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.

The removal of the trivalent chromium from the leather tannery wastewater: the optimisation of the electro-coagulation process parameters

2011 ◽  
Vol 63 (3) ◽  
pp. 385-394 ◽  
Author(s):  
E. GilPavas ◽  
I. Dobrosz-Gómez ◽  
M. Á. Gómez-García
Keyword(s):  
Current Density ◽  
Oxygen Demand ◽  
Trivalent Chromium ◽  
Biological Oxygen Demand ◽  
Optimal Conditions ◽  
Electrode Gap ◽  
Surface Method ◽  
Response Capacity ◽  
Electro Coagulation ◽  
Parameter Values

The capacity of the electro-coagulation (EC) process for the treatment of the wastewater containing Cr3+, resulting from a leather tannery industry placed in Medellin (Colombia), was evaluated. In order to assess the effect of some parameters, such as: the electrode type (Al and/or Fe), the distance between electrodes, the current density, the stirring velocity, and the initial Cr3+ concentration on its efficiency of removal (%RCr+3), a multifactorial experimental design was used. The %RCr3+ was defined as the response variable for the statistical analysis. In order to optimise the operational values for the chosen parameters, the response surface method (RSM) was applied. Additionally, the Biological Oxygen Demand (BOD5), the Chemical Oxygen Demand (COD), and the Total Organic Carbon (TOC) were monitored during the EC process. The electrodes made of aluminium appeared to be the most effective in the chromium removal from the wastewater under study. At pH equal to 4.52 and at 28°C, the optimal conditions of Cr3+ removal using the EC process were found, as follows: the initial Cr3+ concentration=3,596 mg/L, the electrode gap=0.5 cm, the stirring velocity=382.3 rpm, and the current density=57.87 mA/cm2. At those conditions, it was possible to reach 99.76% of Cr3+removal, and 64% and 61% of mineralisation (TOC) and COD removal, respectively. A kinetic analysis was performed in order to verify the response capacity of the EC process at optimised parameter values.

scholarly journals Optimization of UV-Electroproxone procedure for treatment of landfill leachate: the study of energy consumption

2021 ◽  
Author(s):  
Majid Kermani ◽  
Abbas Shahsavani ◽  
Pegah Ghaderi ◽  
Pooria Kasaee ◽  
Jamal Mehralipour
Keyword(s):  
Optimum Condition ◽  
Ozone Concentration ◽  
Current Density ◽  
Landfill Leachate ◽  
Inorganic Compounds ◽  
Model Fitting ◽  
Quadratic Model ◽  
Process Time ◽  
P Value ◽  
Wide Range

AbstractWith increased population, treatment of solid waste landfill and its leachate is of major concern. Municipal landfill leachate shows variable, heterogeneous and incontrollable characteristics and contains wide range highly concentrated organic and inorganic compounds, in which hampers the application of a solo method in its treatment. Among different approaches, biological treatment can be used, however it is not effective enough to elimination all refractory organics, containing fulvic-like and humic-like substance. In this experimental study, the UV Electroperoxone process as a hybrid procedure has been employed to treat landfill leachate. The effect of various parameters such as pH, electrical current density, ozone concentration, and reaction time were optimized using central composite design (CCD). In the model fitting, the quadratic model with a P-Value less than 0.5 was suggested (< 0.0001). The R2, R2 adj, and R2 pre were determined equal to 0.98,0.96, and 0.91 respectively. Based on the software prediction, the process can remove 83% of initial COD, in the optimum condition of pH = 5.6, ozone concentration of 29.1 mg/l. min, the current density of 74.7 mA/cm2, and process time of 98.6 min. In the optimum condition, 55/33 mM H2O2 was generated through electrochemical mechanism. A combination of ozonation, photolysis and electrolysis mechanism in this hybrid process increases COD efficiency removal up 29 percent which is higher than the sum of separated mechanisms. Kinetic study also demonstrated that the UV-EPP process follows pseudo-first order kinetics (R2 = 0.99). Based on our results, the UV-EPP process can be informed as an operative technique for treatment of old landfills leachates. Graphical abstract

scholarly journals Optimization of Ash Yield from Bicomposite Biomass (Terminalia catappa and Chrysophyllum albidium) Seed Barks with Additive upon Combustion

2021 ◽  
pp. 26-38
Author(s):  
Ebenezer Olujimi, Dada ◽  
Uriel Olamilekan, Awe-Obe ◽  
Kamoru Olufemi, Oladosu ◽  
Abass Olanrewaju, Alade ◽  
Tinuade Jolaade, Afolabi
Keyword(s):  
Optimal Design ◽  
Principal Component ◽  
Volatile Matter ◽  
Proximate Analysis ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Dihydrogen Phosphate ◽  
Ammonium Dihydrogen Phosphate ◽  
Ann Model ◽  
Artificial Neural Network Ann

The ash yield from the combustion of a mixture of Africa star apple and tropical almond seeds shells (biocomposite biomass) with ammonium dihydrogen phosphate as an additive in a furnace was optimized using I-Optimal Design under the Combined Methodology of the Design Expert Software. The data obtained were analysed statistically using Analysis of Variance (ANOVA), Artificial Neural Network (ANN) for the prediction of ash yield and Principal Component Analysis (PCA) to determine the coefficient of determination (R²) between variables. Proximate analysis was used to evaluate Moisture Content (MC), Fixed Carbon Content (FCC), and Volatile Matter (VM) values while the Higher Heating Value (HHV) of the mixtures that gave the highest and lowest ash yields was evaluated numerically. The optimum conditions of process variables for the compositions of tropical almond, African star apple, and ammonium dihydrogen phosphate, as well as the temperature, were 30%, 60%, 10% and 704 oC, respectively leading to a minimum ash yield of 24.8%. The mathematical models for the ash using the I-optimal design indicate a good fit to the Quadratic model with a R² of 0.9999. The ANN model agreed significantly with the experimental results with an R² of 0.9939.  The VM, FCC, MC, AC and HHV of the highest ash yield were 11.00%, 2.34%, 3.20%, 33.80% and 4487.747 , respectively. The study established the suitability of optimisation tool to develop solid fuel mixtures for possible use in grate furnaces and its efficiencies.

scholarly journals Comparative Analysis of Optimum Maintenance Cost for Water Boreholes in South-Eastern Nigeria

2020 ◽  
pp. 22-30
Author(s):  
S. C. Ezekafor ◽  
J. C. Agunwamba
Keyword(s):  
Linear Model ◽  
Operating Cost ◽  
Secondary Data ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Maintenance Cost ◽  
Anambra State ◽  
Optimal Maintenance ◽  
Cost Parameters ◽  
The Cost

Aims: This study considered the optimal maintenance cost of water boreholes for a multiple pumping scenario in South-East Nigeria. The objective of the study was to determine the optimal maintenance cost of water boreholes for the Southeastern region of Nigeria. Place and Duration of Study: The data used in this study is secondary data obtained from selected community and commercial boreholes in the Abia State, Anambra State, Ebonyi State, Enugu State and Imo State. Methodology: The linear model and the quadratic model were used to determine the cost parameters for the optimization of the maintenance cost of the water boreholes. Results: The result of the study showed that the maintenance cost obtained for Anambra State is more adequate than the other states since it recorded a higher coefficient of determination value using the linear and quadratic model. Also, it was found the quadratic model was better than the linear model for the estimation of cost parameters used for determining the optimum maintenance cost of water boreholes in South-East Nigeria. The findings showed that the quadratic model recorded the best net savings for Abia, Anambra, and Enugu with the corresponding cost of ₦4,079,246.817, ₦5,198,716.141, and ₦4,767,680.392 respectively. It was found that the linear model recorded the best net savings for Ebonyi and Imo with the corresponding cost of ₦1,593,013.72 and ₦8,692,452.91 respectively. Hence, the quadratic models was identified as the best model for the estimation of cost parameters and enhance the performance of the optimal total operating cost of the water borehole model in South-East Nigeria. Conclusion: The study concludes by recommending the quadratic model for the estimation of cost parameters for the optimization of maintenance cost of water boreholes until further studies prove otherwise.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

scholarly journals Optimization of Betalain Pigments Extraction Using Beetroot by-Products as a Valuable Source

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 50
Author(s):  
Silvia Lazăr (Mistrianu) ◽  
Oana Emilia Constantin ◽  
Nicoleta Stănciuc ◽  
Iuliana Aprodu ◽  
Constantin Croitoru ◽  
...  
Keyword(s):  
Citric Acid ◽  
Acid Concentration ◽  
Ethanol Concentration ◽  
Extraction Process ◽  
Extraction Time ◽  
Quadratic Model ◽  
Citric Acid Concentration ◽  
Operating Variables ◽  
Conventional Solvent Extraction ◽  
Optimized Conditions

(1) Background: This study is designed to extract the bioactive compounds from beetroot peel for future use in the food industry. (2) Methods: Spectrophotometry techniques analyzed the effect of conventional solvent extraction on betalains and polyphenolic compounds from beetroot peels. Several treatments by varying for factors (ethanol and citric acid concentration, temperature, and time) were applied to the beetroot peel samples. A Central Composite Design (CCD) has been used to investigate the effect of the extraction parameters on the extraction steps and optimize the betalains and total polyphenols extraction from beetroot. A quadratic model was suggested for all the parameters analyzed and used. (3) Results: The maximum and minimum variables investigated in the experimental plan in the coded form are citric acid concentration (0.10–1.5%), ethanol concentration (10–50%), operating temperature (20–60 °C), and extraction time (15–50 min). The experimental design revealed variation in betalain content ranging from 0.29 to 1.44 mg/g DW, and the yield of polyphenolic varied from 1.64 to 2.74 mg/g DW. The optimized conditions for the maximum recovery of betalains and phenols were citric acid concentration 1.5%, ethanol concentration 50%, temperature 52.52 °C, and extraction time 49.9 min. (4) Conclusions: Overall, it can be noted that the extraction process can be improved by adjusting operating variables in order to maximize the model responses.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

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.

Comparison of various multivariate models to estimate structural properties by means of non-destructive techniques (NDTs) in Pinus sylvestris L. timber

Holzforschung ◽  
2019 ◽  
Vol 73 (4) ◽  
pp. 331-338
Author(s):  
Antonio Villasante ◽  
Guillermo Íñiguez-González ◽  
Lluis Puigdomenech
Keyword(s):  
Pinus Sylvestris ◽  
Linear Models ◽  
Longitudinal Vibration ◽  
Modulus Of Rupture ◽  
Coefficient Of Determination ◽  
Pullout Resistance ◽  
Density Prediction ◽  
Learning Software ◽  
Artificial Neural Network Ann ◽  
Non Destructive

AbstractThe predictability of modulus of elasticity (MOE), modulus of rupture (MOR) and density of 120 samples of Scots pine (Pinus sylvestrisL.) were investigated using various non-destructive variables (such as time of flight of stress wave, natural frequency of longitudinal vibration, penetration depth, pullout resistance, visual grading and concentrated knot diameter ratio), and based on multivariate algorithms, applying WEKA as machine learning software. The algorithms used were: multivariate linear regression (MLR), Gaussian, Lazy, artificial neural network (ANN), Rules and decision Tree. The models were quantified based on the root-mean-square error (RMSE) and the coefficient of determination (R2). To avoid model overfitting, the modeling was built and the results validated via the so-called 10-fold cross-validation. MLR with the “greedy method” for variable selection based on the Akaike information metric (MLRak) significantly reduced the RMSE of MOR and MOE compared to univariate linear regressions (ULR). However, this reduction was not significant for density prediction. The predictability of MLRak was not improved by any other of the tested algorithms. Specifically, non-linear models, such as multilayer perceptron, did not contribute any significant improvements over linear models. Finally, MLRak models were simplified by discarding the variables that produce the lowest RMSE increment. The resulted models could be even further simplified without significant RMSE increment.

scholarly journals Electrochemical oxidation of sulfamethoxazole using Ti/SnO2-Sb/Co-PbO2 electrode through ANN-PSO

2019 ◽  
Vol 84 (7) ◽  
pp. 713-727 ◽  
Author(s):  
Jiteng Wan ◽  
Chunji Jin ◽  
Banghai Liu ◽  
Zonglian She ◽  
Mengchun Gao ◽  
...  
Keyword(s):  
Aqueous Solution ◽  
Electrochemical Oxidation ◽  
Removal Efficiency ◽  
Current Density ◽  
Ph Value ◽  
Ann Model ◽  
Solution Ph ◽  
Pbo2 Electrode ◽  
Artificial Neural Network Ann ◽  
Major Factors

Even in a trace amounts, the presence of antibiotics in aqueous solution is getting more and more attention. Accordingly, appropriate technologies are needed to efficiently remove these compounds from aqueous environments. In this study, we have examined the electrochemical oxidation (EO) of sulfamethoxazole (SMX) on a Co modified PbO2 electrode. The process of EO of SMX in aqueous solution followed the pseudo-first-order kinetics, and the removal efficiency of SMX reached the maximum value of 95.1 % within 60 min. The effects of major factors on SMX oxidation kinetics were studied in detail by single-factor experiments, namely current density (1?20 mA cm-2), solution pH value (2?10), initial concentration of SMX (10?500 mg L-1) and concentration of electrolytes (0.05?0.4 mol L-1). An artificial neural network (ANN) model was used to simulate this EO process. Based on the obtained model, particle swarm optimization (PSO) was used to optimize the operating parameters. The maximum removal efficiency of SMX was obtained at the optimized conditions (e.g., current density of 12.37 mA cm-2, initial pH value of 4.78, initial SMX concentration of 74.45 mg L-1, electrolyte concentration of 0.24 mol L-1 and electrolysis time of 51.49 min). The validation results indicated that this method can ideally be used to optimize the related parameters and predict the anticipated results with acceptable accuracy.

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