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 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.

Model for Analyzing the Critical Variables of an Industrial Process Using Fuzzy Inference System and Likert Scale

2021 ◽  
Author(s):  
Carine Nogueira Santino ◽  
Cristiano Hora de Oliveira Fontes ◽  
Jorge Laureano Moya Rodríguez ◽  
Salvador Ávila Filho
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Industrial Process ◽  
Likert Scale ◽  
Inference System

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 pH prediction of a neutral leaching process using adaptive-network-based fuzzy inference system and reaction kinetics

2020 ◽  
Vol 53 (2) ◽  
pp. 11901-11906
Author(s):  
Shuang Long ◽  
Weijian Li ◽  
Wei Yang ◽  
Bei Sun ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Reaction Kinetics ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Leaching Process

scholarly journals Effect of Corrosion Time on the Synergistic Corrosion of Q235 Steel in Sodium Aluminate Solutions

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 753
Author(s):  
Bianli Quan ◽  
Junqi Li ◽  
Chaoyi Chen
Keyword(s):  
Charge Transfer ◽  
Iron Sulfide ◽  
Sodium Aluminate ◽  
Corrosion Products ◽  
Alumina Production ◽  
Steel Electrode ◽  
Aluminate Solution ◽  
Q235 Steel ◽  
Sodium Aluminate Solution ◽  
Corrosion Time

During Bayer alumina production with high-sulfur bauxite, the sulfide ions in the sodium aluminate solution caused serious corrosion to Q235 steel, which is the material of the tank equipment. This study investigates the effect of corrosion time on Q235 steel synergistic corrosion in sodium aluminate solution using the weight-loss method and electrochemical measurements. The results indicate that the corrosion rate decreases sharply, the rate equation satisfies the mathematical model of power function at the initial stage of corrosion, and the transformation of unstable iron sulfide to stable iron oxide at the later stage results in the decrease in sulfur content in the corrosion products and surface pseudo-passivation. There are two main types of corrosion products, as follows: one is the octahedral crystal particle, which is composed of Fe2O3, Fe3O4, Al2O3 and NaFeO2, and the other is the interlayer corrosion between the surface layer and the matrix, which is composed of FeS, FeS2 and MnS2. At day 3, the dynamics of the Q235 steel electrode is controlled by charge transfer and ion diffusion. However, at other times the dynamics are mainly controlled by charge transfer.

scholarly journals Initial Corrosion Behavior of 12Cr1MoV Steel in Thiosulfate-Containing Sodium Aluminate Solution

Metals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1283
Author(s):  
Jingjiu Yuan ◽  
Chaoyi Chen ◽  
Junqi Li ◽  
Bianli Quan ◽  
Yuanpei Lan ◽  
...  
Keyword(s):  
Corrosion Product ◽  
Corrosion Behavior ◽  
Corrosion Current ◽  
Sodium Aluminate ◽  
Corrosion Mechanism ◽  
Alumina Production ◽  
Aluminate Solution ◽  
Corrosion Product Film ◽  
Product Film ◽  
Corrosion Pits

When alumina is produced by the Bayer process with high-sulfur bauxite, the sulfur would strongly corrode the 12Cr1MoV steel made heat exchanger. This study investigated the initial corrosion behavior of the 12Cr1MoV steel exposed to a thiosulfate-containing sodium aluminate (TCSA) solution under the evaporation conditions of alumina production. The obtained corrosion rate equation is V = 6.306·t·exp(−0.71). As corrosion progressed, with the corrosion product film growing, the corrosion current density declines slowly, and the corrosion resistance of the steel is increased. At 1–3 days, the corrosion product film consisted of FeO, Fe2O3, and FeOOH. S2O32− lead to corrosion in local areas of the steel and pits appeared. AlO2− is transformed into Al(OH)3 and filled in the corrosion pits. At 4 and 5 days, Fe3O4 is generated in the outermost layer, and Al(OH)3 is shed from the corrosion pits. The corrosion mechanism of 12Cr1MoV steel in a TCSA solution is proposed based on the experimental results.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

Sign in / Sign up

Export Citation Format

Share Document