scholarly journals Artificial neural network prediction of the aluminum extraction from bauxite in the Bayer process

2012 ◽  
Vol 77 (9) ◽  
pp. 1259-1271
Author(s):  
Isidora Djuric ◽  
Ivan Mihajlovic ◽  
Zivan Zivkovic
Keyword(s):  
Statistical Modeling ◽  
Linear Regression Analysis ◽  
Sodium Aluminate ◽  
Multiple Linear Regression Analysis ◽  
Modeling Tools ◽  
Alumina Production ◽  
Leaching Process ◽  
Artificial Neural ◽  
Input Variables ◽  
Degree Of Recovery

This paper presents the results of statistical modeling of the bauxite leaching process, as part of Bayer technology for an alumina production. Based on the data, collected during the period between 2008 - 2009 (659 days) from the industrial production in the alumina factory Birac, Zvornik (Bosnia and Herzegovina), the statistical modeling of the above mentioned process was performed. The dependant variable, which was the main target of the modeling procedure, was the degree of Al2O3 recovery from boehmite bauxite during the leaching process. The statistical model was developed as an attempt to define the dependence of the Al2O3 degree of recovery as a function of input variables of the leaching process: composition of bauxite, composition of the sodium aluminate solution and the caustic module of the solution before and after the leaching process. As the statistical modeling tools, Multiple Linear Regression Analysis (MLRA) and Artificial Neural Networks (ANNs) were used. The fitting level, obtained by using the MLRA, was R2 = 0.463, while ANN resulted with the value of R2 = 0.723. This way, the model, defined by using the ANN methodology, can be used for the efficient prediction of the Al2O3 degree of recovery as a function of the process inputs, under the industrial conditions of the alumina factory Birac, Zvornik. The proposed model also has got a universal character and, as such, is applicable in other factories practicing the Bayer technology for alumina production.

scholarly journals ANFIS based prediction of the aluminum extraction from boehmite bauxite in the Bayer process

2014 ◽  
Vol 16 (1) ◽  
pp. 103-109 ◽  
Author(s):  
Ivan Mihajlović ◽  
Isidora Đurić ◽  
Živan Živković
Keyword(s):  
Statistical Modeling ◽  
Fuzzy Inference ◽  
Industrial Process ◽  
Sodium Aluminate ◽  
Alumina Production ◽  
Inference System ◽  
Aluminate Solution ◽  
Leaching Process ◽  
Efficient Prediction ◽  
Degree Of Recovery

Abstract This paper presents the results of nonlinear statistical modeling of the bauxite leaching process, as part of Bayer technology for alumina production. Based on the data, collected during the year 2011 from the industrial production in the alumina factory Birač, Zvornik (Bosnia and Herzegovina), nonlinear statistical modeling of the industrial process was performed. The model was developed as an attempt to define the dependence of the Al2O3 degree of recovery as a function of input parameters of the leaching process: content of Al2O3, SiO2 and Fe2O3 in the bauxite, as well as content of Na2Ocaustic and Al2O3 in the starting sodium aluminate solution. As the statistical modeling tool, Adaptive Network Based Fuzzy Inference System (ANFIS) was used. The model, defined by the ANFIS methodology, expressed a high fitting level and accordingly can be used for the efficient prediction of the Al2O3 degree of recovery, as a function of the process inputs under the industrial conditions.

scholarly journals Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Andres Mauricio Munar ◽  
José Rafael Cavalcanti ◽  
Juan Martin Bravo ◽  
David Manuel Lelinho Da Motta Marques ◽  
Carlos Ruberto Fragoso Júnior
Keyword(s):  
Linear Regression ◽  
Spatial Heterogeneity ◽  
Multiple Linear Regression ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Accurate Estimation ◽  
Shallow Waters ◽  
Chl A ◽  
Input Variables

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

scholarly journals Statistical modeling of copper losses in the silicate slag of the sulfide concentrate smelting process

2015 ◽  
Vol 17 (3) ◽  
pp. 62-69 ◽  
Author(s):  
Marija V. Savic ◽  
Predrag B. Djordjevic ◽  
Ivan N. Mihajlovic ◽  
Zivan D. Zivkovic
Keyword(s):  
Statistical Modeling ◽  
Fuzzy Inference ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Smelting Process ◽  
Silicate Slag ◽  
Inference System ◽  
Final Model ◽  
Sulfide Concentrates ◽  
Copper Losses

Abstract This article presents the results of the statistical modeling of copper losses in the silicate slag of the sulfide concentrates smelting process. The aim of this study was to define the correlation dependence of the degree of copper losses in the silicate slag on the following parameters of technological processes: SiO2, FeO, Fe3O4, CaO and Al2O3 content in the slag and copper content in the matte. Multiple linear regression analysis (MLRA), artificial neural networks (ANNs) and adaptive network based fuzzy inference system (ANFIS) were used as tools for mathematical analysis of the indicated problem. The best correlation coefficient (R2 = 0.719) of the final model was obtained using the ANFIS modeling approach.

scholarly journals Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors

2021 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Eunseo Kwon ◽  
Sungwon Jung ◽  
Jaewook Lee
Keyword(s):  
Environmental Factors ◽  
Linear Regression Analysis ◽  
Model Development ◽  
Multiple Linear Regression Analysis ◽  
Target Area ◽  
Ann Model ◽  
Factors Affecting ◽  
Crime Prediction ◽  
Model Training ◽  
Input Variables

Crime prediction research using AI has been actively conducted to predict potential crimes—generally, crime locations or time series flows. It is possible to predict these potential crimes in detail if crime characteristics, such as detailed techniques, targets, and environmental factors affecting the crime’s occurrence, are considered simultaneously. Therefore, this study aims to categorize theft by performing k-modes clustering using crime-related characteristics as variables and to propose an ANN model that predicts the derived categorizations. As the prediction of theft types allows people to estimate the features of the possibly most frequent thefts in random areas in advance, it enables the efficient deployment of police and the most appropriate tactical measures. Dongjak District was selected as the target area for analysis; thefts in the district showed four types of clusters. Environmental factors, representative elements affecting theft occurrence, were used as input data for a prediction model, while the factors affecting each cluster were derived through multiple linear regression analysis. Based on the results, input variables were selected for the ANN model training per cluster, and the model was implemented to predict theft type based on environmental factors. This study is significant for providing diversity to prediction methods using ANN.

scholarly journals The relationship of economic variables and final energy consumption: multiple linear regression evidence

2018 ◽  
Vol 189 ◽  
pp. 10025
Author(s):  
L Abdullah ◽  
W H Leong
Keyword(s):  
Energy Consumption ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Economic Variables ◽  
Final Energy ◽  
Final Energy Consumption ◽  
Input Variables ◽  
Influential Variable

Energy consumption in developing countries is sharply increasing due to higher economic growth of industrialization along with population growth and urbanization. This paper provides a multiple linear regression evidence to illustrate the association between final energy consumption and three economic variables. Multiple linear regression analysis was used to obtain a predictive equation and to check for linearity assumption. Three input variables, viz. growth domestic product, population, and tourism are the predictors for final energy consumption. Time series yearly data of final energy consumption and the three input variables for the year 2001 until 2012 was retrieved from various databases. It is found that there was a significant variation in the final energy consumption explained by the three variables. The multiple linear regression equation indicates that the ‘population’ is the most influential variable in predicting final energy consumption.

Pengaruh Motivasi Kerja Dan Lingkungan Kerja Terhadap Kinerja Karyawan Bappeda Kabupaten Wonosobo Tahun 2013

2017 ◽  
Vol 1 (2) ◽  
pp. 135-147
Author(s):  
Ary Sutrischastini ◽  
Ratna Setyani
Keyword(s):  
Work Environment ◽  
Work Motivation ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Test Validity ◽  
Multiple Linear Regression Analysis ◽  
Reliability Testing ◽  
Evaluation Influence ◽  
Environment Variables ◽  
Research Goal

This research goal is to identification and evaluation influence of work motivation and work environment to employee’s performance in BAPPEDA Kabupaten Wonosobo. The object of this research is 37 employees of Badan Perencanaan Pembangunan Kabupaten Wonosobo. And the location of this research is at Badan Perencanaan Pembangunan Kabupaten Wonosobo. The analysis used is test validity, reliability testing, and test the hypothesis, with the help of the computer program SPSS version 17, using multiple linear regression analysis. Based on calculations of data and analysis used, the regression equation is obtained: Y = 11.733 + 0.320 X1 +0.334 X2 + ε, by using the equation regression analytical method can conclude that (X1) take effect positively against employees performance. With t value in amount of 2,219 (bigger than t in table in amount of 1,690) and significance value in amount of 0,33. By applying significance limited value in amount of 0,05, it means, hypothesis that claim if work motivation take effect against employees performance can be accepted. There is a positive and significant correlation between work environment variables (X2) against employees. With t value in amount of 2,219 (bigger than t in table in amount of 1,690) and significance value in amount of 0,33 (smaller than 0,5). Simultaneously, work motivation take effect positively and significantly against employees performance with the F value in amount of 11,562 (bigger than 0.05), then obtained significance value 0.000. It can be concluded that the work motivation and work environment has a positive and significant influence on employee performance in BAPPEDA Kabupaten Wonosobo.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

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