Input Water Quality Research Articles

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3−), Magnesium (Mg2+), Sulphate (SO42−), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment

The Water Quality Index (WQI) is a primary metric used to evaluate and categorize surface water quality which plays a crucial role in the management of fresh water resources. Machine Learning (ML) modeling offers potential insights into water quality index prediction. This study employed advanced ML models to get potential insights into the prediction of water quality index for the Aik-Stream, an industrially polluted natural water resource in Pakistan with 19 input water quality variables aligning them with surrounding land use and anthropogenic activities. Six machine learning algorithms, i.e. Adaptive Boosting (AdaBoost), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), Random Forests (RF), Support Vector Regression (SVR), and Bayesian Regression (BR) were employed as benchmark models to predict the Water Quality Index (WQI) values of the polluted stream to achieve our objectives. For model calibration, 80% of the dataset was reserved for training, while 20% was set aside for testing. In our comparative analyses of predictive models for water quality index, the Gradient Boost (GB) model stood out the fittest for its precision, utilizing a combination of just seven parameters (chemical oxygen demand, total organic carbon, oil & grease, Ammonia- nitrogen, arsenic, nickel and zinc), surpassing other models by achieving better results in both training (R2 = 0.88, RMSE = 7.24) and testing (R2 = 0.85, RMSE = 8.67). Analyzing feature importance showed that all the selected variables, except for NO3 N, TDS and temperature had an impact on the accuracy of the models predictions. It is concluded that the application of machine learning to assess water quality in polluted environments enhances accuracy and facilitates real-time tracking, enabling proactive risk mitigations.

A comprehensive evaluation of private sector investment decisions for sustainable water supply systems using a fuzzy-analytic hierarchy process: A case study of Ha Nam province in Vietnam

This study examined the factors that affect the private sectors' willingness to invest in rural water supply. The study applied a mixed methods approach, including an overview of relevant studies, expert consultation, exploratory factor analysis using SPSS software, and a fuzzy-analytic hierarchy process to identify and evaluate the factors applicable to Ha Nam province in Vietnam. Some factors were distinguished that are significant to private investors’ rural water supply investment decisions, including tax incentive policy, policies to support preferred access to loans and credit, a state risk-sharing mechanism, a mechanism to adjust water price, community support, high community demand for clean water, and input water quality. In addition, the study constructed an investment attractiveness index to evaluate the attractiveness of private sector investment for two typical rural water supply projects in Ha Nam province. This index can be used as a basis for the government to design appropriate incentives to attract investment from private investors and construct an investment attractiveness map.

Assessing and forecasting water quality in the Danube River by using neural network approaches

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0–25), good (26–50), poor (51–75), very poor (76–100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants.The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018–2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Nowcasting of fecal coliform presence using an artificial neural network

At least 2 billion people worldwide use drinking water sources that are contaminated with feces, causing waterborne diseases; poor sanitation, poor hygiene, and unsafe drinking water result in a daily death rate of more than 800 children under 5 years of age from diarrheal diseases. This study shows the feasibility of a novel method to nowcast fecal coliforms' (FC) presence in drinking water sources by applying a multilayer perceptron artificial neuron network (MLP-ANN) model. The model gives a binary answer for FC presence or absence in drinking water sources using a minimum of water quality and geographical parameters, which can be monitored in real-time as predictors with low-cost and in-situ equipment. Using 51,400 samples to train, validate and test the model with temperature, pH, electrical conductivity, turbidity, dissolved oxygen, and total dissolved solids (TDS) as water-quality inputs and the water source type and location (as districts in India) as geographical inputs. The model achieved a total accuracy of 92.8% and a sensitivity of 98.2%, meaning that most FC-contaminated samples were classified correctly. In addition, precision reached 93.1%, meaning that most FC-contamination classifications were actually contaminated. The MLP-ANN performed better than the Linear Regression and K-Nearest Neighbors models, with lower accuracies of 90.2% and 91.0%, respectively. The MLP-ANN model could characterize the water quality geospatially, learn from the parameters whether the water is contaminated by FC, and predict with high accuracy on new testing data. This method can be used as a part of a sensor for FC monitoring and management in water, reducing the time gaps between routine lab testing and thus improving drinking water quality and addressing the SDG 6 targets.

Grade evaluation of black-odorous urban rivers in the Greater Bay Area of China using an improved back propagation (BP) neural network.

With the rapid development of urbanization, the urban water environment is receiving continuous attention. It is necessary to understand water quality in a timely manner and make a reasonable comprehensive evaluation. However, existing black-odorous water grade evaluation guidelines are not sufficient. Understanding the changing situation of black-odorous water in urban rivers is a growing concern, especially in real-world scenarios. In this study, a BP neural network combined with the fuzzy membership degree was applied to evaluate the black-odorous grade of urban rivers in Foshan City, which is within the Greater Bay Area of China. The optimal 4 × 11 × 1 topology structure of the BP model was constructed by taking the dissolved oxygen (DO), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and total phosphorus (TP) concentrations as input water quality indicators. There was almost no occurrence of black-odorous water in the two public rivers outside the region in 2021. Black-odorous water was most significant in 10 urban rivers, with grade IV and grade V occurring over 50% of the time in 2021. These rivers had three features, i.e., parallel with a public river, beheaded, and close proximity to Guangzhou City, the provincial capital of Guangdong. The results of the grade evaluation of the black-odorous water found basically matched those of the water quality assessment. The existence of some inconsistencies between the two systems justified the necessity to expand and extend the number of employed indicators and grades in the present guidelines. The results confirm the capability of the BP neural network combined with the fuzzy-based membership degree in the quantitative grade evaluation of black-odorous water in urban rivers. This study makes a step forward in understanding the grading of black-odorous urban rivers. The findings can provide a reference for local policy-makers regarding the priority of practical engineering projects in prevailing water environment treatment programs.

XAI(eXplainable AI)를 이용한 하수처리 활성오니공정 모델링에 관한 연구

In this paper, to solve the problem of model-based control due to data reliability in the sewage treatment system active sludge process, an explanable artificial intelligence (XAI, eXplainable AI) was applied to implement neural network model for dissolved oxygen in an active sludge process. By applying four reliable items of effluent water quality to explainable artificial intelligence techniques, the input water quality items of the model were determined. Among the explainable artificial intelligence techniques, in the technique using connection strength, the concept of GAN was used to divide it into positive and negative numbers and compete with each other.BRAs a result, the performance of the model was maintained even though the input variables of the model were reduced from 6 to 5. The learning error and evaluation error of the final model were 0.13 and 0.20, respectively. In the case of the learning model, the error increased slightly in the evaluation model compared to the conventional method with 6 input variables, but this difference is almost meaningless in terms of the actual value of DO, so the proposed technique is much more efficient compared to reducing the input variables from 6 to 5.

The application of Bayesian networks to evaluate risks from multiple stressors to water quality of freshwater ecosystems

It is difficult to predict and manage the ecological consequences of multiple water quality stressors on our freshwater systems. This is due to the dynamism of the source-stressor-response relationships and multiple factors including lack of data, complex impact pathways and risks, and uncertainties that are difficult to parameterise. We present a risk-based probability modelling approach using a Bayesian network (BN), to manage multiple water quality stressors at multiple spatial scales. We illustrate the use of this approach, by evaluating the probable ecological effects of altered water quality associated with multiple sources in three case study rivers in South Africa. Water quality and land use activity were used to describe conceptual risk pathways, parameterise the BNs and model the probable consequences of multiple water quality stressors. The BN model demonstrated that the endpoints that were selected for the study reflected the risks associated with the levels of the input water quality variables. The model further demonstrated that the electrical conductivity BN was just as sensitive as the more complex salt model. The BN model was further able to accurately represent risks to all systems irrespective the water quality data base size. This approach can contribute towards more sustainable water resource management.

Prediction modeling of potentially toxic elements' hydrogeopollution using an integrated Q-mode HCs and ANNs machine learning approach in SE Nigeria.

Machine learning techniques have proven to be very useful in environmental and engineering assessments, including water quality studies. This is because they have flexible linear and nonlinear forecasting functions that can efficiently and reliably estimate measurable and continuous variables. The aim of this paper was to classify the water quality and also predict potentially toxic anions (PTAs; e.g., Cl, SO4, HCO3, and NO3) and potentially toxic heavy metals (PTHMs; e.g., Fe, Zn, Ni, Cr, and Pb) in water resources in Ojoto and its surroundings, Nigeria. Q-mode hierarchical clusters (HCs) and artificial neural networks (ANNs) were integrated to achieve the research objectives. Prior to the HCs and ANNs modeling, correlation-, unrotated principal component-, and varimax-rotated factor analyses were performed to flag the level of associations between the input water quality variables. With respect to pH, two water quality cluster groups were identified. However, three and four cluster groups were identified based on the PTAs and PTHMs concentrations, respectively. Four ANN models (two for each group) were used for predicting the PTAs and PTHMs in the waters resources. Using coefficient of determination (R2) and AUC (area under curve) values and direct comparison of parity plots, the performance and accuracy of the ANN models were substantiated. Overall, the results obtained reveal that the proposed ANN models suitably predicted the concentrations of the PTAs and PTHMs. Thus, this paper provides useful information for better monitoring, management, and protection of the water resources. However, more modeling studies are encouraged to validate and/or improve the findings of the current work.

Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China

Abstract This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained with small-scale samples, as indicated by the R2 values of the water quality test results, which were all >0.995. In combination with the Sobol sensitivity analysis method, the contribution degree of the parameterized external conditions as well as the relationship with the water quality were examined, which indicated that TP and TN are primarily related to the external input water quality and flow, while Chl-a is related to inflow (36.42%), TP (26.65%), wind speed (25.89%), temperature (8.38%), thus demonstrating that the governance of Chl-a is more difficult. In general, the accuracy and interpretability of MARS machine learning are more in line with the actual situation, and the use of the Sobol method can save computer calculation time. The results of this research can provide a certain scientific basis for future intelligent management of lake environments.

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen (NO3 -N), Nitrite-Nitrogen (NO2 -N), Phosphate (PO43-), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance (R2test=0.98, RMSEtest=2.99), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy (R2test=0.97, RMSEtest=3.74). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

A novel approach for the formulation of Modified Water Quality Index and its application for groundwater quality appraisal and grading

To overcome the deficiencies of the existing water quality indices, a re-designed quality index based on the Canadian Drinking Water Quality Index was developed as “Modified Water Quality Index” (MWQI) and applied for evaluating the groundwater quality in the drinking water sources of Bommanahalli area in Bangalore. The main modification that was made while developing the MWQI was allocating appropriate weight factors for the critical water quality input parameters. In the process of developing the revised/modified index, 30 groundwater samples were identified in the study area, 10 quality parameters, and the corresponding Bureau of Indian standards with respect to permissible drinking standards for water quality were chosen as the input quality parameters and corresponding benchmarks. This study was carried out both during the pre- and post-monsoon seasons of 2018. The results of the quality analysis interpreted that MWQI in the groundwater resources was 39.71 and 39.10, respectively, in the pre- and post-monsoon seasons. Based on the quality rank, the water quality scenario, is seen to be poor. As per MWQI value, 93.33% of the groundwater samples violate the quality index norms, that is, exceed the prescribed permissible values, failing the test. This study clearly illustrated that the MWQI explained the complete picture of water quality in a simple and extremely reliable manner and hence shows enormous promise for its application and large suitability across the globe.

Fluorescence Spectra Predict Microcystin-LR and Disinfection Byproduct Formation Potential in Lake Water.

Disinfection byproducts (DBPs) and algal toxins can be expensive to monitor and represent significant potential risks to human health. DBPs, including haloacetic acids and trihalomethanes, are possible or probable human carcinogens. Microcystin-LR-produced by cyanobacteria-is linked with various adverse health effects. Here we show that fluorescence spectra predict both microcystin-LR occurrence and DBP formation potential (DBPfp) in lake water. We compared models with either fluorescence spectra or a suite of water quality predictors as inputs. A regularized logistic regression model with fluorescence spectral inputs correctly classified 94% of test data with respect to microcystin-LR occurrence, with a 96% probability of correctly ranking a detect/nondetect pair. Regularized linear regression predicted DBPfp based on fluorescence inputs with a combined R2 of 0.83 on test data. A gradient-boosted classifier with seven water quality inputs was comparable in detecting microcystin-LR (91% correct), as was UV254 in predicting DBPfp (combined test R2 = 0.84), but no single parameter matched fluorescence spectra over both predictive tasks. Results highlight the potential for multiparameter monitoring via fluorescence spectroscopy, extending previous work on predicting DBPs alone. As a high-frequency monitoring tool, this approach could supplement mass spectrometric methods that may only be applicable at low frequency due to resource limitations.

Site-specific differences in microbial community structure and function within the rhizosphere and rhizoplane of wetland plants is plant species dependent

Two types of organisms that are vital to the ecosystem services provided by wetlands are plants and soil microorganisms, more specifically, the bacteria associated with the roots of wetland plants. In order to better understand the relationships between wetland plants and their associated microorganisms, we tested the hypothesis that the microbial rhizosphere and rhizoplane communities in wetland systems highly impacted by anthropogenic activities subjected to poor water quality inputs would differ from those communities in areas less impacted by anthropogenic activity receiving higher water quality inputs. To test this hypothesis we compared structural and functional characteristics of microbial communities from water, rhizosphere and rhizoplane microbial communities associated with three wetland plant species (Iris versicolor, Potamogeton natans and Veronica spicata) at two sites along the Grand River (Ontario Canada) experiencing different levels of anthropogenic impact. West Montrose (WM) is a northern site which has historically been classified as having “good” water quality, while Rare (RA) is a site located in a highly urbanized area receiving effluent from a municipal waste water treatment plant (WWTP). Microbial community composition, assessed using PCR-DGGE, differed between sites, community types and plant species, however, the magnitude of variance between sites was dependent on the associated plant species and the community type. Rhizoplane microbial communities associated with I. versicolor, P. natans, and V. spicata shared 8.1%, 19.9% and 23.9% structural similarity between sites. Rhizosphere microbial communities associated with I. versicolor, P. natans, and V. spicata exhibited 33%, 11.2% and 15.6% structural similarity between sampling locations. Functional community characteristics, determined by BiologTM Ecoplate carbon source utilization profiles exhibited by microbial communities from the rhizoplane and rhizosphere, were unique to each site for I. versicolor and P. natans (rhizoplane community only), but not for V. spicata. Assessment of fecal coliforms and fecal indicator microorganisms (Salmonella, Enterococcus and Escherichia coli) also revealed site-specific differences as well as differences among plant species, most notably the absence of Enterococcus spp. from the rhizosphere of P. natans at both sampling sites. These findings suggest that plant species differ in their ability to recruit root-associated microorganisms, and may be affected differently by variability in environmental conditions, such as site-specific differences in water quality or water chemistry.

Assessment of Environmental Flows from Complexity to Parsimony—Lessons from Lesotho

Over the last decade, Environmental Flow Assessment (EFA) has focused scientific attention around heavily-modified hydrosystems, such as flow regulated releases downstream of dams. In this light, numerous approaches of varying complexity have been developed, the most holistic of which incorporate hydrological, hydraulic, biological and water quality inputs, as well as socioeconomic issues. Finding the optimal flow releases, informing policy and determining an operational framework are often the main focus. This work exhibits a simplification of the DRIFT framework, and is regarded as the first holistic EFA approach, consisting of three modules, namely hydrological, hydraulic and fish quality. A novel conceptual classification for fish quality is proposed, associating fish fauna requirements with hydraulic characteristics, exported by fish survey analyses. The new methodology was applied and validated successfully at three stream sites in Lesotho, where DRIFT was formerly employed.

Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction.

This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO43-, which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R2 ≥ 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.

Application of House of Quality in assessment of seawater pretreatment technologies

Increasing pressure on water resources is leading to a growing reliance on seawater desalination for the provision of freshwater. Pretreatment of seawater is a major step in seawater desalination, which can mitigate problems with reverse osmosis (RO) membranes. No clearly superior pretreatment technology exists, due to the variation in input water quality, and furthermore, the pretreatment must be designed to be functional with input water quality characteristics that vary considerably over time.The objective of this work is to conduct technology assessment by applying a lissome version of HOQ matrix developed in a case study of dissolved air flotation (DAF) system, which is used as water pretreatment for RO desalination systems. The DAF system technology is compared with other pretreatment processes such as ultrafiltration, sedimentation, and granular media filtration. General analysis provides insight to decision makers to help them improve technologies so that they could meet specific demands. HOQ offers a comparative analysis and identifies specific characteristics that must be improved to develop the chosen technology.The HOQ provides a comprehensive analysis for decision making in management, showing that no pretreatment technologies present a superior performance vis-a-vis all customer attributes, however, it identifies the features of the system, to which efforts should be funneled. For DAF system, these are: water quality changes and energy consumption.

Surface analysis of pilot distribution system pipe autopsies: The relationship of organic and inorganic deposits to input water quality

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) surface analysis was conducted to characterise deposits in polyethylene pipes used in a novel pilot water distribution system (PDS). The system consisted of four (4) parallel distribution systems receiving water from different treatment processes, ranging from conventional coagulation through to an advanced membrane filtration system. After two years of operation, the distribution system was shut down and samples of pipe were collected for autopsy analysis. Inlet and outlet samples from each PDS were collected for purpose of comparison. ToF-SIMS was used to assess chemical differences in surface biofilm accumulation and particulate deposition, which resulted as a consequence of the treatment method and operational mode of each system. These data supplemented previously collected bacteriological and chemical water quality data. Results from the inorganic analysis of the pipes were consistent with corrosion and contamination events that occurred upstream in the corresponding treatment systems. Principal component analysis of data on organic constituents showed oxygen and nitrogen containing fragments were associated with the treatment inlet and outlet samples. These types of signals can often be ascribed to biofilm polysaccharides and proteins. A trend was observed when comparing samples from the same PDS, showing an association of lower molecular weight (MW) organic fragments with the inlet and higher MW organic fragments with the outlet samples.

ASSESSMENT OF HOUSEHOLD REVERSE-OSMOSIS SYSTEMS IN HEAVY METAL AND SOLUTE ION REMOVAL IN REAL AND SYNTHETIC SAMPLES

In this study, the efficiency of household reverse-osmosis system (HROS) with and without neutralizer accessory was investigated in both real and synthetic samples. The real samples were collected from rural and urban public drinking-water systems with and without primary refinery treatment. The selected areas were situated in the Kurdistan province, Iran. The HROS model RO100GPD with and without neutralizer accessory was used in all experiments to prevent effects of the membrane used, age of devices, and length of time in service. In order to assess sample quality, some more common physico-chemical analyses consisting of hardness, Ca 2+ , Mg 2+ , total dissolved solids (TDS), electrical conductivity (EC), alkalinity, Cl - , Br - , SO 4 2- , PO 4 3- , NO 3 - , NO 2 - , and heavy metals were performed based on standard methods. The results indicate that HROS and neutralizer accessory have significant effects on the physico-chemical properties of feed water. However, the results indicate the instability of HROS output water quality, but they verify that this instability cannot reduce the output quality. Finally, results emphasize that HROS output water meets standard levels regardless of the input water quality and application of neutralizer accessory.

Statistical Model for Scaling and Corrosion Potentials of Cooling-System Source Waters

Determination of make-up cooling water scaling and corrosion potentials for power plants is essential to prevent cooling system fouling challenges linked to source water quality. In this study, a statistical model is proposed to evaluate the probability that scaling or corrosion could occur. These probabilities were determined from derived distributions for the Langelier saturation index (LSI) and the aggressive index (AI), computed from a fitted joint normal distribution for pH, log[Ca2+], log([Ca2+]+[Mg2+]), and log[Alk]. In most cases only one outcome (scaling or corrosion) exhibited a probability of concern, though highly variable source waters could exhibit significant probability for both. To illustrate application of the method, freshwater and treated municipal wastewater samples collected from the western Pennsylvanian region were analyzed for alkalinity, pH, [Mg2+] and [Ca2+] concentrations. The LSI and AI were calculated using the measured characteristics of the water samples, and their observed distributions were compared to the derived normal distributions for each. This study used the uncertainty associated with water quality inputs to evaluate the uncertainty of scaling and corrosion potentials, and provides a practical approach for power plant operators to evaluate the uncertainty associated with scaling and corrosion indices of cooling waters without use of expensive software or complicated coding.