Neural Network Soft Sensor Application in Cement Industry: Prediction of Clinker Quality Parameters

Ajaya Kumar Pani, Vamsi Vadlamudi, R J Bhargavi, Hare Krishna Mohanta Department of Chemical Engineering, Birla Institute of Technology and Science, Pilani, India – 333031

Abstract –A soft sensor tries to estimate difficult to measure quality parameters from the knowledge of easy to measure online process variables. Empirical approach of soft sensor development has gained much popularity recently due to availability of huge quantity of actual process data stored in the industrial database. In this work a soft sensor based on back propagation neural network has been developed for rotary cement kiln. For this purpose, data for all variables associated with rotary cement kiln were collected over a period of one month from a cement industry having a capacity of 10000 tons of clinker production per day. Data preprocessing of the raw data has been performed to remove the anomalies present in the original data. The processed data was used to develop the neural network model of the kiln. Model simulation produced quite satisfactory prediction of free lime, C3S, C2S and C3A. Key words: Data preprocessing, cement kiln, soft sensor

I. INTRODUCTION

Industrial processing plants are usually heavily instrumented with a large number of sensors. However there are fewer reliable and accurate sensors available for accurate online measurement of quality variables especially related to composition. Failure to accurately estimate important process outputs may result in product loss, energy loss, undesirable byproduct formation and safety hazards [12]. Soft sensors allow online estimation of variables which are otherwise difficult or impossible to monitor online, using easily measurable variables. There is a wide range of process industry problems that a soft sensor can address [1, 3]: Prediction of an unmeasured process variable from the available data of measured variables (Process monitoring), process fault detection which refers to detection of the state of the process and in the case of a deviation from the normal conditions to identification of the cause, sensor fault detection and reconstruction, act as a back-up sensor of the hardware measuring device. Moreover soft sensors also overcome the time delay problem associated with most hardware sensors thereby resulting in more effective control of the process output variable. The heart of a soft-sensor constitutes the model of a process which takes values of the easily measurable process variables and predicts the output which is the difficult to measure process variable thereby replacing or assisting a real physical sensor. This plant model may be a mechanistic model, empirical model or hybrid model. Mechanistic

models describe the physical and chemical background of the process and hence require detailed process understanding. There are difficulties in development of such models because: most of the processes are very complex and are not fully understood, parameters involved in the model equations are difficult to obtain and moreover very often the model equations developed become so complex that it becomes difficult to solve. As a solution the data-driven soft sensors are becoming increasingly popular in the process industry. Because data-driven models are based on the data measured within the processing plants and thus describe the real process conditions, they in comparison to the first principle model based soft sensors, are more reality related and describe the true conditions of the process in a better way. Moreover, the large amount of data being measured and stored in the process industry provides motivation for development of data driven soft sensors The basic steps in any data driven soft sensor design method are: data collection, data preprocessing, model selection, parameter identification and finally model validation [1, 3, 4] which are shown in Figure 1. In the present work a neural network based soft sensor trained by back propagation algorithm has been developed for application to a rotary cement kiln. The article describes the following issues in order: brief description of the cement making process with focus on the rotary cement kiln, data preprocessing, neural network development, simulation results and conclusion. Brief description of cement manufacturing process: The raw materials for cement are limestone as a source of lime, clay as a source of silica, laterite as a source of iron and red ochre as a source of aluminium. These raw materials are first mixed in the required proportion referred to as raw meal. This raw meal is then ground in a vertical roller mill to required size. The required size raw meal then enters a multistage cyclone preheater where it is preheated by the hot flue gas coming from the cement rotary kiln. The preheated raw meal then enters the cement kiln. The rotary kiln is the heart of the cement plant where the required reactions take place. The different components present in the raw meal, at high temperature react with each other and the final product comes out of the kiln what is known as clinker. The clinker is then ground with a small amount of gypsum in the cement mill producing the final products i.e. cement.

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In the cement plant the quality of the clinker to large extent affects the quality of the cement. However this Clinker quality is mostly determined in the laboratory by taking clinker samples at kiln outlet at regular intervals and then analyzing the same for different components. Therefore soft sensor has potential application for clinker quality check in the cement industry. This is shown schematically in figure 2.

Figure 2: Soft Sensor Application to Cement Kiln Data were collected for the quality parameters of the raw meal as well as corresponding output clinker and kiln operating variables from a cement industry having a kiln capacity of 10000 tons clinker per day as shown in Table 1.

Table 1: All variables associated with cement kiln

Raw meal quality Clinker quality Kiln operating

variables SiO2, Al2O3, Fe2O3, CaO, MgO, K2O, Na2O, SO3, Cl, Lime Saturation Factor, Silica Modulus, Alumina Modulus

SiO2, Al2O3, Fe2O3, CaO, MgO, K2O, Na2O, SO3, Cl, Free lime, Lime Saturation Factor, Silica Modulus, Alumina Modulus, C3S, C2S, C3A, C4AF

Kiln feed rate, current, kiln RPM, feed inlet temperature, coal feed rate

Though modeling happens to be the key task in a soft sensor development, data preprocessing in case of data driven soft sensor contribute significantly to the accuracy and effectiveness of the model.

II. DATA PREPROCESSING

There are sensors which record values for a process variable every few seconds or minutes. So a data set for even a period a month or some days comprises a vast number of data which often makes the analysis quite complicated. The raw data extracted from the plant database may often suffer from one or more of these drawbacks: multidimensionality, measurement noise and low accuracy, redundant and incorrect values, non uniformity sampling rates, presence of outliers, data co linearity and missing values, drift and offset and high & low frequency disturbances [8, 9]. Data preprocessing and cleaning is done in order to avoid confusion resulting from availability of huge amount of data pertaining to many variables and better process understanding. Data preprocessing task is a complex procedure involving variable selection followed by data selection. Variable Selection An industrial database provides data of all the variables that are recorded. In the present work as shown in table 1 above there are 12 variables pertaining to raw meal quality (kiln inlet), 5 kiln operating variables and 17 clinker quality variables. However all the available variable data are not relevant to the process variable to be estimated. Presence of irrelevant (or less relevant) variable data in the input data set lead to noise which may result in deterioration of the model. For neural network modeling a reduction in the input data dimension leads to simplified neural architecture and reduced training time [2]. Therefore based on prior process knowledge and consultation with plant operators the

Figure 1: Soft Sensor Design Steps

Data collection

Data preprocessing

Model selection and

training

Model validation

Soft sensor Output

Soft sensor maintenance

Expert knowledge

Input (Raw meal

composition)

Operating parameters

Soft Sensor (Kiln Model)

Output (Clinker quality

prediction)

variables shown in table 2 were retained for model development.

Table 2: Final Selected Variables for the Kiln

Raw meal quality

Clinker quality Kiln operating variables

SiO2, Al2O3, Fe2O3, CaO,

Free lime, C3S, C2S, C3A, C4AF

Kiln feed rate, current, kiln RPM, feed inlet temperature, coal feed rate

The raw meal quality parameters along with the kiln operating variables are the inputs for the kiln model and the clinker quality parameters are the outputs of the model. The next task is handling of outliers and missing data. These problems are more likely to take place in case of online measurement than laboratory measurement. Data outliers Outliers are sensor values which deviate from the typical or sometimes also meaningful, ranges of the measured values. In the context of process industry, outliers in data set may arise due to: hardware failure, process disturbances or changes in operating conditions, instrument degradation, transmission problems and/or human error [7, 9]. Outliers may lead to model misspecification, biased parameter estimation and incorrect analysis results. In the present study three popular outlier detection techniques [6, 10] were applied to each of the online operating variable data set. 3σ outlier detection algorithm is based on univariate observations of the variable distributions. According to this algorithm all process values satisfying the condition σ3>− xxi are outliers.

Hampel identifier is similar to 3σ method but uses median in place of mean and median absolute deviation from median (MAD) in place of standard deviation to calculate the limits. The MAD scale is defined as MAD = 1.4826 median { 5.0xxi − } Box plot is a graphical tool for determining how severe the outlier observations are. A box plot is drawn between the upper and lower quartiles with a solid line drawn across the box to locate the median. The different regions in the plot are defined as: Lower inner fence: Q0.25 – 1.5×(Q0.75 – Q0.25) Upper inner fence: Q0.75 + 1.5×(Q0.75 – Q0.25) Lower outer fence: Q0.25 - 3×(Q0.75 – Q0.25) Upper outer fence: Q0.75 + 3×(Q0.75 – Q0.25) A mild outlier is a point beyond an inner fence on either side while an extreme outlier is a point beyond an outlier fence.

On application of these three outlier detection techniques to each of the online variables, it was observed that the Hampel identifier is able to detect most efficiently the outliers present in the data set. Missing values Missing data are single sample values or set of sample values, where one or more measurements have a value which does not represent the actual state of the physical measured quantity. The affected variables usually have values like ±∞, 0 or any other constant value. The most common causes are the failure of a hardware sensor, its maintenance or removal, failure of proper transmission of the data between the sensors and the database, errors in the database, problems in accessing the database, etc. besides these, missing value may also result when the values are entered manually into a log book. In the present study, when the detected outliers are removed that posed the problem of missing data at that particular time. One approach to addressing the issue of missing data is case deletion i.e. to skip the data samples consisting of variable or variables with the missing values. However this may lead to deletion of some of the useful data also resulting in loss of useful information. Case deletion is suitable when the amount of missing data is small constituting a negligible fraction of the total data Data imputation is a statistical procedure for filling in of missing values by practical possible values so as to make the database complete. The missing data arising in the present work as a result of outlier removal were imputed by the method of linear interpolation [5, 11] between the data preceding and following it. Dimensionality Mismatch Laboratory data for quality parameters were available at an interval of every 2 to 3 hours whereas online data is recorded in the database history almost every 20 to 30 minutes. Therefore for the same time period when there were only a few hundred laboratory quality data available, process variables were available in excess of thousand In order to make all the neural network input variables of the same dimension, linear interpolation was done to estimate the process variable at a time instant when the laboratory data was available Data Normalization A variable of low magnitude may be ignored as compared to a variable of high magnitude though equally important. Normalization was done to maintain the relative importance of all variables Data normalization method followed in the present study is:

minmax

min

xxxx

xnorm −−

=

After performing the different data preprocessing operations of variable selection, outlier detection and removal, missing

value imputation and data normalization the final normalized input data is shown in figure 3.

III. NEURAL NETWORK DESIGN

A predictive time neural network model requires a complete input – output data set. In the present study raw meal quality parameters and kiln operating variables are the inputs to the network and clinker quality parameters are the outputs. A total of 223 input – output data sets were prepared out of which 156 were used for training the neural network model and 67 for model validation Choosing ANN Architecture Choosing the ANN architecture involves determining the number hidden layers and the number of neurons in each hidden layer. The numbers of input and output nodes are fixed by the process conditions of the number of input variables and number of output variables. In this case we have nine input node and four output neuron. Usually a single hidden layer is used to solve functional approximation problems and if the performance goal is not attained in a single hidden layer gradually the number of hidden layers can be increased. The more the number of hidden layers the more is the complexity associated and large training time required. Based on the large number of training conducted with single and two hidden layers with different number of neurons the optimum architecture was determined as mentioned below:

Number of hidden layers: 2 Number of neurons in the first hidden layer: 9 Number of neurons in the second hidden layer: 12 Neural Network Parameters The details of the network parameters are given in table3.

Table 3: Neural Network parameters

Activation function used in hidden layer 1

Logsig (Sigmoidal)

Activation function used in hidden layer 2

Logsig (Sigmoidal)

Activation function used in output layer

Purelin (Linear)

Training function used Trainscg (Scaled conjugate gradient)

Learning rate 0.05 Performance function used MSE (Mean Squared

Error)

IV. RESULTS AND DISCUSSION

MSE value obtained: 0.005 Number of epochs performed: 7250 The simulation results with the trained network, after converting the normalized output to the actual value are shown in figures 4 to 7.

Figure 3: Final Normalized Input Data

Figure 4

Figure 5

It can be seen from the results that the neural network is able to predict most of the values quite closely except for very high and low values. If instead of four variables, the network is trained for one or two variables then it may yield a still better result. However further improvements over the present result may be possible by trying other neural network algorithms or by combining neural network with other modeling techniques to develop a hybrid model.

V. CONCLUSION

A soft sensor model of the rotary cement kiln is developed using back propagation neural network for simultaneously predicting the contents of free lime, C3S, C2S and C3A of the clinker. The data obtained from the cement industry having a capacity of 10000 tons per day of clinker production were first preprocessed to remove data anomalies before using the data for network training. Simulation of the trained network show satisfactory result in prediction of the above clinker quality parameters.

VI. ACKNOWLEDGMENT

The authors are thankful to the Management of Ultratech Cements, Kotputli Cement Works, Rajasthan, India for providing the laboratory and online data for the rotary cement kiln

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