Performance Of Treatment Process Research Articles

Predicting energy performance of the drinking water treatment process and its determinants

ABSTRACT Within the global climate change framework, enhancing energy efficiency presents a significant challenge for water utilities. Drinking water treatment is energy-intensive, involving several physicochemical processes to remove multiple pollutants from raw water. This study employs artificial neural networks (ANNs) and decision tree methods to gain a deeper understanding of the water–energy nexus in drinking water treatment processes. The energy efficiency of a sample of Chilean drinking water treatment plants (DWTPs) was estimated, resulting in an average score of 0.343. This indicates that on average, DWTPs could potentially save 65.7% of their current energy consumption if they were operating at an efficient level while producing the same quantity and quality of drinking water. The main source of raw water and the technology for treating water have been identified as critical factors influencing energy efficiency. Specifically, using surface water for producing drinking water–-energy efficiency can increase to 0.514, whereas using groundwater would regress energy efficiency to 0.240. The use of predictive tools such as ANNs provides relevant information to support decision-making processes for a transition toward a sustainable urban water cycle.

Application of multi index comprehensive evaluation method in the selection of wastewater treatment processes

The study comprehensively evaluates various wastewater treatment processes by using Grey Relational Analysis (GRA) and Analytic Hierarchy Process (AHP) to identify the optimal method for wastewater treatment. To conduct this evaluation, six key indicators were identified and used for assessment: five-day biochemical oxygen demand (BOD5) removal rate, chemical oxygen demand (COD) removal rate, ammonia nitrogen removal rate, total nitrogen removal rate, total phosphorus removal rate, and suspended solid removal rate. Initially, GRA was employed to calculate the grey relational degree of each treatment process based on the weights of the indicators, and the treatment processes were then ranked and analyzed according to these degrees. Subsequently, AHP was applied to construct a judgment matrix, determine the weights of each indicator, and ensure the matrix's rationality through consistency testing. The study reveals significant differences in the performance of various wastewater treatment processes among the indicators, with the Modified A2/O+AO process showing the highest comprehensive performance in the evaluation. The findings provide a scientific basis for improving efficiency, reducing pollution, and guiding policy and technological development.

Impact of copper and sulfamethazine stress on microbial community and antibiotic resistance genes in denitrification systems: Comparison between heterotrophic and sulfur autotrophic denitrification processes

As common pollutants in wastewater, heavy metals and antibiotics have negative effects on the performance, microbial community and antibiotic resistance genes (ARGs) propagation of biological treatment processes. This study investigated the differential responses of heterotrophic denitrification (HD) and sulfur autotrophic denitrification (SAD) process to Cu(II) and sulfamethazine (SMZ) stress. Exposure to 12 mg/L Cu(II) and 25 mg/L SMZ, either alone or combined, reduced nitrogen removal efficiency by 4.22 % to 32.6 % for HD and 38.7 % to 63.2 % for SAD, respectively. HD exhibited greater robustness and microbial cooperation compared to SAD under stress. Cu(II) and SMZ intensified bacterial reactive oxygen species (ROS) responses, inhibited cell activity, and reduced extracellular polymeric substance secretion. Interestingly, microbially reduced sulfides (e.g., H2S and S2−) in SAD alleviated the cellular oxidative stress induced by Cu(II) and SMZ through ROS neutralization and CuS precipitation. Under stress, the relative abundance of ARGs increased significantly more in HD than in SAD compared to their respective control groups. Network analysis revealed more potential ARGs hosts in HD than in SAD. This study underscored the potential of the microbially reduced sulfides generated in the SAD process to effectively mitigate the spread of ARGs under selective pressure from heavy metals and antibiotics.

Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes

The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and optimizing the coagulant dose. This test is time-consuming and requires human intervention, meaning it is infeasible for making continuous process predictions. As a potential alternative, we developed a machine learning (ML) model from historical DWT plant data that can operate continuously using real-time sensor data without human intervention for predicting clarified water turbidity 15 min in advance. We evaluated three types of models: multilayer perceptron (MLP), the long short-term memory (LSTM) recurrent neural network (RNN), and the gated recurrent unit (GRU) RNN. We also employed two training methodologies: the commonly used holdout method and the theoretically correct blocked cross-validation (BCV) method. We found that the RNN with GRU was the best model type overall and achieved a mean absolute error on an independent production set of as low as 0.044 NTU. We further found that models trained using BCV typically achieve errors equal to or lower than their counterparts trained using holdout. These results suggest that RNNs trained using BCV are superior for the development of ML models for DWT processes compared to those reported in earlier literature.

Enhanced fluid mixing using a novel multi-stage static pipeline mixer: Numerical simulation and mechanism analysis

Static pipeline mixers play a crucial role in water treatment processes, and their mixing efficiency can be influenced by factors. Innovating mixer design to simultaneously reduce head loss and G-values while maintaining effective mixing across various flow conditions holds practical significance for improving treatment process performance. This study used a combination of numerical simulations and experimental validation to investigate the mixing process of a novel multi-stage static pipeline mixer. The results indicated that within the flow velocity range of 0.2–1.8 m/s, compared to the Kenics mixer, the new multi-stage static pipeline mixers (HOCM and COCM) achieved significant reductions in head loss (89.4 % for HOCM and 52.9 % for COCM) and G-values (67.3 % for HOCM and 31.3 % for COCM). The turbulent kinetic energy intensity of HOCM was much lower than that of COCM and Kenics mixers. Importantly, all mixers reached a state of complete mixing with the coefficient of variation (CoV) less than 0.05. In the HOCM mixer, the 2nd mixing stage was identified as the primary mixing zone, while in the COCM mixer, the 1st mixing stage played a crucial role in mixing. When the mixers achieved a mixing degree of 95 %, the head loss of HOCM and COCM was significantly lower than various types of traditional mixers (Kenics, SMX, SMV, ISG, Inliner Lightnin). This is attributed to the highly efficient mixing effect generated by the clockwise helical flow produced in the 1st and 2nd mixing stages.

The enhanced performance of Chlorococcum sphacosum in rich carbon wastewater treatment under the influence of calcium ion

The performance of microalgae-based wastewater treatment process is probably affected by high concentrations of organic carbon in wastewater, and calcium ions may influence this process. Nevertheless, the phenomena and mechanism involved have not been well explored. Here the performance of Chlorococcum sphacosum in treating wastewater with high concentrations of organic carbon was studied by coupling calcium ion concentration regulation (adding CaCl2 and free Ca2+ chelator EGTA) via physicochemical and transcriptomic analysis. It was found that photosynthesis and nutrients removal were suppressed and lipid accumulation was enhanced as the microalga was used to treat wastewater with high concentrations of organic carbon (more than 1500 mg/L). Simultaneously, antioxidant enzymes (SOD, CAT and POD) activity and calcium signaling-related protein (CaM, CDPK, and Ca2+-ATPase) content rose implying that the microalga was in a state of stress and calcium was involved in stress response. It was found that EGTA treatment further weakened the performance of microalgal photosynthesis and nutrients removal. However, the supplementation of Ca2+ had the opposite effect. Transcriptomic analysis revealed that Ca2+ strengthened the heterotrophic decomposition process of carbon (up-regulating the gene expression related to glycolysis and tricarboxylic acid cycle) and inhibited lipid accumulation (down-regulating lipid synthesis) via Ca2+ signalings transduction. Briefly, Ca2+ alleviated the stress of high concentration of organic carbon and strengthened the performance treating the corresponding wastewater.

Statistical Analysis of Wastewater at Panjapur, Trichy

Abstract This study is carried out to find wastewater quality index in treated wastewater, and Statistical analysis was done in various collected samples in sewage treatment plant, Panjappur, Trichy. This study presents a Statistical analysis of the physicochemical parameters of sewage wastewater. Nearly twenty parameters of two different months were analysed using data from physico -chemical analyses. A Continuous Waste Water Quality Index (CWWQI) was established to gauge the overall state of treated wastewater quality. By taking into account previously set quality limitation norms, the index condenses a substantial number of measured quality characteristics into a single water quality term. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). This study presents Factor analysis to identify the pollution and its type. The methods and approaches discussed in this study offer a framework for evaluating the monitoring programme for wastewater treatment.

Technology Selection for Wastewater Treatment in the Maldives

In the Maldives, the discharge of untreated wastewater into the sea poses a significant threat to public health and the marine environment. This study proposes technically feasible, sustainable, and economical wastewater treatment technologies appropriate for small islands in the Maldives. Out of the 187 residential islands in the Maldives, three islands (HA. Dhidhdhoo, HA. Hoarafushi, and HA. Ihavandhoo) were selected as study areas. This study characterized the wastewater from these islands using physiochemical and microbiological water quality assessment parameters. The Water and Wastewater Treatment Technologies Appropriate for Reuse (WAWTTAR), an open-source software developed by the Humbolt State University in 1993, was used to simulate the performance of different wastewater treatment processes to treat the characterized wastewater. The feasible alternatives identified from these simulations were analyzed using a Multi-Criteria Analysis (MCA), considering various indicators that influence the decision-making process. The wastewater quality assessment revealed that the wastewater generated in the three islands produced low concentrations of chemical and biological oxygen demand (COD and BOD), with concentrations ranging from 267 – 309 mg/L and 128 – 219 mg/L, respectively. Nitrate and phosphate concentrations were also low (5.7 – 18.0 mg/L and 2.8 – 14.9 mg/L, respectively), where these concentrations in Dhidhdhoo and Hoarafushi were already within the maximum allowable limits of 15 mg/L and 10 mg/L, respectively, for discharge into the deep sea. Based on the findings, secondary treatment technologies such as membrane bioreactors (MBR) and sequencing batch reactors (SBR) with Ultraviolet (UV) disinfection were found to be the most feasible options for treating the wastewater. These results were further checked for robustness using a sensitivity analysis. This study demonstrates that the proposed methodology is suitable for technology selection and can provide valuable information for policymakers and stakeholders in the country and serve as a basis for future studies.

Stormwater Treatment Using Natural and Engineered Options in an Urban Growth Area: A Case Study in the West of Melbourne

The expected increase in urbanization and population in coming years is going to increase the impervious land area, leading to substantial increases in stormwater runoff and hydrological challenges, and presents significant challenges for urban potable water supply. These are worldwide challenges that can potentially be ameliorated by harvesting stormwater for potable use or for other uses that can reduce the pressure on potable water supply. This study sought to assist the local water authority in planning for future potable water supply through a review of the scientific literature to determine the likely chemical and microbial characteristics of stormwater, the treatment train (TT) requirements, and the likely costs of treatment to achieve potable standards for the high-growth metropolitan region of Melbourne, Australia. Literature stormwater quality statistical data and treatment process performance data were used to model the expected product water microbial and chemical quality after treatment using a number of advanced TT options. The results of the modelling were compared with literature microbial log reduction targets (LRTs) for the potable use of stormwater and with the Australian Drinking Water Guidelines (ADWG). It was found that a reverse osmosis (RO)-based TT with microfiltration pre-treatment and post-RO advanced oxidation and chlorination in storage reservoirs is a conservative stormwater potable use treatment option. A less conservative and less expensive ozone-and-biologically active filtration (O3/BAF)-based TT option is also proposed if RO concentrate disposal is deemed to be too challenging. These results could be useful in climate change adaptation involving the evaluation of options for the mitigation of future population-growth- and climate-change-driven water supply challenges, as well as urbanization-driven stormwater hydrology and receiving water pollution challenges.

Life cycle comprehensive performance assessment of the TiAlN coated tool based on surface treatment

The optimal surface treatment process can availably improve tool surface properties, and meet the requirements of low-carbon manufacturing. In this work, based on tool surface modification technology and tool life cycle carbon emission system, TiAlN coated tools are carried out micro-blasting treatment (MBT), cryogenic treatment (CT) and heat treatment (HT). The effects of surface treatment processes on the surface integrity (morphology, roughness, micro-hardness, residual stress) of the coated tool are studied. The high-speed dry finishing turning experiments of cobalt-based superalloy are carried out to investigate the tool wear morphology and wear mechanism. The carbon emissions of the tool life cycle are analyzed. Finally, the comprehensive performance of the coated tool within life cycle is evaluated. The results show that the highest level of surface integrity of coated tool is achieved by MBT, while the lowest carbon emission of environmental is impacted by HT. Compared with the untreated (UT) tool, the micro-blasted tool has the most significant improvement in surface roughness with a 28.6% reduction. The cryogenic treated tool has the greatest improvement on the surface hardness which is increased by 44.4%. The effect of HT on the surface residual stress is the most obvious. The surface stress of the heat-treated tool has increased by 59.4%. Moreover, the flank wear VB of the micro-blasted tool, cryogenic treated tool and heat-treated tool is reduced by 25.17%, 24.50% and 32.12%, respectively. The main wear mechanisms of the treated tool are adhesive wear, oxidation wear and abrasive wear. According to comprehensive performance assessment, the performance of surface treatment process during the coated tool life cycle is: HT > MBT > CT > UT.

Fluorescence-based indicators predict the performance of conventional drinking water treatment processes: Evaluation based on the changes in the compositions of dissolved organic matter

This study investigated the treatability of dissolved organic matter (DOM) by the selected lab-scale drinking water treatment processes using fluorescence excitation-emission matrix (EEM) analysis. The fluorescence ratio Peak 3/Peak 2 was established from well-defined fluorescence peak intensity of humic-like components (Ex/Em: 225 nm/425 nm) and protein-like components (Ex/Em: 230 nm/345 nm). Peak 3/Peak 2 predicted the aromatic characteristics of DOM and their origins in the different natural surface water feeding the different drinking water treatment plants. The drinking water treatment processes confirmed the treatability of DOM using Peak 3/Peak 2 and was well-confirmed by specific UV260 absorbance relative to dissolved organic carbon (DOC) (SUVA) and fluorescence-based indices. Peak 3/Peak 2 was demonstrated to have a strong correlation with SUVA and DOC removal for the water after treatment by coagulation, adsorption, and chlorination. Compared to the humification index and fluorescence index, Peak 3/Peak 2 is better for indicating the DOM composition in terms of treatability. These findings can broaden the use of fluorescence spectroscopy in water treatment applications, by developing the fluorescence ratio to evaluate the performance of drinking water treatment plants.

SPEA2 based on grid density search and elite guidance for multi-objective operation optimization of wastewater treatment process

It is challenging to introduce an optimization method to improve the operation performance of wastewater treatment process (WWTP) on account of its high energy consumption and poor water quality characteristics. In this paper, an improved Strength Pareto Evolutionary Algorithm 2 (SPEA2) based on grid density search and elite guidance (GDSEG-SPEA2) is proposed for multi-objective operation optimization of WWTP. First, the external archive is divided by an improved adaptive grid method to determine the distribution density of the solutions, and a neighborhood circle strategy as well as a mixed perturbation strategy are designed to search the neighborhood of sparse and crowded solutions, resulting in a more uniformly distributed Pareto front. Then, in order to avoid SPEA2 falling into local optimum after adding the neighborhood search strategies, the crossover and mutation operations based on individual information are proposed to generate new individuals. Finally, an elite guidance strategy, in which the poor-performing individuals of the population learn from the best-performing individuals to update their positions, is introduced into the algorithm to improve convergence. The test function verification shows that the proposed algorithm can obtain the Pareto front with better distribution and convergence than other existing algorithms. The operation optimization control experiment of WWTP shows that the proposed algorithm can better purify water quality and reduce more energy consumption on the premise of ensuring that effluent quality meets the discharge standards, which is better than other optimization algorithms in comparison.

Removal of heavy metals, turbidity and coliform from contaminated raw drinking water using Saccharomyces cerevisiae, the Baker's yeast

The efficiency of Saccharomyces cerevisiae (baker's yeast) was investigated for biosorption of Ni, Cu and Al, turbidity and total coliform reduction from raw drinking water and as coagulant aid with alum (aluminum sulphate). Optimization of metal biosorption (at 50 mg/L initial metal ion concentration) using dried baker's yeast was performed using different pH (1–7), bio-sorbent (yeast) dosage (0.02 –2.0 g/L) and contact time (5.0–90 min). S. cerevisiae exhibited high selectivity towards Cu removal compared to Al and Ni at pH 7, 0.5 g yeast dosage and 30 min contact time, which considered the optimum conditions. Concurrently with metal removal bioassay, baker's yeast was tested as a coagulant aid in a parallel assay. Addition of 1.5 mg/L baker's yeast to the optimum dose of alum (24 mg/L) enhanced turbidity removal reaching 95.43% compared to 90% achieved when alum was used alone. Also, total coliform (TC) was reduced by 92.86% from initial density of 280 CFU/100 mL in the raw water when 1.0 mg/L baker's yeast was added to the optimum alum compared to 67.86% with alum alone. Therefore, it is recommended to use baker's yeast as coagulant aid with alum as primary coagulant. Results of the present study confirmed the important role of the baker's yeast for the removal of heavy metals, turbidity and coliform bacteria associated with the raw drinking water at the tested area. Moreover, results exhibited high efficiency of baker's yeast as a coagulant aid in the improvement of the drinking water treatment process performance.

Sequential treatment of surfactant-laden wastewater using low-cost rice husk ash coagulant and activated carbon: Modeling, optimization, characterization, and techno-economic analysis

Treating surfactant-laden wastewater by a sequential coagulation/flocculation (C/F) and adsorption process was investigated. The treatment process performance was modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). C/F using rice husk ash (RHA) coagulant provided 42.3 % surfactant removal at 3.79 g/L RHA dosage, pH 3.6, and 23 min slow-mixing time. For adsorption onto granular activated carbon (GAC), 96.5 % surfactant removal was maintained at 10 g/L GAC and pH = 4.5. The ANN model showed better predictive capabilities for C/F and adsorption than RSM. By applying real wastewater, the combined system effluent contained 367 ± 2.4, 18.4 ± 1.2, 4.2 ± 0.2, and 0.95 ± 0.06 mg/L for TDS, COD, oil-grease, and surfactants, respectively, with almost complete removals of turbidity and TSS. The integrated system exhibited a profitability scenario with 6.3 years payback period. The study output is beneficial for designing full-scale systems of surfactant-laden wastewater treatment and predicting the associated performance under various operating conditions.

Determination of antibiotics during treatment of hospital wastewater using automated solid-phase extraction followed by UHPLC-MS: occurrence, removal and environmental risks

ABSTRACT The extent of removal of pharmaceuticals by African-based wastewater treatment plants (WWTPs) is relatively unknown with various studies observing high concentrations in effluents. This is mainly due to WWTPs still utilising the traditional treatment methods which are known to be less effective. In this study, 15 selected antibiotics (amoxicillin, ampicillin, azithromycin, ciprofloxacin, doxycycline, erythromycin, gentamicin, metronidazole, norfloxacin, ofloxacin, penicillin, sulfamethoxazole, sulfapyridine, tetracycline and trimethoprim) were monitored in wastewater as it goes through sedimentation (primary and secondary), aeration and chlorination stages of a WWTP. Analytical method involved solid-phase extraction followed by liquid chromatographic determination. Removal efficiencies during sedimentation were generally positive with doxycycline achieving 80–95.8%, while negative removal efficiencies were observed for penicillin V (−46.4 to −17.1%) and trimethoprim (−26.2 to −18.9%). The aeration and agitation stage resulted in concentration enhancement for several antibiotics with seven of them ranging between −273 and −15.5%. This stage was responsible for the relatively low overall removal efficiencies in which only 4 antibiotics (doxycycline, tetracycline, ciprofloxacin, and erythromycin) experienced overall removal efficiencies above 50%. The recorded effluent concentrations ranging between 0.0130 and 0.383 ng/mL were translated to low potential for development of antibiotic resistance genes in the receiving environments while ecotoxicity risk was high for only amoxicillin, ampicillin and sulfapyridine. The study has provided an overview of the performance of common wastewater treatment processes in South Africa and hopes that more monitoring and environmental risk data can be made available towards drafting of antibiotic priority lists that cater for Africa.

CO2-Inorganic Carbon Auto-Buffering System for Efficient Ammonium Reclamation Coupled with Valuable Biomass Production in a Euryhaline Microalga Tetraselmis subcordiformis

The performance of microalgae-based wastewater treatment processes for ammonium-N (NH4+-N) removal depends on the maintenance of a favorable pH that is critical for minimizing nitrogen escape in the form of free ammonia (NH3) and preventing high-NH3 or extreme-pH stress. This study developed a CO2-inorganic carbon (CO2-IC) buffering system that automatically stabilized pH with the supply of a carbon source for efficient photosynthetic reclamation of NH4+-N by a euryhaline microalga Tetraselmis subcordiformis. The soluble (NaHCO3) and insoluble (CaCO3 and MgCO3) ICs were compared for this purpose. The pH was well controlled in the range of 6.5~8.5 in the CO2-IC system, which was suitable for the photosynthetic growth of T. subcordiformis. The NH4+-N (100 mg/L) was almost completely removed in three days, with the maximum removal rate of 60.13 mg N/L/day and minimal N escape of 19.65% obtained in the CO2-NaHCO3 system. The CO2-IC system also restricted the release of extracellular organic matter by preventing stress conditions. The CO2-NaHCO3 system enabled the highest “normal” starch production suitable for fermentation, while the CO2-CaCO3/MgCO3 system facilitated high-amylose starch accumulation that was conducive to producing bio-based materials and health-promoting ingredients. The proteins accumulated in T. subcordiformis were of good quality for animal feeds.

Impact of electrochemically generated iron on the performance of an anaerobic wastewater treatment process

Anaerobic treatment of domestic wastewater has the advantages of lower biomass yield, lower energy demand and higher energy recover over the conventional aerobic treatment process. However, the anaerobic process has the inherent issues of excessive phosphate and sulfide in effluent and superfluous H2S and CO2 in biogas. An electrochemical method allowing for in-situ generation of Fe2+ in the anode and hydroxide ion (OH−) and H2 in the cathode was proposed to overcome the challenges simultaneously. The effect of electrochemically generated iron (e‑iron) on the performance of anaerobic wastewater treatment process was explored with four different dosages in this work. The results showed that compared to control, the experimental system displayed an increase of 13.4–28.4 % in COD removal efficiency, 12.0–21.3 % in CH4 production rate, 79.8–98.5 % in dissolved sulfide reduction, 26.0–96.0 % in phosphate removal efficiency, depending on the e‑iron dosage between 40 and 200 mg Fe/L. Dosing of the e‑iron significantly upgraded the quality of produced biogas, showing a much lower CO2 and H2S contents in biogas in experimental reactor than that in control reactor. The results thus demonstrated that e‑iron can significantly improve the performance of anaerobic wastewater treatment process, bringing multiple benefits with the increase of its dosage regarding effluent and biogas quality.

Treatment of antibiotic-containing wastewater with self-suspended algae-bacteria symbiotic particles: Removal performance and reciprocal mechanism

Emerging contaminants such as antibiotics in wastewater have posed a challenge on conventional biological treatment processes. Algae-bacteria symbiotic mode could improve the performance of biological treatment processes. Self-suspended algae-bacteria symbiotic particles (ABSPs) were prepared with Chlorella vulgaris and Bacillus subtilis using the sol-gel method and hollow glass microspheres in this study. The removal effect of nitrogen and phosphorus as well as the feedback mechanism of ABSPs under tetracycline stress were investigated through three-cycles wastewater treatment experiments. The antioxidant enzyme activity and phycosphere extracellular polymeric substance (EPS) content were identified as well. The results indicated that the removal rates of NH4+-N, TP, COD, and tetracycline in the ABSPs group finally reached 96.18%, 95.44%, 81.36%, and 74.20%, respectively, which were higher than the single algae group apparently. The phycosphere EPS content increased by 20.41% and algae cell structure maintained integrity in ABSPs group as compared with that in single algae group. This study demonstrates that the self-suspended ABSPs can improve contaminants removal performance and alleviate the antioxidant stress response of algae through algal-bacterial reciprocity mechanism.

Optimization and technical parametric assessment of a novel triple media rapid sand filter: The effect of height of beds and size of particles

Turbidity is critically important in the drinking water treatment process and optimal performance of a sand filter can reduce it significantly. In this research, the optimization of a rapid sand filter was investigated in COMSOL Multiphysics® to decline the output turbidity. The conventional filter changed to a triple-bed filter. For validation of the large-scale model, an experimental model was used. According to 35 experimental data for surface load rate of 120 and 240 m3/m2.day, the average absolute deviation of outlet turbidity was calculated by nearly 7%. Moreover, 243 experiments were considered based on the different height of beds and dimeter of particles. The optimization results showed that the proposed model could remove the turbidity of 10 NTU in the inlet stream to 0.341 NTU in outlet one. Also, when the bed's porosity reaches about 0.2, the bed undergoes a pressure drop, and modeling calculations get slow, which indicates that the bed becomes filled. Therefore, it could be implemented as an index to estimate the filter's valuable working hours or backwash time.

Investigating machine learning models to predict microbial activity during ozonation–biofiltration

Continuous online monitoring of water treatment process performance is an essential step in ensuring reliable water quality outcomes.