Search

Access to clean and safe water is essential to sustain human life. With the growth of the world population, the demand for clean water is also increasing. Water Treatment Plants (WTPs) are among the highest electricity consumers, thus causing damage to the environment and human health by producing solid waste and sludge as by-products, along with air pollution and noise. With the fourth highest population in the world, Indonesia faces a high demand for clean water. The country has pledged to reach net zero by 2060 or sooner, requiring effort in all sectors of the economy. Taiwan is relatively small compared to Indonesia but has a higher average clean water supply per capita than Indonesia. This study assessed and compared the Carbon Footprint (CF) emitted from four WTPs in Indonesia and Taiwan. A Life Cycle Assessment (LCA) was used, employing a cradle to gate as the methodology. The results showed that the Indonesian WTPs emit more CF than the Taiwanese ones. Electricity consumption from the intake operation and screening process mainly contributes to the CF in most of the studied WTPs. While chemical usage is related to the amount of treated water and the water quality regulations, their consumption has more impact on ecosystem services in Taiwan. Using both renewable energy and good chemical management will be a better solution to lessen the environmental impact of all of these WTPs. [ABSTRACT FROM AUTHOR]

The scope of the present study is the estimation of key operational parameters of a drinking water treatment plant (DWTP), particularly the dosages of treatment chemicals, using artificial neural networks (ANNs) based on measurable in situ data. The case study consists of the Aposelemis DWTP, where the plant operator had an estimation of the ANN output parameters for the required dosages of water treatment chemicals based on observed water quality and other operational parameters at the time. The estimated DWTP main operational parameters included residual ozone (O3) and dosages of the chemicals used: anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas (Cl2(g)). Daily measurable results of water sample analysis and recordings from the DWTP Supervisory Control and Data Acquisition System (SCADA), covering a period of 38 months, were used as input parameters for the artificial neural network (1188 values for each of the 14 measurable parameters). These input parameters included: raw water supply (Q), raw water turbidity (T1), treated water turbidity (T2), treated water residual free chlorine (Cl2), treated water concentration of residual aluminum (Al), filtration bed inlet water turbidity (T3), daily difference in water height in reservoir (∆H), raw water pH (pH1), treated water pH (pH2), and daily consumption of DWTP electricity (El). Output/target parameters were: residual O3 after ozonation (O3), anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas supply (Cl2(g)). A total of 304 different ANN models were tested, based on the best test performance (tperf) indicator. The one with the optimum performance indicator was selected. The scenario finally chosen was the one with 100 neural networks, 100 nodes, 42 hidden nodes, 10 inputs, and 4 outputs. This ANN model achieved excellent simulation results based on the best testing performance indicator, which suggests that ANNs are potentially useful tools for the prediction of a DWTP's main operational parameters. Further research could explore the prediction of water chemicals used in a DWTP by using ANNs with a smaller number of operational parameters to ensure greater flexibility, without prohibitively reducing the reliability of the prediction model. This could prove useful in cases with a much higher sample size, given the data-demanding nature of ANNs. [ABSTRACT FROM AUTHOR]

Among the various techniques used to reduce hydrogene sulfide in biogaz avoiding harmful effects on engines, the chemical and biological treatment appears particularly promising. The main objective of this article was to develop a new process to reduce the harmful effect of hydrogen sulfide (H2S), contained in the biogas resulting from methanization, on the equipment of the wastewater treatment plant (WWTP) of FES city in particular the two cogeneration units. A multiple regime technique for biogas desulfuration, based on the chemical and biological treatment as well as internal micro-aeration of the digester was developed. Owing to the insights gained from this study, it was identified that reducing the concentration of H2S in biogas and improving methane production (biogas production increased from 3.6 M Nm³ in 2018 to 3.8 M Nm³ in 2019, a saving of about 300,000 MAD); reduction of desulfurization tower downtime from 4 times/year to 1 time/year; increasing operating time of generating sets from 8800 in 2018 to 14 400 h in 2019; electricity production increased from 5.9 GWh in 2018 to 7.2 GWh in 2019. In light of these findings, it can be affirmed that the study successfully achieved its objectives, presenting valuable avenues for future scientific exploration. [ABSTRACT FROM AUTHOR]

The ingestion of Ti-containing nanoparticles from drinking water has emerged as a concern in recent years. This study therefore aimed to characterize Ti-containing nanoparticles in water samples collected from four water treatment plants in Taiwan and to explore the challenges associated with measuring them at low levels using single particle-inductively coupled plasma mass spectrometry. Additionally, the study sought to identify the most effective processes for the removal of Ti-containing nanoparticles. For each water treatment plant, two water samples were collected from raw water, sedimentation effluent, filtration effluent, and finished water, respectively. Results revealed that Ti-containing nanoparticles in raw water, with levels at 8.69 μg/L and 296.8 × 103 particles/L, were removed by approximately 35% and 98%, respectively, in terms of mass concentration and particle number concentration, primarily through flocculation and sedimentation processes. The largest most frequent nanoparticle size in raw water (112.0 ± 2.8 nm) was effectively reduced to 62.0 ± 0.7 nm in finished water, while nanoparticles in the size range of 50–70 nm showed limited changes. Anthracite was identified as a necessary component in the filter beds to further improve removal efficiency at the filtration unit. Moreover, the most frequent sizes of Ti-containing nanoparticles were found to be influenced by salinity. Insights into the challenges associated with measuring low-level Ti-containing nanoparticles in aqueous samples provide valuable information for future research and management of water treatment processes, thereby safeguarding human health. [ABSTRACT FROM AUTHOR]

The potentially harmful effects of consuming potentially toxic elements (PTEs) and microplastics (MPs) regularly via drinking water are a significant cause for worry. This study investigated PTEs (Cd, Cu, Cr, Ni, Pd, Zn, Co), MPs, turbidity, pH, conductivity, and health risk assessment in the water treatment plant in Kielce, Poland. Zn had the highest concentrations throughout the water treatment facility, whereas Cd, Pb, and Co had lower concentrations (< 0.1 µg/L). The order of the concentrations among the specified PTEs was like Zn˃Cu˃Ni˃Cr˃Cd˃Pb and Co. The minimum turbidity was 0.34, and the maximum was 1.9 NTU. The range of pH in water samples was 6.51—7.47. The conductivity was 1,203—1,445 ms in water samples. These identified MPs were categorized into fiber and fragments. The color of these identified MPs was blue, red, black, green, and transparent. The minimum and maximum size of the MPs was 196 and 4,018 µm, while the average size was 2,751 ± 1,905 µm. The average concentration of MPs per liter of the water treatment plant was 108.88 ± 55.61. The elements listed are C, O, Na, Mg, Al, Si, K, Ca, and Ti. Fe and Zn were the predominant elements seen using EDX. HQ values of the PTEs were less than one for adults and children. The human health risk associated with all detected PTEs revealed that the HQ values exhibit a satisfactory degree of non-carcinogenic adverse health risk. HI values for adults and children age groups were less than one. In most water treatment samples, the carcinogenic value exceeds the threshold value of 10−6. The PTEs and MP concentrations in drinking water should be periodically monitored to minimize consumers' environmental pollution and health risks. [ABSTRACT FROM AUTHOR]

Drinking water treatment reduces or eliminates certain health risks and ensures appropriate water quality by removing physical, chemical, and biological pollutants. The treatment process’s increased need for energy, chemicals, and technological inputs raises the expense of producing water as well as its secondary environmental effects. The goal of this research is to use the water quality index (WQI) and life cycle assessment (LCA) to determine and assess the environmental effects of the Al-Hashimiyah water treatment plant (WTP) in Babylon City, Iraq. The water quality index was employed as a criterion for categorizing and treating water in accordance with fundamental water characterization variables using a weighted arithmetic index technique. The LCA was supported by the EcoIndicator 99 database and SimaPro 7.0 software. What makes this study unusual is the identification of two extra functional units related to decontamination, beyond the usual one cubic meter treated water. Samples of treated and raw water were gathered during a 25-month period, from March 2022 to March 2023, and were regularly tested. The results demonstrated that all chemical and physical characteristics (for both raw and processed water) met Iraqi criteria, with the exception of total suspended particles and electrical conductivity. According to LCA studies, certain environmental consequences grow as pollutant concentrations drop. Due to this, a more thorough analysis of the environmental performance of water treatment facilities is now required. [ABSTRACT FROM AUTHOR]

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making. [ABSTRACT FROM AUTHOR]

One of the main methods for determining coagulant dosage (CD) is the jar test. However, this method is expensive, time-consuming, and requires laboratory equipment. In this situation, machine learning, especially hybrid machine learning, is suitable to estimate the CD without requiring a jar test. To this end, a hybrid model based on a combination of the M5 and the gorilla troops optimizer (GTO) algorithm was introduced as the M5-GTO model. Nine different parameters, including raw water production (RWP), turbidity of water, Conductivity, TDS, Salinity, pH, water temperature (WT), SM, and O2, were also utilized as inputs in the CD modeling. The results of comparing the proposed model with the multiple linear regression, multiple nonlinear regression, artificial neural network, multivariate adaptive regression splines, M5 model tree, k-nearest neighbor, least-squares support vector machine, general regression neural network, and random forest (RF) showed that this model is more accurate in CD modeling. Besides, the M5-GTO well estimated the distribution of observational data. For example, the values of the mean absolute error, root mean square error (RMSE), relative RMSE, normalized RMSE, and correlation coefficient criteria for the M5-GTO were equal to 0.562, 1.172, 0.257, 0.170, and 0.967, which were up to 73%, 54.9%, 54.8%, 55%, and 4% are more accurate than LSSVM (worst algorithm), respectively. In addition, the M5 and RF algorithms were also in the second and third ranks. The partial dependence plots results showed that the RWP and WT had the most significant effects on CD changes. Increasing the RWP reduced the amount of CD, while increasing the WT increased the CD. The algorithm introduced in the present study has a high potential for modeling various parameters in water treatment plants. [ABSTRACT FROM AUTHOR]

Background: The increase in population growth, industries and living standards have caused an increasing need for drinking water in many countries. The reuse of treated water and wastewater is one of the most important options to deal with water shortage. To ensure the correctness of this work, it is necessary that the health risk assessment be reassessed during use so that consumers do not face serious problems. In this regard, the assessment of health risks assessment for the water recovered from the backwashing wastewater of the Saveh water treatment plant was investigated.Methods: To reuse the backwash wastewater from the Saveh water treatment plant, the processes of primary sedimentation and coagulation (in the form of a test jar) were investigated. Metals and heavy metals like iron (Fe), aluminum (Al), lead (Pb), Arsenic (As) and cadmium (Cd) were examined to evaluate health risks. The initial settling time was 1 hour, the coagulant used was FeCl3 made in Iran, and heavy metal contents were also measured with an Inductively coupled plasma (ICP) device.Results: The value of HRIs for Al, Fe, As, Pb and Cd in the treated spent filter backwash water (SFBW) with primary sedimentation and coagulation was less than “1” and indicates the absence of risk.Conclusion: The treated backwash wastewater treated with primary sedimentation and coagulation processes as well as raw water of the Saveh have no harmful effects in terms of heavy metals, and its reuse will not pose a risk to the health of the consumer.

Abstract Background “Carbon footprint” (CF) is a direct measure of greenhouse gas emissions caused by a defined activity and can demonstrate global warming effects. The emissions of Greenhouse gases (GHGs) in water projects start from the primary water sources, followed by transportation, construction, and operation phases in the final treatment plants. Due to their possible environmental impacts, the water treatment plants equipped with Reverse Osmosis (RO) units will be investigated for their carbon footprint. Methods The research question is “What is the carbon footprint of reverse osmosis in water treatment plants?”. The literature search in this study will be divided into two sequential sections; in the first section, the search will be limited to Scopus, Science Direct, EMBASE, and PubMed databases. The keywords of water, “water treatment plants”, “water purification”, desalination, “reverse osmosis”, RO, “carbon emission”, “carbon dioxide/CO2 emission”, “carbon footprint”, “Life Cycle Assessment” and, LCA will be used. The carbon footprint of RO will be expressed based on the direct and indirect effects based on RO capacity. In the second section, the internet and specialist search will be done, and the search will be updated. No date limitation will be considered, and the main search will be done in English. When the search is completed, the screening will be performed. After removing duplicates, the title and abstract will be examined. The full text will be read if the title and abstract are not helpful for decision-making. In addition, the bibliography and references will proceed after the full-text screening. The Collaboration for Environmental Evidence (CEE) Critical Appraisal Tool will be used for risk of bias checking and study validity assessment. After full-text evaluation, data will be collected and categorized by two authors. If there is enough data, meta-analysis will be performed. Systematic review registration PROSPERO CRD42022327572.

The scope of the present study is the estimation of key operational parameters of a drinking water treatment plant (DWTP), particularly the dosages of treatment chemicals, using artificial neural networks (ANNs) based on measurable in situ data. The case study consists of the Aposelemis DWTP, where the plant operator had an estimation of the ANN output parameters for the required dosages of water treatment chemicals based on observed water quality and other operational parameters at the time. The estimated DWTP main operational parameters included residual ozone (O3) and dosages of the chemicals used: anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas (Cl2(g)). Daily measurable results of water sample analysis and recordings from the DWTP Supervisory Control and Data Acquisition System (SCADA), covering a period of 38 months, were used as input parameters for the artificial neural network (1188 values for each of the 14 measurable parameters). These input parameters included: raw water supply (Q), raw water turbidity (T1), treated water turbidity (T2), treated water residual free chlorine (Cl2), treated water concentration of residual aluminum (Al), filtration bed inlet water turbidity (T3), daily difference in water height in reservoir (∆H), raw water pH (pH1), treated water pH (pH2), and daily consumption of DWTP electricity (El). Output/target parameters were: residual O3 after ozonation (O3), anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas supply (Cl2(g)). A total of 304 different ANN models were tested, based on the best test performance (tperf) indicator. The one with the optimum performance indicator was selected. The scenario finally chosen was the one with 100 neural networks, 100 nodes, 42 hidden nodes, 10 inputs, and 4 outputs. This ANN model achieved excellent simulation results based on the best testing performance indicator, which suggests that ANNs are potentially useful tools for the prediction of a DWTP’s main operational parameters. Further research could explore the prediction of water chemicals used in a DWTP by using ANNs with a smaller number of operational parameters to ensure greater flexibility, without prohibitively reducing the reliability of the prediction model. This could prove useful in cases with a much higher sample size, given the data-demanding nature of ANNs.

Critical Infrastructure Security Showdown 2021—Online (CISS2021-OL) represented the fifth run of iTrust's international technology assessment exercise. During this event, researchers and experts from the industry evaluated the performance of technologies designed to detect and mitigate real-time cyber-physical attacks launched against the operational iTrust testbeds and digital twins. Here, we summarize the performance of an anomaly detection mechanism, named AICrit, that was used during the exercise. AICrit utilizes the plant's design to determine the models to be created using machine learning, and hence is referred to as a "design-enhanced" anomaly detector. The results of the validation in this large-scale exercise reveal that AICrit successfully detected 95.83% of the 27 launched attacks. Our analysis offers valuable insights into AICrit's efficiency in detecting process anomalies in a water treatment plant under a continuous barrage of cyber-physical attacks. [ABSTRACT FROM AUTHOR]

Background: "Carbon footprint" (CF) is a direct measure of greenhouse gas emissions caused by a defined activity and can demonstrate global warming effects. The emissions of Greenhouse gases (GHGs) in water projects start from the primary water sources, followed by transportation, construction, and operation phases in the final treatment plants. Due to their possible environmental impacts, the water treatment plants equipped with Reverse Osmosis (RO) units will be investigated for their carbon footprint. Methods: The research question is "What is the carbon footprint of reverse osmosis in water treatment plants?". The literature search in this study will be divided into two sequential sections; in the first section, the search will be limited to Scopus, Science Direct, EMBASE, and PubMed databases. The keywords of water, "water treatment plants", "water purification", desalination, "reverse osmosis", RO, "carbon emission", "carbon dioxide/CO2 emission", "carbon footprint", "Life Cycle Assessment" and, LCA will be used. The carbon footprint of RO will be expressed based on the direct and indirect effects based on RO capacity. In the second section, the internet and specialist search will be done, and the search will be updated. No date limitation will be considered, and the main search will be done in English. When the search is completed, the screening will be performed. After removing duplicates, the title and abstract will be examined. The full text will be read if the title and abstract are not helpful for decision-making. In addition, the bibliography and references will proceed after the full-text screening. The Collaboration for Environmental Evidence (CEE) Critical Appraisal Tool will be used for risk of bias checking and study validity assessment. After full-text evaluation, data will be collected and categorized by two authors. If there is enough data, meta-analysis will be performed. Systematic review registration PROSPERO CRD42022327572. [ABSTRACT FROM AUTHOR]

Water treatment plant (WTP) can be described as water processing to attain water quality that meets specific end-user or community objectives. A WTP's performance assessment is a method for measuring functioning efficiencies based on certain performance indices such as degree of removal of pollutants such as turbidity, color, suspended impurities, etc. The present study aimed to evaluate the performance of Qandil WTP units, Erbil City, Iraq. For assessment of the WTP units by turbidity removal efficiency, water samples were collected from raw water, after clarification, after filtration, and storage tank. Obtained removal efficiencies for the sedimentation unit, filtration unit, after disinfection, and the entire Qandil WTP were 86.83 %, 91.28 %, 31.26%, and 99.29 %, respectively. Also, water quality index (WQI) for the WTP was studied. WQI assessment was made by testing 14 physicochemical and bacteriological drinking water quality parameters such as turbidity, pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity, total hardness, calcium (Ca), chloride (Cl), Sulfate (SO4), magnesium (Mg), sodium (Na), potassium (K), Nitrate (NO3), and total Coliform. It has been found that turbidity, EC, total alkalinity, and total hardness had more effect on drinking water quality. WQI for Qandil WTP was 43.29 and it regarded as excellent level.

Microplastics (MPs) cannot be completely removed from water/wastewater in conventional wastewater treatment plants (WWTPs) and drinking water treatment plants (DWTPs). According to the literature analysis, membrane technologies, one of the advanced treatment technologies, are the most effective and promising technologies for MP removal from water and wastewater. In this paper, firstly, the properties of MPs commonly present in WWTPs/DWTPs and the MP removal efficiency of WWTPs/DWTPs are briefly reviewed. In addition, research studies on MP removal from water/wastewater by microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), and membrane bioreactors (MBRs) are reviewed. In the next section, membrane filtration is compared with other methods used for MP removal from water/wastewater, and the advantages/disadvantages of the removal methods are discussed. Moreover, the problem of membrane fouling with MPs during filtration and the potential for MP release from polymeric membrane structure to water/wastewater are discussed. Finally, based on the studies in the literature, the current status and research deficiencies of MP removal by membrane technologies are identified, and recommendations are made for further studies. HIGHLIGHTS The removal efficiency of MPs by pressure-driven membrane technologies and MBRs are presented and discussed separately.; The advantages of MP removal by membrane technologies compared to other removal methods are discussed.; Studies investigating membrane fouling with MPs and the possibility of MP release from polymeric membranes to water/wastewater are discussed.;

The main objective of this work is to evaluate the antiparasitic activity of Moringa oleifera seeds in the purification of waste water. Parasitological analyzes were carried out on three types of water: raw sewage (RS), treated sewage (TS), and wastewater treated (WT) with M. oleifera seeds. The results obtained, showed that the RS have a high pollution in parasites (Entamoeba histolytica, Entamoeba coli, Giardia lamblia and Ascaris sp, Strongyloides sp, Trichuris spp, Ankylostoma sp). The TS revealed a decrease in helminth 9 eggs/L. however, this value remain above the algerian andWHO standards. The results obtained of wastewater treated with M. oleifera seeds, a total absence of parasites. The rate of abatement of protozoan cysts and helminth eggs were of treated sewage was 85.86% and 58.02% respectively. While for water treated by M. oliefera was 100% and 100% respectively. [ABSTRACT FROM AUTHOR]

Triclosan (TCS) has been increased in the water during the COVID-19 pandemic because it cannot remove by conventional water treatment. In addition, it can accumulate in the human body over time through long-term exposure. Therefore, the occurrence of TCS in the water treatment plant (WTP) and tap water, and its human health risk assessment through tap water ingestion, dermal absorption, and inhalation routes in Isfahan, Iran, were investigated. Moreover, spatial regression methods were used for the prediction of water quality parameters, TCS concentration, and total hazard quotient (HQ). The average TCS concentration in the influent and effluent of WTP and tap water was 1.6, 1.4, and 0.4 μg/L, respectively. Conventional WTP has low efficiency in the removal of TCS (12.6%) from water. The average values of total HQ for males were 7.79×10−5, 4.97×10−4, and 4.97×10−5 and for females were 3.31×10−5, 2.11×10−4, and 2.11×10−5 based on RfDEPA, RfDMDH, and RfDRodricks, respectively that were in the low-risk levels (HQ<1). Furthermore, TCS concentration in tap water and the ingestion rate of drinking water had the highest effect on the risk of TCS exposure from tap water. The non-carcinogenic health risk of TCS in water was low. The results of this study may be useful for promoting WTP processes to remove emerging pollutants. [ABSTRACT FROM AUTHOR]

Worldwide, there has been an increase in the presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs). The objective of this study is to validate the use of in situ probes for the detection and management of cyanobacterial breakthrough in high and low-risk DWTPs. In situ phycocyanin YSI EXO2 probes were devised for remote control and data logging to monitor the cyanobacteria in raw, clarified, filtered, and treated water in three full-scale DWTPs. An additional probe was installed inside the sludge holding tank to measure the water quality of the surface of the sludge storage tank in a high-risk DWTP. Simultaneous grab samplings were carried out for taxonomic cell counts and toxin analysis. A total of 23, 9, and 4 field visits were conducted at the three DWTPs. Phycocyanin readings showed a 93-fold fluctuation within 24 h in the raw water of the high cyanobacterial risk plant, with higher phycocyanin levels during the afternoon period. These data provide new information on the limitations of weekly or daily grab sampling. Also, different moving averages for the phycocyanin probe readings can be used to improve the interpretation of phycocyanin signal trends. The in situ probe successfully detected high cyanobacterial biovolumes entering the clarification process in the high-risk plant. Grab sampling results revealed high cyanobacterial biovolumes in the sludge for both high and low-risk plants. [ABSTRACT FROM AUTHOR]

The realization that we are highly dependent on the increasingly limited water resources and the opportunity offered by the circular economy for the reversibility of this important resource become a matter of personal and societal growth. The use of wastewater treatment plants has been proven to bring with it positives for both the environment and water, as well as for human health. The introduction of local waste water treatment plants in small agglomerations is the nowadays step towards a better and ecological environment, and in addition it leads to financial savings achieved by the low subsequent costs. [ABSTRACT FROM AUTHOR]

Ensuring good drinking water quality, which does not damage the population's health, should be a priority of decision factors. Therefore, water treatment must be carried out to remove the contaminants. Chlorination is one of the most used treatment procedures. Modeling the free chlorine residual concentration series in the water distribution network provides the water supply managers with a tool for predicting residual chlorine concentration in the networks. With regard to this idea, this article proposes alternative models for the monthly free chlorine residual concentration series collected at the Palas Constanta Water Treatment Plant, in Romania, from January 2013 to December 2018. The forecasts based on the determined models are provided, and the best results are highlighted. [ABSTRACT FROM AUTHOR]

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant’s systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making.

Due to climate change, some areas in the world witnessed higher levels of heavy rain than the capacity of the wastewater system of the streets. Therefore, water tankers are used for the dewatering process to take the extra rainwater from the streets to keep a smooth flow of vehicles and to use the water in agriculture and industry. Water is taken to a water treatment plant. Performing the dewatering process as fast as possible, especially in crowded streets, was ignored by researchers. In this study, at first, the problem was solved using two mixed integer programming (MIP) models. A new variant of identical parallel machine scheduling with job splitting is proposed for the first time, where one or at most two tankers can work at the same flood location at the same time. This is performed in the second model. However, the first model considers dividing the dewatering processes into two phases, where the first one, which is more urgent, is to reduce the amount of floodwater. The second one is for dewatering the rest of the water. Then two genetic algorithms (GAs) were used to solve faster the two MIP models, which are NP-hard problems. At first, the MIP and GA models were applied to small-sized problems. Then GA was used for large practical data sets. Results showed that for small problems, MIP and GA gave optimal solutions in a reasonable number of iterations, while for larger problems, good solutions were obtained in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

The current work is an illustration of an empirical investigation conducted on a pharmaceutical water treatment plant that subsequently proposes potential ICT implications for optimizing the plant's conventional operating procedures and improving production efficiency. Typically, the pilot plant incorporates a standard infrastructure for maintaining quality and production goals. In the study, a schematic of the reverse osmosis section of the pilot treatment plant was developed. A mathematical modeling and process simulation approach was adopted to carry out the linear process investigation and validation of key performance parameters. The study's findings reveal that the performance and lifecycle of the RO treatment unit are primarily determined via the structured pre-treatment filtering procedures, including critical parameters such as volumetric flowrate, solute concentrations, and differential pressure across the membrane. These operational parameters were also found to be instrumental in increasing plant production and improving equipment efficiency. Based on our results, the study proposes cost-effective ICT implications for plant managers through which pilot organization can substantially save on their annual water and energy consumption. [ABSTRACT FROM AUTHOR]

Producing potable water generates a large amount of sludge, which consumes energy and must be managed and controlled. A case study of a potable water production plant in Al Hoceima City in northern Morocco is described here. The plant produces 15000 ​m3 of potable water each day to meet the water demand of Al Hoceima City, and this causes 3 ​t of sludge to be produced each day. Sludge extracted while producing potable water was characterized, and the possibility of reusing the sludge rather than disposing of it directly to the environment was assessed. The pH, turbidity, aluminum content, and other characteristics of the sludge were determined. Jar tests were performed to determine the optimum coagulant (aluminum sulfate) dose to ensure that the minimum required amount of aluminum sulfate was used. The characterization results indicated that the sludge was stable and reusable. The jar-test results allowed the amount of coagulant used to be optimized and will lead to markedly less sludge being disposed of than is currently the case.

Using the example of the city and commune of Mikołajki, the state of water and sewage management as well as the management of groundwater resources available in the commune has been characterized for a typical tourist town and commune located in Mrągowo County, Warmian-Masurian Voivodeship. The town and commune of Mikołajki, located in the Warmian-Masurian Voivodeship, has about 7 457 inhabitants. Over 83% of the commune's inhabitants are covered by the water supply network. The Mikołajki commune has four water intakes (one of which is out of use) and three water treatment plants. The water used by the inhabitants of the commune meets all sanitary requirements. [ABSTRACT FROM AUTHOR]

The concentrations level and distribution of 16 US EPA polycyclic aromatic hydrocarbon (PAHs) from the water treatment plant (WTP), sewage treatment plant (STP), and industrial water treatment plant (ITP) sludge in Taiwan were determined and then assessed the sources, and potential toxicity (carcinogenic polycyclic aromatic hydrocarbons [CPAHs] and toxic BaP equivalent [TEQ]). Results indicated that the total concentrations of PAHs ranged between 58 and 16,436 μg/kg dw. Among the 17 samples, the 2-4 ring of total PAHs were the predominant compound in three kinds of treatment plant (> 60%). Especially, ITP1 owns 95.8% of 2-4 ring of total PAHs and ITP3 owns 54% of five- and six-ring of total PAHs. The molecular indices and principal component analysis (PCA) were used to determine the source contributions, with the results showing that the contributions of combustion/grass, coal or wood combustion and combustion/ liquid (oil) fossil fuel combustion. A PAH toxicity indicated by TEQ was 2.5–506 μg TEQ/g dw. Although, the results indicated that these were not recommended for land applications, but analyses are beneficial to develop effective management strategies for controlling PAH discharge in treatment plants and establishing strategies for its reuse in managing pollutants. [ABSTRACT FROM AUTHOR]

Environmental risk can result from the interaction between human activity and the environment. Human activities can generate certain changes in the environmental components, thus inducing a potential danger for the health of people, flora, vegetation and fauna through the negative effects they have on water resources, air and soil quality, climate, microclimate, and so on. This paper presents the potable water treatment plant from Zănoaga, located in Petroșani Municipality, and highlights the potential risks related to the activity within this plant. Particular emphasis is placed on the risk to the environment and human health that may arise from improper storage and handling of liquid chlorine tanks, chlorine being a toxic gas if found in high concentrations in air, water or soil. [ABSTRACT FROM AUTHOR]

A 12‐event survey of 8 water treatment plants (WTPs) was conducted to determine the impact of the 2021 summer (January) to winter (August) cyanobacterial challenge on WTP performance. High cyanobacterial numbers (>20,000 cells/mL) and high taste and odor (T&O) concentrations (>100 ng/L) were encountered. The summer to winter transition of this cyanobacterial challenge also saw the proliferation of Pseudanabaena blooms which challenged conventional WTPs and a breakthrough of cells was detected. This required further investigation into optimizing Pseudanabaena removal in WTP processes. WTP sampling determined that the sludge lagoons provided conducive conditions for cell accumulation and metabolite release. These cells and T&O compounds were recycled back into the head of the WTP through the supernatant return flows. In the most extreme case, 512 ng/L of T&O compounds was detected in the supernatant return. This study highlighted the need to understand the potential risks that treatment plants can encounter and ensure appropriate management during and after cyanobacterial events. [ABSTRACT FROM AUTHOR]

The objective of this work was to characterize the quality of the water coming from the Guarumo water treatment plant through measurements of basic physicochemical parameters. For this, a selection of the parameters to be measured was made and these measurements were compared with the values established in the Ecuadorian Technical Standard INEN 1108-2014, Fifth Revision. The results show that the basic parameters to take into account are turbidity, temperature, residual chlorine and pH. The parameters analyzed comply with the requirements established in the standard for the period of analysis and can be used to permanently monitor the quality of the water and it is concluded that the water is of good quality. [ABSTRACT FROM AUTHOR]

The world lacks access to clean water for approximately a quarter million people. It is estimated that only 1% of the Earth's 2.5% water can be used. In developing countries, almost 80% of the three million early deaths can be attributed to drinking contaminated water due to organic and inorganic pollutants. Therefore, it is imperative that the treatment plants for surface water and waste water that supply treated water to the public is monitored regularly in order to ensure the quality of treated water. Water treatment plants performance can be evaluated using a variety of methods. However, these methods are absolute since they give equal weight to all parameters, which only partly reflects reality. This paper presents a hybrid decision making technique combining trapezoidal fuzzy BWM with trapezoidal fuzzy TOPSIS, whose goal is to determine the most beneficial alternative to improve the effectiveness of a water treatment plant (WTP). To evaluate the weights of criteria, the trapezoidal fuzzy BWM is employed, and TOPSIS is used to rank the alternatives. From the results, it is clearly found that water quality is the most responsible alternative. A sensitivity analysis validates the results obtained by the proposed study. [ABSTRACT FROM AUTHOR]

Plankton and other microscopic colloids are tiny particles that are suspended in water and cause turbidity, which causes the water to seem murky or opaque. These particles are too unstable and light to settle or be naturally eliminated. These details contribute to water turbidity and pose some stability. During the process of purifying raw water, all water treatment Plants (WTPs) produce waste/residue known as water treatment sludge (WTS). The majority of the sludge's chemical components include silica, alumina, ferric oxide, lime, and many heavy metals. The surface water treatment technique included coagulation, flocculation, sedimentation, and filtration to remove colloidal and suspended particles from raw water. The sludge obtained from the WTP located at Kekri (Rajasthan), India is being investigated for its physical and chemical properties. About 60% of the sand contained in the sludge is found in the 155-60 grain size range. Additionally, nutrient reduction of soil due to contamination and runoff can be minimized or rounded out by wastewater treatment or the removal of heavy metals from water solutions. To develop water-safe and appealing sludge management solutions, the efficiency of aluminum sulfate and poly aluminum chloride was assessed at different coagulant doses in the study. To make water safe and appealing for human consumption, numerous purification procedures are employed from a variety of sources. Sludge bricks are acceptable to high temperatures in the furnace and have better compressive strength than clay bricks. [ABSTRACT FROM AUTHOR]

Critical Infrastructure Security Showdown 2021—Online (CISS2021-OL) represented the fifth run of iTrust’s international technology assessment exercise. During this event, researchers and experts from the industry evaluated the performance of technologies designed to detect and mitigate real-time cyber-physical attacks launched against the operational iTrust testbeds and digital twins. Here, we summarize the performance of an anomaly detection mechanism, named AICrit, that was used during the exercise. AICrit utilizes the plant’s design to determine the models to be created using machine learning, and hence is referred to as a “design-enhanced” anomaly detector. The results of the validation in this large-scale exercise reveal that AICrit successfully detected 95.83% of the 27 launched attacks. Our analysis offers valuable insights into AICrit’s efficiency in detecting process anomalies in a water treatment plant under a continuous barrage of cyber-physical attacks.

Producing potable water generates a large amount of sludge, which consumes energy and must be managed and controlled. A case study of a potable water production plant in Al Hoceima City in northern Morocco is described here. The plant produces 15000 m3 of potable water each day to meet the water demand of Al Hoceima City, and this causes 3 t of sludge to be produced each day. Sludge extracted while producing potable water was characterized, and the possibility of reusing the sludge rather than disposing of it directly to the environment was assessed. The pH, turbidity, aluminum content, and other characteristics of the sludge were determined. Jar tests were performed to determine the optimum coagulant (aluminum sulfate) dose to ensure that the minimum required amount of aluminum sulfate was used. The characterization results indicated that the sludge was stable and reusable. The jar-test results allowed the amount of coagulant used to be optimized and will lead to markedly less sludge being disposed of than is currently the case. [ABSTRACT FROM AUTHOR]

Input of wastewater-borne pollutants to the water bodies of the Russian Federation were investigated, and the use of fresh water in Russia was analyzed. The dynamics of the input of pollutants to the water bodies over the period from 1993 to 2020, as well as that of the use of fresh water for the needs of industry, household and drinking water supply, and irrigation and agricultural water supply in the territory of the Russian Federation were analyzed. Mathematical models that adequately reflect the changes in the investigated parameters were selected. The forecast of the inputs of wastewater-borne phenols, lead, mercury, chlorides, sulfates, nitrates, and fats and oils to the water bodies of the Russian Federation, as well as the forecast of the use of fresh water for the needs of household and drinking water supply, irrigation and agricultural water supply, and industry for the year 2025 were prepared. [ABSTRACT FROM AUTHOR]

Analysis of traditional water distribution network (WDN) is more time-consuming and less effective to predict the problem related to water supply systems such as water quality, coagulant dose, and residual chlorine in developing countries. In the present paper water quality neural network, coagulation dose neural network, and residual neural network model were implemented. The performance of the Cascade Feed Forward Neural Network (CFFNN) and Feedforward neural network (FFNN) was excellent for the prediction of water quality parameters and residual chlorine respectively during the training and testing period. CFFNN water quality model (27-30-27) with R = 0.989 produced an excellent prediction of outlet water quality parameters. In coagulant dose modelling, CFFNN (2-40-1) yielded a good prediction with R = 0.947 for a broad range of turbidities as compared to other models. Similarly in residual chlorine modelling, FFNN (2-25-1) delivered the best prediction with R = 0.988 as compared to other models. [ABSTRACT FROM AUTHOR]

Polyaluminium chloride (PAC) with different basicity is used as a coagulant in most drinking water treatment plants (WTP). The aluminium concentration in PAC and its hydrolysis mechanism varied with the basicity of PAC. Incremental addition of PAC changes various physico-chemical properties and turbidity removal mechanisms in water. Water treatment plants use the PAC concentration beyond its optimum dose without considering other aspects, including residual aluminium concentration. In the present work, the effect of high and medium basicity of PAC on different physico-chemical properties like pH, zeta potential, and residual aluminium concentration of water was investigated. The pH of treated water decreases with the incremental addition of PAC, and an increase in zeta potential and residual aluminium concentration in treated water was evidenced. The change in pH after PAC addition is responsible for deciding the coagulation mechanism and efficiency of the coagulation process. pH reduction is comparatively more in high basicity PAC than medium basicity. PAC hydrolysis mechanism is controlled by the zeta potential of water and can be used as an alternative method to decide the optimum coagulant dose. The performance of clariflocculator and pulsator-based WTP was also evaluated for raw water from the same source. To reduce down the turbidity below the acceptable level, the coagulant requirement for clariflocculator based WTP is comparatively less than pulsator based WTP. The floc blanket in the pulsator gets disturbed with a slight change in the coagulant chemistry and quantity. HIGHLIGHTS Performance of clariflocculator and pulsator-based WTP was studied for the same raw water source.; Clariflocculator based WTP required comparatively less coagulant than pulsator based WTP to remove turbidity below 1 NTU.; Incremental coagulant addition decreases pH and enhances zeta potential and residual Al in treated water.; Floc blanket in the pulsator gets disturbed with a small change in the coagulant chemistry and quantity.;

Analysis of the velocity gradient of flocculators through Computational Fluid Dynamics (CFD) simulation can be essential to the optimization of hydraulic conditions in Water Treatment Plants. This study aims to simulate the velocity field in the last tank of a perforated tray-type flocculator and quantify locally velocity gradient (G) through CFD. This stage of flocculation has a higher risk of flocs rupture when there are not adequate conditions. Thus, simulations occurred at the flocculator current operational flow rate (7 ls-1), and at full capacity (9 ls-1). An alternative cost-effective and easy to implement modification was tested by increasing the number of orifices in the flocculator trays. As result, the velocity field indicates the formation of dead zones at the edges of the tank for all simulations, which facilitates short circuit occurrences. This is an indicator of reductions in water treatment efficiency. After structural modifications, simulations indicate a reduction in dead zone areas. Plus, as the flow rate increases, the maximum G inside the structure increases considerably (184 to 266 s-1), causing a risk for floc rupture. However, changing the number of orifices from 22 to 33 creates conditions for the flocculator to operate at higher flow rates without increasing the velocity gradient.

Ensuring good drinking water quality, which does not damage the population’s health, should be a priority of decision factors. Therefore, water treatment must be carried out to remove the contaminants. Chlorination is one of the most used treatment procedures. Modeling the free chlorine residual concentration series in the water distribution network provides the water supply managers with a tool for predicting residual chlorine concentration in the networks. With regard to this idea, this article proposes alternative models for the monthly free chlorine residual concentration series collected at the Palas Constanta Water Treatment Plant, in Romania, from January 2013 to December 2018. The forecasts based on the determined models are provided, and the best results are highlighted.

This article aims to study the hydrodynamic conditions of the parameters that influence the removal of dirt particles and aggregates by the drainage of wash water into filter materials for better regeneration efficiency. The water treatment plant with a nominal capacity of 600 m3·h−1 is located in the city of Parakou in Benin. It appears that one of the causes of the poor performance of the methods used for water treatment is the insufficient value of the shear stresses of the movement created by the flow of washing water in the filter bed. The use of hydro-elevators and hydrocyclones for the removal of particles from the surface of the grains of the filtering material under hydraulic load makes it possible to reduce the quantity of remaining pollution respectively by 1.5 to 2.5 times compared with the combined washing of water and air. To this end, the performance of grain washing in the hydrocyclone is explained by the presence of centrifugal field, self-separation and the friction of the particles between themselves and on the wall of the apparatus. The regeneration of the filter material under the action of ultrasonic waves reduces the remainder of particles compared to washing with water and air about 6 times. HIGHLIGHTS Study of the hydrodynamic conditions for dirt particles and aggregates removal.; Regeneration efficiency of the granular filtering material.; Calculation of the value of the shear stresses of the movement created by the flow of washing water in the filter bed.; Regeneration of the filter material under the action of ultrasonic waves.;