Continuous water monitoring is expensive and time consuming. Because it requires sampling information throughout 12 months and restricts the conduct of water aid management studies as well as the ...calibration and validation of excellent water models. To overcome this obstacle to better water quality management, improving water quality models is a necessary step. Various modelling strategies have been developed in recent years to improve the accuracy of predictions of major water parameters. In this work, for the prediction of raw water sulfate, we used five machine learning models were considered in this work: artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and decision tree (DT) and ensemble tree (ET). Moreover, the DT model was used to know the influence of the other physicochemical parameters (inputs) on the, and the ET model to improve the DT result and ensure the influence of the other physicochemical parameters on the sulfate. The experimental results indicate that all models were found to be effective in predicting sulfate levels, due to their very high correlation coefficients (close to 1) and very low statistical errors (close to 0); however, the most suitable water quality models were GPR and ANN, as their coefficients and statistical indicators do not show much difference between them. Indeed, the coefficients and the statistical indicators of the GPR model were R = 0.9991, R
2
= 0.9982, R
2
adj
= 0.9978, RMSE = 0.0182, MSE = 0, 0003, MAE = 0.0073 and EPM = 1.5386; while those of the ANN model were: R = 0.9989, R
2
= 0.9978, R
2
adj
= 0.9972, RMSE = 0.0124, MSE = 0.0001, MAE = 0.0083 and EPM = 2.0639. The only difference that favored the GPR model if compared to the ANN was the number of parameters, namely 70 parameters and a very weak loss, 3.3404e-04. In contrast, the ANN model was run with 190 parameters. The model tests (interpolation) confirmed this result, owing to the values of the the correlation coefficient (R = 0.99834) and the coefficient of determination (R
2
= 0.9966), as well as that of statistical indicators (RMSE = 0.0309, MSE = 9.5219e-04, EPM = 3.0267 and MAE = 0.0122). In light of these results it can be concluded that the GPR model is the more efficient to predict sulfate in raw water. Additionally, its ability to deal with missing values, outliers, and the updating ability shows its relevance, which should be kept in the future. This efficiency seems to be due to the fact that the sulfate concentration in the raw water is linked to the physico-chemical characteristics of the environment by non-linear relationships. It is confirmed by a tree and ensemble model decision which provided information on how sulfate reacts with other physicochemical characteristics.
Graphical abstract
Wastewater from the Antibiotical-Saidal pharmaceutical plant (Medéa) was pretreated by coagulation-flocculation using copper sulfate (CuSO4), iron chloride (FeCl3), and mixture of the two salts ...combined in a 1:1 (v/v) ratio in the present study. Response surface methodology (RSM) was used to optimize pH and coagulant dosage as independent variables, while dissolved organic carbon (DOC), absorbance at 254 nm (UV 254), and turbidity were provided as dependent variables in the central composite design (CCD). Then, the databases of the three treatments were combined in a single database to create a general model valid for the three treatments at the same time, and to predict the reduction rates of DOC, UV254, and turbidity, using the Gaussian process regression coupled with the dragonfly optimization algorithm (GPR-DA). To have the best model obtained between RMS and GPR-DA, an experimental validation was carried out after having had the optimal conditions of each type of coagulant, using the multi-objective optimization technique. The results of the experimental validation show the superiority of the GPR-DA model compared to the RSM model. Also, the results show that the mixed coagulant (CuSO4 + FeCl3) obtain better results than CuSO4 or FeCl3 alone with a treatment efficiency equal to 92.68% at pH = 5 and dosage = 600 mg/L, and the reductions in DOC, UV 254 and turbidity are 97.32%, 82.90% and 96.47%, respectively.
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The purpose of the work is to investigate the use of decision tree (DT) enhanced by bootstrap aggregates (Bag) and least-squares boosting (Lsboost) in modeling the organic matter of water according ...to its physicochemical parameters. An entire database of 500 samples of 21 physicochemical parameters, including organic matter, was used to build the DT, DT_Bag, and DT_Lsboost models. Training data (364 data points) is resampled using a bootstrap technique to form different training datasets to train different models. The models built were validated by a dataset of 91 samples. The predicted outputs obtained from the developed DT models are then combined by simple averaging. On the other hand, the data was also boosted with the Lsboost technical aid to increase the strength of a weak learning algorithm. The model trains the first weak learner with equal weight across all data points in the training set, then trains all other weak learners based on the updated weight aimed at the validation result to minimize the squared error medium. Good agreement between the predicted and experimental organic matter concentrations for the DT_Lsboost model was obtained (the correlation coefficient for the validation dataset was 0.9992), followed by the DT-Bag model with a correlation coefficient of 0.9949. The comparison between DT, DT_Bag, and DT_Lsboost revealed the superiority of the DT_Lsboost model (the mean root of the squared errors for the dataset were 0.1295 for the DT_Lsboost, 0.1664 for the DT_Bag, and 0.5444 for the DT). These results show that Lsboost technology dramatically improved the DT model. This result is also confirmed by the results of tests on models (interpolation data of 45 points). It should also be noted that the Bag technique was also very effective in optimizing the DT model, as the results obtained with this technique were very close to the DT_Lsboost model.
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•The concentration of organic matter in the water was predicted using various models.•The decision tree (DT) model was coupled by bootstrap aggregates (Bag) and least-squares boosting (Lsboost) techniques.•Significant Physico-chemical parameters on the organic matter were identified using the decision tree (DT) and the ensemble trees model (DT_Bag and DT_Lsboost).•The decision of tree (DT) and the ensemble trees model made it possible to highlight the significant interactions.•Bootstrap aggregates (Bag) and least-squares boosting (Lsboost) techniques have improved and developed the decision tree model.
In this paper, Terbinafine HCl antifungal powder is dried by vacuum dryer. A further analysis of the dried product by HPLC is compulsory to guarantee its compliance with quality requirements. Twelve ...drying kinetics are obtained, adjusted by 40 mathematical models, and the effective diffusivity coefficients Deff and activation energies Ea are calculated by two approaches, the classical linearization of Fick's second equation of diffusion and Arrhenius equation, and by dragonfly swarm optimization method DA-nlinfit. The second approach delivered predictions 3 times closer to actual measured residual moisture ratio MR. Accordingly, precise estimations are conveyed by the method in terms of Deff varying from (1.41 × 10–9 to 7.22 × 10–9) m2/s, Ea projected between (33.37 and 35.64) kJ/mol, and subsequently more accurate enthalpies ΔH∈ (30.52;33.03)kJ/mol, entropies ΔS∈(−0.31;-0.29)kJ/K.mol and Gibbs free energies ΔG∈(136.77;126.58) kJ/mol. The qualitative analysis of the dry powder gives a better quality in the range 40 60 °C, but above this, the results are not satisfactory.
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•Vacuum drying of Terbinafine HCl antifungal powder in different conditions.•Forty mathematical drying models have been tested and Modified Two Term – IV model showing good estimate parameters.•DA_nlinfit techniques were used for calculating thermodynamic parameters.•The proposed DA_nlinfit techniques reached high accuracy and precision compared to conventional linear approximation.
Monitoring stations have been established to combat water pollution, improve the ecosystem, promote human health, and facilitate drinking water production. However, continuous and extensive ...monitoring of water is costly and time-consuming, resulting in limited datasets and hindering water management research. This study focuses on developing an optimized K-nearest neighbor (KNN) model using the improved grey wolf optimization (I-GWO) algorithm to predict dry residue quantities. The model incorporates 20 physical and chemical parameters derived from a dataset of 400 samples. Cross-validation is employed to assess model performance, optimize parameters, and mitigate the risk of overfitting. Four folds are created, and each fold is optimized using 11 distance metrics and their corresponding weighting functions to determine the best model configuration. Among the evaluated models, the Jaccard distance metric with inverse squared weighting function consistently demonstrates the best performance in terms of statistical errors and coefficients for each fold. By averaging predictions from the models in the four folds, an estimation of the overall model performance is obtained. The resulting model exhibits high efficiency, with remarkably low errors reflected in the values of R, R2, R2ADJ, RMSE, and EPM, which are reported as 0.9979, 0.9958, 0.9956, 41.2639, and 3.1061, respectively. This study reveals a compelling non-linear correlation between physico-chemical water attributes and the content of dry tailings, indicating the ability to accurately predict dry tailing quantities. By employing the proposed methodology to enhance water quality models, it becomes possible to overcome limitations in water quality management and significantly improve the precision of predictions regarding critical water parameters.
Safeguarding drinking water is a major public health and environmental concern because it is essential to human life but may contain pollutants that can cause illness or harm the environment. ...Therefore, continuous research is necessary to improve water treatment methods and guarantee its quality. As part of this study, the effectiveness of coagulation–flocculation treatment using aluminum sulfate (Al2(SO4)3) was evaluated on a very polluted site. Samplings were taken almost every day for a month from the polluted site, and the samples were characterized by several physicochemical properties, such as hydrogen potential (pH), electrical conductivity, turbidity, organic matter, ammonium (NH+4), phosphate (PO43−), nitrate (NO3−), nitrite (NO2−), calcium (Ca2+), magnesium (Mg2+), total hardness (TH), chloride (Cl−), bicarbonate (HCO3−), sulfate (SO42−), iron (Fe3+), manganese (Mn2+), aluminum (Al3+), potassium (K+), sodium (Na+), complete alkalimetric titration (TAC), and dry residue (DR). Then, these samples were treated with Al2(SO4)3 using the jar test method, which is a common method to determine the optimal amount of coagulant to add to the water based on its physicochemical characteristics. A mathematical model had been previously created using the support vector machine method to predict the dose of coagulant according to the parameters of temperature, pH, TAC, conductivity, and turbidity. This Al2(SO4)3 treatment step was repeated at the end of each month for a year, and a second characterization of the physicochemical parameters was carried out in order to compare them with those of the raw water. The results showed a very effective elimination of the various pollutions, with a very high rate, thus demonstrating the effectiveness of the Al2(SO4)3. The physicochemical parameters measured after the treatment showed a significant reduction in the majority of the physicochemical parameters. These results demonstrated that the coagulation–flocculation treatment with Al2(SO4)3 was very effective in eliminating the various pollutions present in the raw water. They also stress the importance of continued research in the field of water treatment to improve the quality of drinking water and protect public health and the environment.
Several drinking water production techniques are being established to respond immediately to the growing needs of the population. The system of air gap membrane distillation (AGMD) is the best ...attractive option for the process of water desalination. This thermal process is characterized by its potential to provide drinking water at low energy costs when combined with solar energy. In this paper, the AGMD brackish water desalination unit potentialities coupled with solar energy were investigated. Ghardaïa of the south region has been considered as the field of our study. Mathematical modeling is investigated by employing MATLAB software to develop the prediction of the permeate flux related to the phenomena of heat and mass transfer. Herein, flat plate solar collectors (SFPC) were exploited as a source for heating saline water through free solar energy conversion. The further model validation of a flat solar collector made it possible for following the instantaneous evolution of the collector outlet temperature depending on the feed water temperature and the flow rate. Furthermore, it is interesting to note that the results prove the possibility to produce water by the solar AGMD process with a maximum permeate flux of 8 kg·m−2·h−1 achieved at 68 °C, a feed temperature. Moreover, gained output ratio (GOR) of the unit of thermal solar desalination was estimated to be about 4.6, which decreases with increasing hot water flow and temperature.
Air gap membrane distillation (AGMD) is a widely utilized technology for producing drinking water due to its low heat loss, high thermal efficiency, and compatibility with solar energy. The ...application of the first and second laws of thermodynamics in energy and exergy analyses provides a comprehensive evaluation of the efficiency of thermal processes. This study aims to examine numerically the energy and exergy performance indicators of a solar AGMD system used for seawater desalination. The simulation was carried out using MATLAB 9.7 software. The total thermal efficiency and overall efficiency of each element in the AGMD system were calculated for various solar field energy outputs, and moreover, a parametric study was conducted. The results indicate that the exergetic efficiency of the AGMD system components was the lowest in the solar field, with the concentrator having the lowest energy efficiency. Additionally, the thermal and exergetic efficiency of the entire solar AGMD system decreases along with the raise of ambient temperature. An additional investigation was conducted to better apprehend the sources of exergy destruction in the solar field. The obtained results from this study can be employed as a guide to reduce exergy destruction in the whole solar AGMD desalination system with recognition of the main sources of irreversibility.
The “excess salt replication process” is a new simple method of fabrication of open-cell metal foam based on the commonly known salt replication method. Porous materials with porosity between 46% and ...66% result when the employed alloy is 25% antimonial lead alloy and when it is 58% to 65% zamak 5. These foams are proposed as structured catalysts instead of packed beds in the treatment of wastewater. The local regimes influencing macroscopic air flow behaviour through these foams are delimited and boundaries are analysed in terms of sample length. Most of the experimental tests in this work exhibited a general trend of air flow in ESR foams dominated by the “strong inertia regime”. It was established that the law governing the unidirectional air flow through these foams was the full cubic law. The permeability and inertia coefficient of five samples with a cell diameter between 2.5 and 4.5 mm were calculated, and an empirical correlation was fitted. The irregular cuboid shape of salt grains used in the ESR foam was the origin of the special cell form of ESR foams leading to an anisotropic ordered porous media. This can explain the macroscopic turbulence of air flow because there were many dead zones present in the corner of each cubic cell, thus causing kinetic energy loss starting at earlier regimes.
In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), ...support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that all models accurately fitted the experimental data, even if the ANN model was slightly above the other models. The SVM model led to almost similar results as the ANN model; the only difference was in the validation phase, since the correlation coefficient was very high and the statistical indicators were very low for the ANN model compared to the SVM model. However, from an economic point of view, the SVM model was more appropriate than the ANN model, since its number of parameters was 22, i.e., almost half the number of parameters of the ANN model (43 parameters), while the results were almost similar in all the data phase. To reduce the economic costs further, the RSM model can also be used, which remained very useful due to its high coefficients related to the number of parameters - only 13. In addition, the statistical indicators of the RSM model remained acceptable.
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