Optimization of Saprolite Ore Composites Reduction Process Using Artificial Neural Network (ANN)

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Procedia Computer Science 161 (2019) 424–432

The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019

Optimization of Saprolite Ore Composites Reduction Process Using Optimization of Saprolite Ore Composites Reduction Process Using Artificial Neural Network (ANN) Artificial Neural Network (ANN)

Angella Natalia Ghea Puspitaaa, Isti Surjandaria,a,*, Zulkarnainaa, Adji Kawigrahabb, Nur Vita b Angella Natalia Ghea Puspita , Isti Surjandari *, Zulkarnain , Adji Kawigraha , Nur Vita Permatasari b Permatasari Industrial Engineering Department, Faculty of Engineering, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia Center of aMineral Resources Technology, Agency for Assesment and Application of Indonesia, TechnologyKampus (BPPT),UI, Puspiptek, Serpong, 15314, Indonesia Industrial Engineering Department, Faculty of Engineering, Universitas Depok, 16424, Indonesia b Center of Mineral Resources Technology, Agency for Assesment and Application of Technology (BPPT), Puspiptek, Serpong, 15314, Indonesia a

b

Abstract Abstract Indonesia is the third largest country that has laterite reserves. The potential for resources and nickel ore reserves is quite large in Indonesia thenickel third content largest country has laterite potential for resources and nickel ore reserves is quite large in Indonesia,isbut in naturethat is very small. reserves. One type The of laterite nickel ore is saprolite ore, which has small Fe and large Ni content but (around 1.5content - 2.5%). accordance with the Law No.of4laterite of 2009nickel concerning Mineral and Mining in increasing the Indonesia, nickel in In nature is very small. One type ore is saprolite ore,Coal which has small Fe and large Ni content 1.5ore, - 2.5%). In accordance with and the Law 4 ofore. 2009 concerning Mineral and Coal Mining intechnologies increasing the added value(around of nickel it is necessary to process refineNo. nickel One of the nickel ore processing/refining is added of nickel ore,technique. it is necessary to process and refine nickel ore.high Onetemperature of the nickeland orelarge processing/refining technologies is throughvalue Pyrometallurgy Pyrometallurgy technique involves energy. The reduction process is one ofPyrometallurgy the nickel ore processing process using the Pyrometallurgy In addition to theenergy. reduction theprocess use of through technique. Pyrometallurgy technique involvestechnique. high temperature and large Theprocess, reduction is one of thewhich nickelare oremixing processing processore, using theadditive Pyrometallurgy technique. Inimportant addition torole. the There reduction process,factors the usethat of composites of saprolite coal, and bentonite has an are several composites which are mixing of in saprolite ore,orecoal, additive and importantprocess role. There are several factors that influence the reduction process saprolite composites. Thebentonite results ofhas thisanreduction are analyzed using X RayDifferencethe Fluorescence (XRF). The objectiveore of composites. this research The is to results obtain an factor combination the reduction process influence reduction process in saprolite of optimal this reduction process are of analyzed using X RayDifference (XRF). The objective of research is to obtain an and optimal factor combination of study the reduction of saproliteFluorescence ore composites, which is important to this develop effective, efficient systematic methods. This utilises aprocess neural of saprolite ore composites, which isoptimal important to develop effective, and systematic methods. Thisprocess study utilises a neural network approach that will produce factors with the estimateefficient on the composition in the reduction of saprolite ore Ca10P2 with composites. The optimal factor combination is a coalwith ratio 15% with type of additiveinCathe network approach that will produce optimal factors theofestimate onathe composition reduction processSB of15saprolite ore 2SO 4 or Composite 0 factor combination is a coal ratio of 15% with a type of additive Ca SO or Composite SB Ca P with optimal C and time duration of 3 hours. acomposites. temperatureThe of 1200 2 4 15 10 2 a temperature of 1200 0C and time duration of 3 hours. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee The FifthInformation Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Optimization; Saprolite Ore; Reduction Process; Tube Furnace; Artificial Neural Network Keywords: Optimization; Saprolite Ore; Reduction Process; Tube Furnace; Artificial Neural Network

* Corresponding author. Tel.: +62-21-788-888-05; fax: +62-21-788-856-56. E-mail address:author. [email protected] * Corresponding Tel.: +62-21-788-888-05; fax: +62-21-788-856-56. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.141

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1. Introduction Indonesia is the third largest country that has laterite reserves after New Caledonia and Philippines [1]. The potential for resources and nickel ore reserves in Indonesia which are concentrated on the islands of Sulawesi, Maluku, and Papua are quite large, but the nickel content contained in nature is very small. The laterite nickel ore is generally divided into 2 types of ore, namely limonite ore, which has large Fe content and small Ni (around 0.8 – 1.5%) and saprolite ore, which has a small Fe content and large Ni (around 1.5% - 2.5%) [2]. In accordance with the law No. 4 of 2009 concerning Mineral and Coal Mining increasing the added value of Nickel ore, it is necessary to process and refine nickel ore. One of the nickel ore processing/refining technologies is through a Pyro metallurgy technique. The pyro metallurgy technique involves high temperature and large energy [3]. The use of pellet composites that do not require cooking and sintering plants causes the process and equipment needed to produce hot metal simpler than the use of conventional pellets and can minimize the use of gas. Therefore, the use of composites leads to a better process [4]. In addition to composite, the reduction process is one of the nickel ore processing process using the Pyro metallurgy technique and has an important role in making a nickel. There are several factors that influence the reduction process in saprolite ore composites. All of the parameters, the availability of carbon along with processing time and temperature is the main of concern by finding the effects of the coal ratio, process temperature, and time duration in the reduction process [5]. In addition to the coal ratio parameters, process temperature, and time duration in the reduction process, the ratio of additives (Na2SO4) [6] is also used as parameter for the composite reduction process. The composite pellet reduction process is carried out in Tube Furnace that can reach a temperature of 1600 0C. The composite will be reduced in a Tube Furnace with a temperature increase of 10 0C/minute. The results of this reduction process that analyzed using X-Ray-Difference Fluorescence (XRF). Artificial Neural Network (ANN) is a common method that is usually used to solve multi-objective and multifactor problems. To reduce time consuming and costly target, ANN combines relationship between experimental parameters and output targets [7]. In various studies ANN is used to estimate the response surface. The ANN method has complex relationship modeling, especially in nonlinear equations which are obtained without complex equations. In addition, ANN analysis is very flexible on the number and the shape of its experimental data, which allows an experimental design to be more informal that statistical approaches and has better predictive power than regression [8]. ANN is also the key technique of machine learning (ML) and artificial general intelligence (AGI) and has been successfully used in function approximation, feature selection, pattern recognition and more complex tasks is ANN [9]. ANN technique has been developed rapidly, and in this context, more attention has been paid to neural network technique in the field of mineral processing, especially in the pyro metallurgy technique that will produce optimal factors with the estimates on the composition in the reduction process of saprolite ore composites. The ANN can be utilized to set up a complex, multidimensional non-linear model for optimization factor composition reduction process of saprolite ore composites. Sabilla et al. used Artificial Neural Network method to estimate the concentration of a gas in the air based on the ratio for Electric Nose [10]. The objective of this research is to obtain optimal factor combination reduction process of saprolite ore composites in Tube Furnace by looking at the results of the chemical compositions of Ni which was tested through XRF using Artificial Neural Networks (ANN) method. Nomenclature ANN XRF MSE X1 X2 X3 X4 H1 Y1

Artificial Neural Network X-Ray Difference Fluorescence Mean Square Error parameter of % coal ratio parameter of temperature parameter of duration time parameter of type of additive hidden network response factor of % mass Ni

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2. Literature review 2.1. Saprolite ore composites Saprolite ore composites are produced by mixing saprolite, coal, additives, and bentonite with a predetermined percentage of the material. The process of making composite through the process of mixing, pelletizer, and drying. This composite produced are in diameter 1.2–2 cm. 2.2. Nickel ore reduction Nickel is spread in mineral hydrate. In general, it is difficult to increase the level, because the laterite nickel ore is finer and does not have magnetic properties and there is no significant difference in density. The metallurgical process for extracting nickel from nickel oxide ore depends on nickel content, iron content and impurity mineral composition. The conventional process in the extraction process of nickel ore is reduce in rotary kilns and smelting in electric furnace. Products can be either ferronickel or matte added sulfur to the smelting process. The energy needed to obtain ferronickel slag and metal is very high. Decrease in melting energy depends on the success of the reduction process in the rotary kiln. A high level of reduction will reduce energy per ton of hot metal [11]. 2.3. Artificial Neural Network (ANN) Artificial Neural Network (ANN) is a very powerful modelling, prediction and classification system, which is able to capture and represent any kind of input-output relationship. The goal is to create a model that can precisely map the functional relationship between input and output by using historical data. To predict outputs from the unseen testing data pattern is the usability of trained model. ANN has three or more numbers of layers with each composed of a certain number of processing elements or nodes. Hidden layers are positioned between input and output layers. The number of nodes in hidden layer can be varied based on the optimum performance from the ANN. The number of nodes in the input layer is equal to the number of independent variables, and the number of nodes in output layer is equal to the number of dependent variables.

Wij X1

X2

X3

H1

Wjk

H2

…

Y1

Output layer

H10 X4

Hidden layer

Input layer Fig. 1. Artificial neural network (ANN) model.

Fig. 1 shows artificial neural network model, where X1 … X3 is the inputs to the node j positioned in the hidden layer. H1, H2 … H9, H10 is the hidden layer and Y1 are the output layers. The Wij is the synaptic weight that connects the node j with i input and Wjk is the synaptic weight that connect the node j with k output. The back-propagation algorithm is the most commonly used methods to train an ANN. The algorithm occurs in two phases, namely forward pass, and backward pass [12].

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 Forward pass A set of experimental data is presented to the network as input and a set of output is produced and the error vector is calculated in accordance with the following equation � 𝐸𝐸 = ∑��� 𝐸𝐸� , where𝐸𝐸� = ∑����(𝑇𝑇� − 𝑂𝑂� )�

(1)

Where E is the error vector, Ej is the error associated with the j pattern, P is the total number of training patterns, Tk and Ok are the target output node k, and S is the total number of output nodes.  Backward pass In the backward pass, this error signal is propagated backwards to the network and the synaptic weight is adjusted in such a manner that the error signal decreases in each iteration step. th

Pseudo code feed forward Back Propagation to describe the flow of Feed forward Back Propagation algorithm can be seen in Fig. 2. Pseudo code Feed forward Back Propagation Input : ProblemSize,InputPatterns,iterationsmax,learnrate) Output : Network Network ← ConstructNetworkLayer (); Networkweight ← InitializeWeights(Network,ProblemSize); for i = 1 to iterationsmax do Patterni ← SelectInputPattern(InputPatterns); Outputi ← ForwardPropagate(Pattern,Network); BackwardPropagateError (Pattern,Output,Network); UpdateWeight (Pattern, Output, Network, learnrate); end return network; Fig. 2. Pseudo code Feed forward Back Propagation.

2.4. Mean Square Error (MSE) Mean Squared Error (MSE) determining the network performance is calculated as follows: 𝑀𝑀𝑀𝑀𝑀𝑀 =

�

�

� ∑� ���(𝑦𝑦𝑦𝑦 − 𝑦𝑦𝑦𝑦)

(2)

Where yi is the predicted value of the ith item, yk is the target value of the ith and N is the number of training cycle [13]. 3. Data collection Data collecting conducted in Extractive Metallurgical Laboratories, Centre of Mineral Resource Development Technology (PTPSM), Agency for Assessment and Application Technology (BPPT), Serpong, South Tangerang. Prepares sample, making composite, reduction process, and analysed using X-Ray-Difference Fluorescence (XRF) already implemented from September 18th, 2018 – January 10th, 2019. The saprolite ore composite reduction process in Tube Furnace with a temperature increase of 10 0C/minute. Tube Furnace that can reach a temperature of 1600 0C. Composites are placed in the crucible of alumina if reduced below 1300 0C, or placed in the crucible of graphite if reduced above 1300 0C. Crucible graphite is suitable for heating samples in Tube Furnace on this research. After reduction process, composite analyzed using X-Ray-Difference Fluorescence (XRF) to know the chemical compositions of Ni. The result of analyses using XRF shown detail in Table 1.

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Table 1. Result of XRF. Ex.

Ratio coal (%)

Temperature (0C)

Time duration (h)

Type of additive

Ni (mass %)

1.

27

1300

2

1

0.83

2.

27

1200

4

2

0.751

3.

27

1200

2

2

0.903

4.

30

1300

3

1

0.771

5.

30

1200

3

2

0.985

6.

27

1200

3

3

1.04

7.

27

1400

4

2

0.357

8.

30

1300

2

2

0.852

9.

27

1300

4

3

0.924

10.

15

1300

3

1

0.83

11.

27

1400

2

2

0.444

12.

30

1300

4

2

0.796

13.

27

1300

3

3

0.978

14.

30

1300

3

3

0.943

15.

30

1400

3

2

0.449

16.

27

1300

3

2

0.758

17.

27

1300

3

2

0.8

18.

27

1300

3

2

0.818

19.

15

1300

2

2

0.878

20.

27

1400

3

3

0.573

21.

15

1300

3

3

1.02

22.

27

1300

3

1

0.879

23.

30

1400

3

1

0.778

24.

15

1200

3

2

1.2

25.

15

1400

3

2

0.417

26.

27

1300

4

1

0.812

27.

27

1400

3

1

0.696

28.

15

1300

4

2

0.938

29.

30

1300

3

1

0.824

30

15

1300

3

1

0.782

31.

27

1400

4

2

0.323

32.

30

1200

3

2

1.05

33.

27

1300

3

2

0.852

34.

27

1200

4

2

1.05

35.

30

1300

3

3

0.99

36.

15

1300

3

3

1.02

37.

27

1300

2

3

1.01

38.

27

1200

3

1

0.714

39.

30

1300

2

2

0.879

40.

27

1200

3

3

1.1

5

6

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Ex.

Ratio coal (%)

Temperature (0C)

Time duration (h)

Type of additive

Ni (mass %)

41.

15

1400

3

2

0.48

42.

27

1400

3

1

0.334

429

4. The proposed method Based on the explanation above, parameter that influence the reduction process in saprolite ore composites are % coal ratio (X1), temperature (X2), duration time (X3), and type of additive (X4). Each level of each parameter in reduction process in saprolite ore is shown detailing in Table 2. Table 2. Parameter and level. Parameter

Ratio coal (%) (X1) Process temperature ( C) (X2) 0

Level 1

2

3

15

27

30

1200

1300

1400

Time duration (h) (X3)

2

3

4

Type of additive (X4)S

Na2SO4

Ca2SO4

CaCO3

Data result of XRF in Table 1 then analysed using Artificial Neural Network (ANN) method. To find the optimum hidden network, the first step is to trial distribution percentage of training, validation and testing data with having the largest value of MSE and R. Based on trial result, the largest value of MSE and R is the distribution percentage of training: validation:test = 5% : 5% : 90% or 2 samples:2 samples: 38 samples. Percentage distribution of training, validation and testing data that used to find an optimum hidden network with trial testing from 8 hidden networks to 42 hidden networks and find an optimum hidden network is 10 hidden networks. The result of MSE and R value with 10 hidden networks is shown in Fig. 3. This research used Feed forward Back Propagation (BP) neural networks with transfer function tangent sigmoid (tansig) at input layer and hidden layer, and S-logarithmic function (logsig) at transfer function, with training function Levenberg-Marquardt (trainlm) and performance function Mean Square Error (MSE) with a number of layers 1 (one) and numbers of hidden neurons 10 (ten) to create the network.

Fig. 3. MSE and R value with 10 hidden networks.

5. Result and discussion The result of the Back Propagation (BP) network showing that the optimum R-value = 0.96663 with Epoch 55 iteration is shown in Fig. 4. The optimal factor combination is the combination with the smallest value of MSE.

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Comparison between experimental value, predicted value and MSE value for each combination that processing with ANN method shown in Fig. 5. From Fig. 4, the smallest MSE value is 0.00000400 with combination 15 % coal ratio, temperature 1200 0C, time duration 3 hours, a type of additive Ca2SO4 or Composite SB15Ca10P2 with temperature 1200 0C and time duration 3 hours. It is the optimal factor combination for the reduction process of saprolite ore composites in Tube Furnace using Artificial Neural Network (ANN) method.

Fig. 4. R value with epoch 55 iteration.

Artificial Neural Network (ANN) is one of optimization method that coherence between machine learning (ML) and artificial general intelligence (AGI) and a very powerful modelling, prediction and classification system, which is able to capture and represent any kind of input-output relationship. In recent years, there has been a growing interest using Artificial Neural Network method in engineering application especially for mineral and metallurgy technique.

Fig.5. Comparison between experimental value, predicted value and MSE value.

This research uses Artificial Neural Network (ANN) method produce an optimal prediction with the estimate composition in the reduction process of saprolite ore composite in Tube Furnace. Based on the processing between

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Machine Learning (ML) and artificial general intelligence (AGI) generate predicted value approaching experimental value and results the smallest MSE value to find the optimal combination of saprolite ore composite in Tube Furnace.

Fig.6. Mass % Ni, Fe, Al and Si.

6. Conclusion Fig. 5 shows that the smallest MSE value could be reached from a combination of composite SB15Ca10P2 with a temperature of 1200 0C and time duration of 3 hours. To validate the results, the chemical compositions (mass %) of Fe, Al, and Si is carefully examined. The result of Chemical compositions (mass %) of Fe, Al, and Si is shown in Fig. 6. From Fig. 6, the specimen with composite of SB15Ca10P2, a temperature of 1200 0C and time duration of 3 hours, have the largest mass % of Fe, and small mass % of Al and Si. Those chemical compositions validate the result of the optimal factor combination reduction process of saprolite ore composites in Tube Furnace using Artificial Neural Network (ANN) method. Several future research agendas may be addressed from this point. First, to obtain optimal factor combination reduction process saprolite ore composites in Tube Furnace, the study can be developed further by comparing with other Design of Experiment (DOE) method and other metaheuristic method or hybrid metaheuristic like Simulated Annealing (SA) or Artificial Neural Network – Genetic Algorithm (ANN – GA) to know the best optimal factor combination. Second, the use of more data factor combination to get the best results factor combination would also be an option. Acknowledgements Authors would like to express gratitude and appreciation to Universitas Indonesia for funding this research through PIT-9 Research Grants Universitas Indonesia No: NKB-0061/UN2.R3.1/HKP.05.00/2019. References [1] Team of Geological Resource Center. (2014) Mineral Balance 2014. [2] Chen, Guan-Jhou, Shiau Jia-Shyan, Liu Shih-Hsien, and Hwang Weng-Sing. (2016) “Optimal Combination of Calcination and Reduction Conditions as well as Na2SO4 Additive for Carbothemic Reduction of Limonite Ore.” Material Transactions 57 (9): 1560-1566. [3] Rochani, Siti, and Saleh Nuryadi. (2013) Nickel Processing and Purification Technology, Mineral and Coal Technology Research and Development Center. [4] Kawigraha, Adji, Johny Wahyudi, Sri Harjanto, and Pramusanto. (2013) “Reduction of Composite pellet containing Indonesia lateritic iron ore as raw material for producing TWDI.” Applied Mechanic and Materials 281 (1): 490-495. [5] Wahyuadi, Johny, Rosoebaktian Simarmata, Adji Kawigraha, Rianti Dewi Sulamet-Ariobimo, Andi Rustandi, Seto Tjahyono, and Aidil Zamri. (2014) “Effect of Reduction Process Parameter in Direct Reduction Process of Laterite to Produce Substitute Pig Iron for Thin Wall Ductile Iron Material.” Advance Material Research 893 (1): 95-99. [6] Yongli, Li, Sun Tichang, Anhua Zou, and Chengyan Xu. (2012) “Effect of Coal Levels During Direct Reduction Roasting of High Phosphorus Oolitic Hematite Ore in a Tunnel Kiln.” Internation Journal of Mining Science Technology 22 (3): 323-328. [7] Yadav, Anand Mohan, Ram Chandra Chaurasia, Nikkam Suresh, and Pratima Gajbhiye. (2018) “Application of Artificial Neural Networks and Response Surface Methodology Approaches for the Prediction of Oil Agglomeration Process.” Fuel 20 (1): 826-836.

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[8] Altarazi, Safwan, Maysa Ammouri, and Ala Hijazi. (2018) “Artificial Neural Network Modeling to Evaluate Polyvinylchloride Composites Properties.” Computer Material Science 153 (1): 1-9. [9] Han, Fei, Jing Jiang, Qing-Hua Ling, and Ben-Yue Su. (2019) “A Survey on Metaheuristic Optimization for Random Single-Hidden Layer Feed Forward Neural Network.” Neurocomputing 335 (1): 261-273. [10] Sabilla, Shoffi Izza, Riyanarto Sarno, and Joko Siswantoro. (2017) “Estimating Gas Concentration using Artificial Neural Network for Electric Nose”, in Procedia Computer Science 124 (1): 181-188. [11] Saleh, Nuryadi. (2016) Optimization of Reduction Iron Ore, Nickel with Rotary Kiln Scale Semi Pilot, Mineral and Coal Technology Research and Development Center. [12] Majumdar, Abhijit. (2010) “Comparative Analysis of Regression and Artificial Neural Network Model for the Prediction of Yarn Hairiness.” Research Journal of Textile and Apparel 14 (3): 85-93. [13] Deng, Lina, Bo Feng, and Yue Zhang. (2018) “An Optimization Method for Multi-Objective and Multi-Factor Designing of a Ceramic Slurry: Combining Orthogonal Experimental Design with Artificial Neural Networks.” Ceramic International 44 (13): 15918-15923.

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