scholarly journals Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression

2020 ◽  
Vol 10 (15) ◽  
pp. 5179
Author(s):  
Meryem Akoulih ◽  
Smail Tigani ◽  
Rachid Saadane ◽  
Amal Tazi
Keyword(s):  
Heavy Metals ◽  
Logistic Regression ◽  
Removal Efficiency ◽  
Chromium Removal ◽  
Refractory Alloys ◽  
Best Value ◽  
Proposed Model ◽  
Auc Value ◽  
Special Steels ◽  
Minimal Area

Surface treatment and tanning industries use huge quantities of heavy metals—especially Chromium (III) and (VI)—in their processes thanks to its physical proprieties. It is used in the composition of special steels and refractory alloys. By dint of using this metal, an enormous quantity of rejects is produced each year and discharged into the oceans. As this is very dangerous for our environment, it is very important to treat these discharges before getting rid of them. This study treats chromium removal as a special type of heavy metals that can be a component of industrial discharges. Electrocoagulation is considered among the best methods used in this kind of treatment. However, it requires a lot of time, energy and remains expensive. This paper presents a predictive model in order to classify the chromium removal efficiency using electrocoagulation method. The proposed model is a logistic regression (LR) that consumes four parameters that we call predictors: pH, time, current, and stirring speed. After the training and validation process, we obtained 88% as classification precision, recall and F-Score metrics values while the use of the 10-Folds cross-validation method gave a minimal area under curve (AUC) value of 97% while the best value attempts 100%. Classification report states that the model performs well comparing to similar experimentation efficiencies.

Hexavalent chromium removal by using synthesis of polyaniline and polyvinyl alcohol

2016 ◽  
Vol 74 (10) ◽  
pp. 2305-2313 ◽  
Author(s):  
Majid Riahi Samani ◽  
Parisa Ebrahimbabaie ◽  
Hamed Vafaei Molamahmood
Keyword(s):  
Heavy Metals ◽  
Polyvinyl Alcohol ◽  
Hexavalent Chromium ◽  
Removal Efficiency ◽  
Chromium Concentration ◽  
Chromium Removal ◽  
Chromium Vi ◽  
Pani Composite ◽  
Stirring Time ◽  
Textile Finishing

Over the past few years, heavy metals have been proved to be one of the most important contaminants in industrial wastewater. Chromium is one of these heavy metals, which is being utilized in several industries such as textile, finishing and leather industries. Since hexavalent chromium is highly toxic to human health, removal of it from the wastewater is essential for human safety. One of the techniques for removing chromium (VI) is the use of different adsorbents such as polyaniline. In this study, composites of polyaniline (PANi) were synthesized with various amounts of polyvinyl alcohol (PVA). The results showed that PANi/PVA removed around 76% of chromium at a pH of 6.5; the PVA has altered the morphology of the composites and increased the removal efficiency. Additionally, synthesis of 20 mg/L of PVA by PANi composite showed the best removal efficiency, and the optimal stirring time was calculated as 30 minutes. Moreover, the chromium removal efficiency was increased by decreasing the pH, initial chromium concentration and increasing stirring time.

scholarly journals Chromium removal from tannery effluents by adsorption process via activated carbon chat stems (Catha edulis) using response surface methodology

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yigezu Mekonnen Bayisa ◽  
Tafere Aga Bullo ◽  
Desalegn Abdissa Akuma
Keyword(s):  
Heavy Metals ◽  
Activated Carbon ◽  
Removal Efficiency ◽  
Cost Effective ◽  
Treatment Method ◽  
Proximate Analysis ◽  
Chromium Removal ◽  
Tannery Effluents ◽  
Chromium Vi ◽  
Before And After

Abstract Objective In tannery processing, water consumption is high, which generates wastewater as a by-product and numerous pollutants such as chromium heavy metals that make adverse effects of water bodies and the surrounding environment. This study analyzed, chromium (VI) removal from wastewater through activated carbon chat stem was investigated. Adsorption is a common treatment method via activated carbon due to its cost-effective, profitable, and removal efficiency of these heavy metals. Results The proximate analysis of moisture content of chat stem has 6%, activated carbon ash content of 17.35%, volatile materials of 20.12%, and fixed carbon contents of 56.53%, which are well-matched the standards quality of activated carbon. As the process parameter varies, the increment of the chromium removal efficiency was from 62.5 to 97.03%. The maximum adsorption efficiency was observed at 30 g/L dosage of the adsorbent, at pH 4, and contact time at 180 min of activated carbon from chat stem waste was found 97.03%. FTIR was used to characterize the surface of the chat stem before and after adsorption. Langmuir and Freundlich are used for short contact time’s adsorption isotherm 0.9839 and 0.9995 respectively, which conformed, no visible change in the corrosion state.

Regression model of assessment of customer solvency and banking risks in the process of lending

2019 ◽  
pp. 69-73
Author(s):  
Zoryna Yurynets ◽  
Rostyslav Yurynets ◽  
Nataliya Kunanets ◽  
Ivanna Myshchyshyn
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Credit Scoring ◽  
Bank Loans ◽  
Management Decisions ◽  
Computer Data ◽  
Proposed Model ◽  
Loan Risk

In the current conditions of economic development, it is important to pay attention to the study of the main types of risks, effective methods of evaluation, monitoring, analysis of banking risks. One of the main approaches to quantitatively assessing the creditworthiness of borrowers is credit scoring. The objective of credit scoring is to optimize management decisions regarding the possibility of providing bank loans. In the article, the scientific and methodological provisions concerning the formation of a regression model for assessing bank risks in the process of granting loans to borrowers has been proposed. The proposed model is based on the use of logistic regression tools, discriminant analysis with the use of expert evaluation. During the formation of a regression model, the relationship between risk factors and probable magnitude of loan risk has been established. In the course of calculations, the coefficient of the individual's solvency has been calculated. Direct computer data preparation, including the calculation of the indicators selected in the process of discriminant analysis, has been carried out in the Excel package environment, followed by their import into the STATISTICA package for analysis in the “Logistic regression” sub-module of the “Nonlinear evaluation” module. The adequacy of the constructed model has been determined using the Macfaden's likelihood ratio index. The calculated value of the Macfaden's likelihood ratio index indicates the adequacy of the constructed model. The ability to issue loans to new clients has been evaluated using a regression model. The conducted calculations show the possibility of granting a loan exclusively to the second and third clients. The offered method allows to conduct assessment of client's solvency and risk prevention at different stages of lending, facilitates the possibility to independently make informed decisions on credit servicing of clients and management of a loan portfolio, optimization of management decisions in banks. In order for a loan-based model to continue to perform its functions, it must be periodically adjusted.

Utilisation of Biomass and Hybrid Biochar from Elephant Grass and Low Density Polyethylene for the Competitive Adsorption of Pb(II), Cu(II), Fe(II) and Zn(II) from Aqueous Media

2020 ◽  
Vol 13 ◽  
Author(s):  
Joshua O. Ighalo ◽  
Lois T. Arowoyele ◽  
Samuel Ogunniyi ◽  
Comfort A. Adeyanju ◽  
Folasade M. Oladipo-Emmanuel ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Removal Efficiency ◽  
Contact Time ◽  
Competitive Adsorption ◽  
Adsorption Process ◽  
Aqueous Media ◽  
Polluted Water ◽  
Batch Adsorption ◽  
Order Kinetic ◽  
Elephant Grass

Background: The presence of pollutants in polluted water is not singularized hence pollutant species are constantly in competition for active sites during the adsorption process. A key advantage of competitive adsorption studies is that it informs on the adsorbent performance in real water treatment applications. Objective: This study aims to investigate the competitive adsorption of Pb(II), Cu(II), Fe(II) and Zn(II) using elephant grass (Pennisetum purpureum) biochar and hybrid biochar from LDPE. Method: The produced biochar was characterised by Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). The effect of adsorption parameters, equilibrium isotherm modelling and parametric studies were conducted based on data from the batch adsorption experiments. Results: For both adsorbents, the removal efficiency was >99% over the domain of the entire investigation for dosage and contact time suggesting that they are very efficient for removing multiple heavy metals from aqueous media. It was observed that removal efficiency was optimal at 2 g/l dosage and contact time of 20 minutes for both adsorbent types. The Elovich isotherm and the pseudo-second order kinetic models were best-fit for the competitive adsorption process. Conclusion: The study was able to successfully reveal that biomass biochar from elephant grass and hybrid biochar from LDPE can be used as effective adsorbent material for the removal of heavy metals from aqueous media. This study bears a positive implication for environmental protection and solid waste management.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

scholarly journals Investigation of Heavy Metal Effects on the Anaerobic Co-Digestion Process of Waste Activated Sludge and Septic Tank Sludge

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Quang-Minh Nguyen ◽  
Duy-Cam Bui ◽  
Thao Phuong ◽  
Van-Huong Doan ◽  
Thi-Nham Nguyen ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Activated Sludge ◽  
Removal Efficiency ◽  
Biogas Production ◽  
Cod Removal ◽  
Septic Tank ◽  
Waste Activated Sludge ◽  
Digestion Process ◽  
Cod Removal Efficiency

The effect of copper, zinc, chromium, and lead on the anaerobic co-digestion of waste activated sludge and septic tank sludge in Hanoi was studied in the fermentation tests by investigating the substrate degradation, biogas production, and process stability at the mesophilic fermentation. The tested heavy metals were in a range of concentrations between 19 and 80 ppm. After the anaerobic tests, the TS, VS, and COD removal efficiency was 4.12%, 9.01%, and 23.78% for the Cu(II) added sample. Similarly, the efficiencies of the Zn(II) sample were 1.71%, 13.87%, and 16.1% and Cr(VI) efficiencies were 15.28%, 6.6%, and 18.65%, while the TS, VS, and COD removal efficiency of the Pb(II) added sample was recorded at 16.1%, 17.66%, and 16.03% at the concentration of 80 ppm, respectively. Therefore, the biogas yield also decreased by 36.33%, 31.64%, 31.64%, and 30.60% for Cu(II), Zn(II), Cr(VI), and Pb(II) at the concentration of 80 ppm, compared to the raw sample, respectively. These results indicated that Cu(II) had more inhibiting effect on the anaerobic digestion of the sludge mixture than Zn(II), Cr(VI), and Pb(II). The relative toxicity of these heavy metals to the co-digestion process was as follows: Cu (the most toxic) > Zn > Cr > Pb (the least toxic). The anaerobic co-digestion process was inhibited at high heavy metal concentration, which resulted in decreased removal of organic substances and produced biogas.

Enhancing the soil heavy metals removal efficiency by adding HPMA and PBTCA along with plant washing agents

2017 ◽  
Vol 339 ◽  
pp. 33-42 ◽  
Author(s):  
Yaru Cao ◽  
Shirong Zhang ◽  
Guiyin Wang ◽  
Ting Li ◽  
Xiaoxun Xu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Removal Efficiency ◽  
Heavy Metals Removal ◽  
Soil Heavy Metals ◽  
Metals Removal

scholarly journals Copper and Lead Ions Removal by Electrocoagulation: Process Performance and Implications for Energy Consumption

2021 ◽  
Vol 10 (3) ◽  
pp. 415-424
Author(s):  
Aji Prasetyaningrum ◽  
Dessy Ariyanti ◽  
Widayat Widayat ◽  
Bakti Jos
Keyword(s):  
Heavy Metals ◽  
Energy Consumption ◽  
Removal Efficiency ◽  
Current Density ◽  
High Current Density ◽  
Energy Requirement ◽  
Lead Ions ◽  
Initial Concentration ◽  
Electroplating Wastewater ◽  
Electrocoagulation Process

Electroplating wastewater contains high amount of heavy metals that can cause serious problems to humans and the environment. Therefore, it is necessary to remove heavy metals from electroplating wastewater. The aim of this research was to examine the electrocoagulation (EC) process for removing the copper (Cu) and lead (Pb) ions from wastewater using aluminum electrodes. It also analyzes the removal efficiency and energy requirement rate of the EC method for heavy metals removal from wastewater. Regarding this matter, the operational parameters of the EC process were varied, including time (20−40 min), current density (40−80 A/m2), pH (3−11), and initial concentration of heavy metals. The concentration of heavy metals ions was analyzed using the atomic absorption spectroscopy (AAS) method. The results showed that the concentration of lead and copper ions decreased with the increase in EC time. The current density was observed as a notable parameter. High current density has an effect on increasing energy consumption. On the other hand, the performance of the electrocoagulation process decreased at low pH. The higher initial concentration of heavy metals resulted in higher removal efficiency than the lower concentration. The removal efficiency of copper and lead ions was 89.88% and 98.76%, respectively, at 40 min with electrocoagulation treatment of 80 A/m2 current density and pH 9. At this condition, the specific amounts of dissolved electrodes were 0.2201 kg/m3, and the energy consumption was 21.6 kWh/m3. The kinetic study showed that the removal of the ions follows the first-order model.

scholarly journals Comparison of sorption efficiency of natural and MnO2 coated zeolite for copper removal from model solutions

2021 ◽  
Vol 900 (1) ◽  
pp. 012003
Author(s):  
M Balintova ◽  
Z Kovacova ◽  
S Demcak ◽  
Y Chernysh ◽  
N Junakova
Keyword(s):  
Heavy Metals ◽  
Removal Efficiency ◽  
Ph Value ◽  
Batch Experiments ◽  
Modified Zeolite ◽  
Optimum Ph ◽  
Removal Of Heavy Metals ◽  
Initial Ph ◽  
Correlation Factors ◽  
Maximum Removal

Abstract Removal of heavy metals from the environment is important for living beings. The present work investigates the applicability of the natural and MnO2 - coated zeolite as sorbent for the removal of copper from synthetic solutions. Batch experiments were carried out to identify the influence of initial pH and concentration in the process of adsorption. A maximum removal efficiency of Cu(II) was observed in 10 mg/L for natural (95.6%) and modified (96.4%) zeolite, where the values was almost identical, but at concentration of 500 mg/L was the removal efficiency of modified zeolite three times higher. Based on the correlation factors R2, the Langmuir isotherms better describe the decontamination process than Freundlich. The optimum pH value was set at 5.0.

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