horizon is sufficient to enable proactive operational interventions to improve the treatment processes, thereby helping to ensure safe drinking water.

Anisotropic biofouling behavior of sharkskin-patterned desalination membranes

Biofouling is a crucial issue in operating water treatment and desalination membranes because it deteriorates membrane performance. Topological tailoring of the membrane surface by implanting a biomimetic sharkskin (Sharklet) pattern has proven effective at mitigating biofouling. However, the anisotropic antibiofouling effect of the uniaxially aligned Sharklet-patterned membrane and its underlying mechanism remain unknown. Herein, the biofouling tendency of the Sharklet-patterned membrane was examined by gradually varying the angle between the pattern orientation and the bulk flow direction from 0° to 90°. The antibiofouling effect of the membrane was gradually enhanced as the pattern orientation angle increased. Computational simulation revealed that increasing the pattern orientation angle intensifies vortex flows and diverts local flows to a large extent while reducing the local flow parallel to the bulk flow; this flow change is hypothesized to effectively prevent foulant accumulation, consequently enhancing biofouling resistance. Furthermore, the observed anisotropic biofouling behavior was quantitatively interpreted using a microbial growth model. A larger pattern orientation angle attenuates the bulk flow component parallel to the pattern alignment, which critically suppresses the rate and extent of microbial growth, thereby mitigating biofouling. Our findings offer fundamental insights into the anisotropic antibiofouling mechanisms of uniaxially and/or biomimetically patterned membranes.

Mo and N co-doped iron biochar materials activating peroxymonosulfate for enhanced degradation of bisphenol A: Mechanism discussion and practical application

Mo and N co-doped iron biochar material (Fe/Mox-NC) was prepared through one-pot calcination method with anhydrous iron chloride, ammonium molybdate, and cherry stone powder as precursors. The composite can effectively catalyze activation of peroxymonosulfate (PMS) for organic pollutants degradation, and the removal rate of 20 mg/L bisphenol A (BPA) reaches 99.08% in 30 min in the presence of 0.1 g/L Fe/Mo0.1-NC and 1 g/L PMS. Electrochemical analysis indicates that appropriate doped Mo and N can improve the electron transfer performance of the composite, which enhances the PMS activation property for BPA degradation. Quenching experiments and electron paramagnetic resonance analysis both demonstrated that the Fe/Mo0.1-NC/PMS system generated SO4•−, •OH and 1O2, which involved in BPA degradation. Moreover, a galvanic oxidation process had shown that there existed nonradical pathway which relied on electron transfer. The degradation pathway is proposed by measuring the intermediate products with HPLC-HRMS/MS. Through the exploration of the reaction mechanism, low-valence molybdenum ions can promote the cycle of Fe2+/Fe3+, and the pyridine-N and graphitic-N contained in Fe/Mo0.1-NC can catalyze activation of PMS together with Fe2+. The designed membrane filtration device revealed the feasibility of the composite in dynamic process, which was great significant for actual operation of water treatment. Furthermore, the Fe/Mo0.1-NC/PMS system could effectively decompose organic pollutants in the effluent from sewage treatment plant, achieving 82.6% of total organic carbon removal rate. This study not only prepared a novel heteroatom doped Fe-biochar material for PMS activation to degrade organic pollutants, but also deepened the reaction mechanism and explored its applicability for advanced treatment of real wastewater.

Forecasting and Optimizing Dual Media Filter Performance via Machine Learning

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO4, FeCl3, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R2 of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.