knowledge driven intelligent decision support system for iron ore pellet using artificial...

8/22/2019 Knowledge Driven Intelligent Decision Support System for Iron Ore Pellet Using Artificial Intelligence Technique- Ma

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KNOWLEDGE DRIVEN INTELLIGENT DECISION SUPPORT SYSTEM FOR IRON

ORE PELLET USING ARTIFICIAL INTELLIGENCE TECHNIQUEManoj Mathew

1, L P Koushik

2,

Department Mechanical EngineeringChristian College of Engineering and Technology

Kailash Nagar P.O. Industrial Estate, Bhilai Distt. Durg, (CG) India

[email protected]

1

, [email protected]

2

ABSTRACT

Cold Compression Strength (CCS) of iron ore pellets plays a vital role for the production of DRI from shaft

furnace. During the pellet production CCS is one of the important control parameters and it is supposed to beclosely monitored, to control the process. A knowledge driven intelligent decision support system wasdeveloped to predict the cold compressive strength of the iron ore pallet. Non traditional Artificialintelligence (AI) techniques like artificial neural networks (ANNs) and fuzzy logic were used for theprediction. Different models were compared for their quality and for the simplicity of application.In this paper the ANN and Rule based Fuzzy model was created in the MATLAB toolbox. To obtain themodel having lowest Mean Absolute Percentage error (MAPE), different learning algorithms, training

function and architectures in combinations were tested. It was found that MAPE of 1.34% was found in theANN model with architecture 3x10x1, the training function, transfer function used in hidden and output layerare TRAINLM, TANSIG, PURELIN respectively. The rule based fuzzy model was created in MATLABtoolbox, and it was found that the MAPE of fuzzy model is 0.576%. The simulated value is found close to

the actual value. Thus the model acts as a guide (DSS) for the operator and thereby helps to attain the desiredobjective in iron ore pellet process.

Keywords: Artificial Neural network; pelletization; rule based fuzzy model; Decision Support System

1 INTRODUCTION

Decision-making process is an intelligent activities performed by human beings. The term intelligence inDecision Support System (DSS) itself is the ability of DSS to use refined information, knowledge, andinference in order to achieve the desired objectives. Simulation of a system, modelling and prediction of the

output can be done with the help of artificial intelligence (AI) in which neural network and fuzzy logic hasan important place. Thus artificial intelligence can be implemented to make a knowledge driven intelligent

decision support system used for the prediction of cold compressive strength of iron ore pellet. Pelletizing isa process used for agglomeration of the raw iron-ore fines, which consist of two steps: balling of powderedfine using rotating disk/drum and induration (thermal hardening) of green pellet on a moving straight grate[1].Input parameters like percentage bentonite by weight, Blaine number and green pellet moisture content

directly affect the CCS of iron ore pellet. Attempts have been made by the researchers to make models topredict the quality of iron ore pellet. Srinivas Dwarapudi [2] has presented the artificial neural network

model for predicting the Strength of iron ore pellet in straight grate indurating machine from 12 inputvariables. The model was compared with the regression model and it was found that feed Forward back

propagation error correction technique predicted the CCS of iron ore with a result less than 3% error.Jun-xiao Feng [3] has made a mathematical model of drying and preheating processes and also studied the effects

of pellet diameter, moisture, grate velocity, and inlet gas temperature on the pellet bed temperature. S.K.Sadrnezhaad [4] has made a mathematical model for the induration processsof the iron-ore pelletst based onthe laws of heat, mass and momentum transfer. In the present work computerised AI models have beencreated so as to predict the CCS of pellet.

2 DESIGN METHODOLOGIES

The selection of process parameters that affect the cold compressive strength is an important step in carryingout the analysis. A survey was conducted in the iron ore plant and based on the heuristic knowledge providedby the plant expert, a total of 3 input process parameters were taken. Quality control data from plant wereused in the modelling studies. The data were randomly separated into two parts of which the first one

contained 83 datas for training and the second part had 25 datas used for testing the models created usingartificial intelligence. Table 1. Show the Quality Variables chosen for the analysis of iron ore Pellet. The

standard deviation measures how spread out a data set is. The mean of a data set with n values is calculated


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with equation 1. The Standard deviation of x describes the mean distance from the points in the data set to

which is given by equation 2, where s is the standard deviation.

=

(1)

= () (2)Table 1: Quality Variables of iron ore Pellet.

Variable Bentonite,%Blaine Number

(cm2/gram)Green PalletMoisture, %

CCS

Mean 0.79 2137.27 9.199 203.164

Median 0.8 2120 9.2 203.1

Mode 0.8 2040 9.3 202.8StandardDeviation

0.0922 192.985 0.224 2.108

CCS was found to be more sensitive to variation in Bentonite, Blaine Number and Green pellet moisture,thus these attributes were used as input variable to control the CCS. Srinivas Dwarapudi [5] has also written

paper regarding the influence of Pellet Size on Quality of Iron Ore Pellets. He came to the conclusion that toimprove the pellets quality, pelletising plants should tune their balling and screening circuits. S.P.E.Forsmo[6] showed the behaviour of wet iron ore pellet with variation in bentonite binder.

2.1 NEURAL NETWORK ARCHITECTURE AND LEARNING MECHANISM

Artificial neural network have interested researches from quite a long time. The various researches donewithin the field of computer added manufacturing, describing the use of artificial neural networks has evolvedfor a diverse range of engineering applications [7-11]. McCulloch and Pitts created a simplified neuron modelin 1943 [12]. An ANN consists of a group of processing unit which communicate with each other by numberof weighted connection.Working in neural network data manager-

Step1: Data transfer to command window- The data is transferred from excel to the command window and

there the data is made in matrix type. All the data are automatically stored in the Workspace

Input data: the input data have 3 parameters (bentonite, green pellet moisture, Blaine number) and 83 sets of

data are taken, so it is stored as a matrix of size (3 x 83). Target data: Similarly the target data have single

parameters i.e. CCS of iron ore pellet and 83 sets of data are taken, so it gets stored as a matrix of (1 x 83).

Step2: Opening data manager -The Data Manager appears with nntool as the command.

Step3: Import of data- The import is made feasible using the appropriate command button on the data manager

Step4: Creating new network- After import of data the most important step is designing a network by selecting

appropriate number of layers and appropriate number of neurons in each layer with the best suited processingmathematical transfer function interlinking each layer. Neural networks are generally composed of threelayers i.e. input layer, hidden layer, and output layer. In each layer there are number of neurons which areinterconnected to each other such that each node in one layer have connections to the next layer.

Step5: Initialization- Initialization is done to make fresh selection of random numbers as weights and biases.

Step6: Train the network- Once the network is created and initialization is done the network is now ready for

training. For the training various combination of input and output neurons are taken (see Table 2) and as a part

of training information inputs and targets are selected, optional information is also given like number of

epochs, goal for MSE, maximum failures and minimum gradient etc, an example of MSE performance graph

obtained during the training is shown in Fig. 1. The mean square error obtained during the training was

1.7989.


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Fig. 1 MSE Performance graph

A generalized feed forward network with back propagation error correction technique has been adopted to

train the network. Training function such as Levenberg-Marquardt (trainlm), Conjugate gradient (traincgf)

and Quasi-Newton (trainoss) were used. There were in total 40 network model with different learning

algorithms, training function and architectures in combination made so as to obtain the best MSE

performance graph. Table 2 shows some of the trial network taken for the training. The training function

used for creating the network was TRAINLM.

Table 2. Some trials network taken for the training using TRAINLM Training Function

NetworkHidden Layer

MSELayer1 layer2 Output Layer

Neurons Transferfunction

Neurons Transferfunction

Network1 10 LOGSIG PURELIN 2.1008

Network2 15 LOGSIG PURELIN 6.1464

Network3 10 TANSIG PURELIN 1.7989

Network4 15 TANSIG PURELIN 4.7683

Network5 10 TANSIG 10 LOGSIG PURELIN 6.8743

Network6 10 TANSIG 5 LOGSIG PURELIN 5.4873

Network7 10 TANSIG 3 LOGSIG PURELIN 4.0064Network8 5 TANSIG 3 LOGSIG PURELIN 2.3839

Network9 10 TANSIG TANSIG 2.1142

Network10 15 TANSIG TANSIG 4.1616

2.2 FUZZY LOGIC ARCHITECTURE

The fuzzy logic model was created in MATLAB using the five basic GUI tool i.e. FIS Editor, Rule Editor,

Membership function editor, surface viewer, rule viewer. Fig. 2 shows the fuzzy interference system of the

iron ore pellet in which bentonite, Blaine Number and Green pellet Moisture are input and CCS is in output.

The fuzzy output engine is governed by the Fuzzy rule base.


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Fig. 2 Fuzzy Model of iron ore pellet

Working in mamdani type fuzzy inference system (FIS) Editor

Step 1: Opening FIS Editor- The FIS Editor appears with fuzzy as the command. The required number ofinput and output variables is added in the FIS editor. Fig. 3 shows the FIS Editor in MATLABStep 2: Fuzzification- The input and output variables are fuzzified using the membership function editor. Themembership function editor can be opened by double clicking on the input variable icon. The output is fuzzy

degree of membership which is always in the interval between 0 and 1. The membership function can beadded and the type can also be selected.

Step 3: Creating Rules- The rules are created in the rule editor by interconnecting the inputs and output by If-Then rule.Rules created in the rule editor defines the behaviour of the model. The rule editor can be opened

by clicking on the rule base from the view menu.Step 4: Defuzzification- The fuzzy output is converted to crisp data using various defuzzification methods.

The surface and rules can be viewed in the surface viewer and rule viewer.

Fig. 3 FIS Editor

Both input and output variable of the iron ore pellet plant were fuzzified, and fuzzy membership functions

were employed. In the Mamdani fuzzy editor, total 18 rules with IF-Then format were created in the rule

editor. Various defuzzification methods like centre of gravity (COG), Bisector, MOM, SOM, LOM etc were

employed. The mean average percentage error in the fuzzy model was 0.576%.

The 25 datas for testing purpose was used for comparison between the two models created in the

MATLAB. Both ANN and fuzzy model can be compared using Mean Absolute percentage error which is


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given by the equation 3

Mean Absolute Percentage Error (MAPE) = || 100 (3)Where n = number of observation

Graph.1 Actual vs. predicted CCS from ANN and fuzzy logic

3 RESULTS AND DISCUSSION

The developed neural network model and rule based fuzzy model aims to predict the cold compressive

strength of the iron ore pellet by selecting the right amount of bentonide , Blaine number, and green pellet

moisture. It can be used as an effectual tool with large range of industrial datas available for training of

network. A Mean Absolute percentage error of 1.34% was obtained in the neural network with architecture

3x10x1, training function used was TRAINLM. The fuzzy model created to have a mean absolute percentage

error of 0.576% with centroid as defuzification method trimf as membership function. Thus it can be

concluded that fuzzy model created was more appropriate and can be more useful tool in decision support

system for decision maker Graph 1 depicts the actual and predicted value of CCS obtained from the artificial

neural network and fuzzy logic.

Graph 2.a) Residual for ANN model b) Residual for Fuzzy model

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1 2 3 4 5 6 7 8 9 1011 12 13 14 15 16 17 18 1920 21 22 23 24 25

ColdCom

pressiveStrength

Observation Number

Actual

Predicted ANN

Predicted Fuzzy

-4

-3

-2

-1

0

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0 5 10 15 20 25Residual'e'

Observation Number

From Fuzzy

-8

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0 5 10 15 20 25Residual'e'

Observation Number

From ANN


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The residual was calulated for both ANN and fuzzy model and and it was found that fuzzy model gave more

close result to the actual value. Graph.2 a,b shows the residual for both the model. There is always scope of

further reduction of Error and further reduction from this level would be a commendable achievement by the

future researchers. Depending upon the number of data and the architecture used for training the network can

be fine tuned to obtain more accurate results.

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simulation of induration of wet iron ore pellets on a moving grate,Computers and Chemical Engineering, Vol. 33,2009, pp11411152.

2. Srinivas Dwarapudi, P. K. Gupta and S. Mohan Rao, Prediction of iron ore pellet strength using artificial neural

network model, ISIJ International, Vol. 47, 2007, No. 1, pp. 6772.

3. Jun-xiao Feng, Yu Zhang, Hai-wei Zheng, Xiao-yan Xie and Cai Zhang, Drying and preheating processes of iron

ore pellets in a traveling grate, International Journal of Minerals, Metallurgy and Materials, Vol.17, Number 5,

October 2010, Page 535.4. S.K. Sadrnezhaad , A. Ferdowsi, H. Payab, Mathematical model for a straight grate iron ore pellet induration

process of industrial scale, Computational Materials Science, Vol. 44, 2008, pp 296302

5. Srinivas Dwarapudi, T. Uma Devi, S. Mohan Rao And Madhu Ranjan, Influence of Pellet Size on Quality and

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6. S.P.E. Forsmo, A.J. Aqelqvist, B.M.T. Bjorkman, P.O.Samskog, Binding mechanisms in wet iron ore green

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