scholarly journals Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

2021 ◽  
Author(s):  
Nuno S. Graça ◽  
Ana M. Ribeiro ◽  
Alírio E. Rodrigues
Keyword(s):  
Operating Cost ◽  
Fluoride Concentration ◽  
Fluoride Removal ◽  
Optimal Conditions ◽  
Feed Concentration ◽  
Predictive Capacity ◽  
Electrocoagulation Process ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Intelligence Approach

Abstract An artificial neural network (ANN) with the topology 8-94-85-2 (input – hidden layer 1 - hidden layer 2 - output) was used to model the operation of the continuous electrocoagulation (CEC) process for the removal of fluoride from water. After the ANN training, the sum of the squared errors (MSE) and the determination coefficient (R2) of the testing set model predictions were 0.0088 and 0.999, respectively, showing a good generalization and model's predictive capacity. The optimization of the process cost using the genetic algorithm (GA) showed that the optimal conditions are highly dependent on the feed concentration and the fluoride removal requirements. For a 5 L of water containing 10 mg/L of fluoride, the optimal conditions to reduce the fluoride concentration below the permissible limit (1.5 mg/L) are 88.3 mA of current intensity, a flow rate of 73.6 mL/min, and the use of a series monopolar (SM) electrode configuration, corresponding to a fluoride removal of 85% and an operating cost of 0.05 €/L.

Effects of operational parameters on defluoridation efficiency using electrocoagulation process

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
M. Behbahani ◽  
M.R. Alavi Moghaddam ◽  
M. Arami
Keyword(s):  
Reaction Time ◽  
Anode Material ◽  
Removal Efficiency ◽  
Fluoride Concentration ◽  
Fluoride Removal ◽  
Operational Parameters ◽  
Applied Current ◽  
Initial Ph ◽  
Electrocoagulation Process ◽  
Electrode Connection

The aim of this study is to examine the effect of operational parameters on fluoride removal using electrocoagulation method. For this purpose, various operational parameters including initial pH, initial fluoride concentration, applied current, reaction time, electrode connection mode, anode material, electrolyte salt, electrolyte concentration, number of electrodes and interelectrode distance were investigated. The highest defluoridation efficiency achieved at initial pH 6. In the case of initial fluoride concentration, maximum removal efficiency (98.5%) obtained at concentration of 25mg/l. The increase of applied current and reaction time improved defluoridation efficiency up to 99%. The difference of fluoride removal efficiencies between monopolar and bipolar series and monopolar parallel were significant, especially at reaction time of 5 min. When aluminum used as anode material, higher removal efficiency (98.5%) achieved compared to that of iron anode (67.7%). The best electrolyte salt was NaCl with the maximum defluoridation efficiency of 98.5% compared to KNO3 and Na2SO4. The increase of NaCl had no effect on defluoridation efficiency. Number of electrodes had little effect on the amounts of Al3+ ions released in the solution and as a result defluoridation efficiency. Almost the same fluoride removal efficiency obtained for different interelectrode distances.

scholarly journals Application of Electrocoagulation Process Using Iron and Aluminum Electrodes for Fluoride Removal from Aqueous Environment

2012 ◽  
Vol 9 (4) ◽  
pp. 2297-2308 ◽  
Author(s):  
Edris Bazrafshan ◽  
Kamal Aldin Ownagh ◽  
Amir Hossein Mahvi
Keyword(s):  
Removal Efficiency ◽  
Fluoride Concentration ◽  
Electrical Potential ◽  
Cost Effective ◽  
Fluoride Removal ◽  
Aqueous Environment ◽  
Treatment Rate ◽  
Effective Removal ◽  
Electrocoagulation Process ◽  
Aluminum Electrodes

Fluoride in drinking water above permissible level is responsible for human being affected by skeletal fluorosis. The present study was carried out to assess the ability of electrocoagulation process with iron and aluminum electrodes in order to removal of fluoride from aqueous solutions. Several working parameters, such as fluoride concentration, pH, applied voltage and reaction time were studied to achieve a higher removal capacity. Variable concentrations (1, 5 and 10 mg L-1) of fluoride solutions were prepared by mixing proper amount of sodium fluoride with deionized water. The varying pH of the initial solution (3, 7 and 10) was also studied to measure their effects on the fluoride removal efficiency. Results obtained with synthetic solution revealed that the most effective removal capacities of fluoride could be achieved at 40 V electrical potential. In addition, the increase of electrical potential, in the range of 10-40 V, enhanced the treatment rate. Also comparison of fluoride removal efficiency showed that removal efficiency is similar with iron and aluminum electrodes. Finally it can be concluded that the electrocoagulation process has the potential to be utilized for the cost-effective removal of fluoride from water and wastewater.

Decolorization of Reactive Black B from wastewater by electro-coagulation: optimization using multivariate RSM and ANN

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Kajal Gautam ◽  
Rishi K. Verma ◽  
Suantak Kamsonlian ◽  
Sushil Kumar
Keyword(s):  
Current Density ◽  
Operating Cost ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Process Time ◽  
Mathematical Tool ◽  
Electrode Gap ◽  
Artificial Neural Network Ann ◽  
Electro Coagulation ◽  
Optimized Conditions

AbstractThe present study is aimed to model and optimize the electrocoagulation (EC) process with five important parameters for the decolorization of Reactive Black B (RBB) from simulated wastewater. A multivariate approach, response surface methodology (RSM) together with central composite design (CCD) is used to optimize process parameters such as pH (5–9), electrode gap (0.5–2.5 cm), current density (2.08–10.41 mA/cm2), process time (10–30 min), and initial dye concentration (100–500 mg/l). The predicted percentage decolorization of dye is obtained as 97.21% at optimized conditions: pH (6.8), gapping (1.3 cm), current density (8.32 mA/cm2), time (23 min), and initial dye concentration (200 mg/L), which is very close to experimental percent decolorization (98.41%). The statistical analysis of variance (ANOVA) is performed to evaluate the quadratic model (RSM), and shows good fit of experimental data with coefficient of determination R2 >0.93. An Artificial Neural Network (ANN) is also used to predict the percentage decolorization and gives overall 94.96% which shows performance accuracy between the predicted and actual value of decolorization. The additional considerations of operating cost and current efficiency are also taken care to show the efficacy of EC process with mathematical tool. The sludge characteristics are determined by FE-SEM/EDX.

scholarly journals User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 585
Author(s):  
Fabio Luis Marques dos Santos ◽  
Paolo Tecchio ◽  
Fulvio Ardente ◽  
Ferenc Pekár
Keyword(s):  
Internal Combustion Engine ◽  
User Behavior ◽  
Choice Model ◽  
Combustion Engine ◽  
Stated Preference ◽  
Operating Cost ◽  
Internal Combustion ◽  
Ann Model ◽  
Purchase Cost ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) model that simulates user’s choice of electric or internal combustion engine automotive vehicles based on basic vehicle attributes (purchase price, range, operating cost, taxes due to emissions, time to refuel/recharge and vehicle price depreciation), with the objective of analyzing user behavior and creating a model that can be used to support policymaking. The ANN was trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric automotive vehicle attributes. Model results show that the electric vehicle parameters (especially purchase cost, range and recharge times), as well as the purchase cost of internal combustion engine vehicles, have the most influence on consumers’ vehicle choices. A graphical interface was created for the model, to make it easier to understand the interactions between different attributes and their impacts on consumer choices and thus help policy decisions.

Modelling of fluoride removal via batch monopolar electrocoagulation process using aluminium electrodes

2017 ◽  
Author(s):  
N. Amri ◽  
M. I. Hashim ◽  
N. Ismail ◽  
F. S. Rohman ◽  
N. A. A. Bashah
Keyword(s):  
Fluoride Removal ◽  
Electrocoagulation Process

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

scholarly journals Characterisation of smectite-rich clay soil: Implication for groundwater defluoridation

2016 ◽  
Vol Volume 112 (Number 11/12) ◽  
Author(s):  
Rabelani Mudzielwana ◽  
Mugera W. Gitari ◽  
Titus A.M. Msagati ◽  
◽  
◽  
...  
Keyword(s):  
South Africa ◽  
Rural Areas ◽  
Contact Time ◽  
Clay Soil ◽  
Dental Fluorosis ◽  
Fluoride Concentration ◽  
Limpopo Province ◽  
World Health ◽  
Fluoride Removal ◽  
X Ray

Abstract Groundwater is a widely used and affordable source of drinking water in most of the rural areas of South Africa. Several studies have indicated that groundwater in some boreholes in South Africa has a fluoride concentration above the level recommended by the World Health Organization (1.5 mg/L). Fluoride concentrations above the permissible limit (>1.5 mg/L) lead to dental fluorosis, with even higher concentrations leading to skeletal fluorosis. In the present work, we evaluate the application of smectite-rich clay soil from Mukondeni (Limpopo Province, South Africa) in defluoridation of groundwater. The clay soil was characterised by mineralogy using X-ray diffraction, by elemental composition using X-ray fluorescence and by morphology using scanning electron microscopy. Surface area and pore volume was determined by the Brunauer–Emmett–Teller surface analysis method. Cation exchange capacity and pHpzc of the soil were also evaluated using standard laboratory methods. Batch experiments were conducted to evaluate and optimise various operational parameters such as contact time, adsorbent dose, pH and initial adsorbate concentration. It was observed that 0.8 g/100 mL of smectite-rich clay soil removed up to 92% of fluoride from the initial concentration of 3 mg/L at a pH of 2 with a contact time of 30 min. The experimental data fitted well to a Langmuir adsorption isotherm and followed pseudo second order reaction kinetics. Smectite-rich clay soil showed 52% fluoride removal from field groundwater with an initial fluoride concentration of 5.4 mg/L at an initial pH of 2 and 44% removal at a natural pH of 7.8. Therefore smectite-rich clay soil from Mukondeni has potential for application in defluoridation of groundwater. Chemical modification is recommended to improve the defluoridation capacity.

scholarly journals Biosorption of Fluoride from Synthetic and Ground Water Using Chlorella vulgaris Immobilized in Calcium Alginate Beads in an Upflow Packed Bed Column

2017 ◽  
Vol 61 (3) ◽  
pp. 188 ◽  
Author(s):  
Poornima G. Hiremath ◽  
Thomas Theodore
Keyword(s):  
Experimental Data ◽  
Ground Water ◽  
Chlorella Vulgaris ◽  
Water Samples ◽  
Packed Column ◽  
Fluoride Concentration ◽  
Fixed Bed ◽  
Fluoride Removal ◽  
Fluoride Ions ◽  
Fluoride Adsorption

The potential of immobilized Chlorella vulgaris to remove fluoride from synthetic and real ground water samples in a fixed bed was investigated. The effect of important kinetic parameters including column bed height, feed flow rate and influent fluoride concentration of solution on fluoride removal was studied. Thomas, Yoon-Nelson, and BDST models were used to analyze the experimental data and understand the influence on biosorption performance. The models’ predictions were in good agreement with the experimental data for all the process parameters studied, indicating that the models were suitable for fixed-bed column design. Fluoride adsorption was reversible. Desorption of fluoride ions was accomplished by pumping 0.1 N HCl solution. The reusability of adsorbent was studied by subjecting column to repeated cycles of fluoride adsorption and desorption. The suitability of immobilized C. vulgaris adsorbent for fluoride removal from ground water samples of Pavagada taluk, Tumakuru district was studied in the packed column.

scholarly journals Column Efficiency of Fluoride Removal Using Quaternized Palm Kernel Shell (QPKS)

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Ayu Haslija Abu Bakar ◽  
Luqman Chuah Abdullah ◽  
Nur Amirah Mohd Zahri ◽  
Ma’an Alkhatib
Keyword(s):  
Fluoride Concentration ◽  
Fixed Bed ◽  
Palm Kernel ◽  
Fluoride Removal ◽  
Trimethylammonium Chloride ◽  
Transfer Resistance ◽  
Adsorption Models ◽  
Palm Kernel Shell ◽  
Bed Height ◽  
Fixed Bed Adsorption

In this research, the adsorption potential of quaternized palm kernel shell (QPKS) to remove F− from aqueous solution was investigated using fixed-bed adsorption column. Raw palm kernel shell waste was reacted with 3-chloro-2-hydroxypropyl trimethylammonium chloride (CHMAC) in order to modify the surface charge. The effects of inlet F− concentrations (2–12 mg/l) and QPKS bed height (2–10 cm) with optimum pH (pH = 3) on the breakthrough characteristics of the adsorption system were determined. In the fixed-bed column, breakthrough time increases with increasing bed height due to increasing amount of active site on adsorbents to adsorb the fluoride ion. Decreasing trend of breakthrough values was obtained with increasing initial fluoride concentration due to greater driving force for the transfer process to overcome the mass transfer resistance in the column. The adsorptions were fitted to three well-established fixed-bed adsorption models, namely, Thomas, Yoon–Nelson, and Adams–Bohart models. The results fitted well to the Thomas and Yoon–Nelson models with correlation coefficient, R2 ≥ 0.96.

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