Neural Network Based Data-Driven Clinker Quality Prediction: Case Study on Mugher Cement Factory
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Date
2012-10
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Addis Ababa University
Abstract
Soft sensors are key solutions in predicting importance process variables. In process industries,
important parameters which are difficult or cost a lot to measure online can be predicted using
soft sensors. In this thesis a data driven soft sensor is developed using neural network to predict
important clinker quality parameters.
The developed predictor is significant and can be categorized to the class of neural network
based soft sensors. The significance of the thesis is that it avoids measurement delay incurred
while analyzing clinker samples. As a result, quick control actions can be taken and clinker
quality can be further improved. This is one of the solutions provided by soft sensors. Many soft
sensors have been developed in different application areas and cement factory is the one. Some
papers report neural network based predictors that are developed on rotary cement kiln. These
works are related to the thesis. However, the thesis has its own new contribution. The first new
feature is that it has developed data synthesis strategy. Besides, multiple and advanced neural
network architectures are used to get improved result. Moreover, it is of the first kind for the
selected case, which is the third line of Mugher cement factory.
The thesis is developed stage wise and a desired result is obtained. First, cement production
specific to the case is studied. Then, data of all the recorded variables in the factories database is
collected. This data collection is accompanied by variable selection and data encoding. The data
is processed prior to using it for training the neural networks. This data preprocessing treated
missing and outlier values. Based on the cleaned data, new data is synthesized to have enough
dataset to work on. Finally, neural network models are developed and trained on this dataset. As
a result, neural network models are obtained that can predict LSF, SM, AM and C3S values of
clinker with mean square error values of 4.3482, 0.0027, 0.0011 and 10.8759 respectively.
In conclusion, in this thesis a neural network based data driven clinker quality predictor is
developed. While developing the predictor, Mugher cement factory is used as a case study. The
developed predictor estimates LSF, SM, AM and C3S values
Key words: Soft sensor, neural network, clinker quality prediction.
Soft sensors are key solutions in predicting importance process variables. In process industries,
important parameters which are difficult or cost a lot to measure online can be predicted using
soft sensors. In this thesis a data driven soft sensor is developed using neural network to predict
important clinker quality parameters.
The developed predictor is significant and can be categorized to the class of neural network
based soft sensors. The significance of the thesis is that it avoids measurement delay incurred
while analyzing clinker samples. As a result, quick control actions can be taken and clinker
quality can be further improved. This is one of the solutions provided by soft sensors. Many soft
sensors have been developed in different application areas and cement factory is the one. Some
papers report neural network based predictors that are developed on rotary cement kiln. These
works are related to the thesis. However, the thesis has its own new contribution. The first new
feature is that it has developed data synthesis strategy. Besides, multiple and advanced neural
network architectures are used to get improved result. Moreover, it is of the first kind for the
selected case, which is the third line of Mugher cement factory.
The thesis is developed stage wise and a desired result is obtained. First, cement production
specific to the case is studied. Then, data of all the recorded variables in the factories database is
collected. This data collection is accompanied by variable selection and data encoding. The data
is processed prior to using it for training the neural networks. This data preprocessing treated
missing and outlier values. Based on the cleaned data, new data is synthesized to have enough
dataset to work on. Finally, neural network models are developed and trained on this dataset. As
a result, neural network models are obtained that can predict LSF, SM, AM and C3S values of
clinker with mean square error values of 4.3482, 0.0027, 0.0011 and 10.8759 respectively.
In conclusion, in this thesis a neural network based data driven clinker quality predictor is
developed. While developing the predictor, Mugher cement factory is used as a case study. The
developed predictor estimates LSF, SM, AM and C3S values
Key words: Soft sensor, neural network, clinker quality prediction.
Soft sensors are key solutions in predicting importance process variables. In process industries,
important parameters which are difficult or cost a lot to measure online can be predicted using
soft sensors. In this thesis a data driven soft sensor is developed using neural network to predict
important clinker quality parameters.
The developed predictor is significant and can be categorized to the class of neural network
based soft sensors. The significance of the thesis is that it avoids measurement delay incurred
while analyzing clinker samples. As a result, quick control actions can be taken and clinker
quality can be further improved. This is one of the solutions provided by soft sensors. Many soft
sensors have been developed in different application areas and cement factory is the one. Some
papers report neural network based predictors that are developed on rotary cement kiln. These
works are related to the thesis. However, the thesis has its own new contribution. The first new
feature is that it has developed data synthesis strategy. Besides, multiple and advanced neural
network architectures are used to get improved result. Moreover, it is of the first kind for the
selected case, which is the third line of Mugher cement factory.
The thesis is developed stage wise and a desired result is obtained. First, cement production
specific to the case is studied. Then, data of all the recorded variables in the factories database is
collected. This data collection is accompanied by variable selection and data encoding. The data
is processed prior to using it for training the neural networks. This data preprocessing treated
missing and outlier values. Based on the cleaned data, new data is synthesized to have enough
dataset to work on. Finally, neural network models are developed and trained on this dataset. As
a result, neural network models are obtained that can predict LSF, SM, AM and C3S values of
clinker with mean square error values of 4.3482, 0.0027, 0.0011 and 10.8759 respectively.
In conclusion, in this thesis a neural network based data driven clinker quality predictor is
developed. While developing the predictor, Mugher cement factory is used as a case study. The
developed predictor estimates LSF, SM, AM and C3S values
Key words: Soft sensor, neural network, clinker quality prediction.
Soft sensors are key solutions in predicting importance process variables. In process industries,
important parameters which are difficult or cost a lot to measure online can be predicted using
soft sensors. In this thesis a data driven soft sensor is developed using neural network to predict
important clinker quality parameters.
The developed predictor is significant and can be categorized to the class of neural network
based soft sensors. The significance of the thesis is that it avoids measurement delay incurred
while analyzing clinker samples. As a result, quick control actions can be taken and clinker
quality can be further improved. This is one of the solutions provided by soft sensors. Many soft
sensors have been developed in different application areas and cement factory is the one. Some
papers report neural network based predictors that are developed on rotary cement kiln. These
works are related to the thesis. However, the thesis has its own new contribution. The first new
feature is that it has developed data synthesis strategy. Besides, multiple and advanced neural
network architectures are used to get improved result. Moreover, it is of the first kind for the
selected case, which is the third line of Mugher cement factory.
The thesis is developed stage wise and a desired result is obtained. First, cement production
specific to the case is studied. Then, data of all the recorded variables in the factories database is
collected. This data collection is accompanied by variable selection and data encoding. The data
is processed prior to using it for training the neural networks. This data preprocessing treated
missing and outlier values. Based on the cleaned data, new data is synthesized to have enough
dataset to work on. Finally, neural network models are developed and trained on this dataset. As
a result, neural network models are obtained that can predict LSF, SM, AM and C3S values of
clinker with mean square error values of 4.3482, 0.0027, 0.0011 and 10.8759 respectively.
In conclusion, in this thesis a neural network based data driven clinker quality predictor is
developed. While developing the predictor, Mugher cement factory is used as a case study. The
developed predictor estimates LSF, SM, AM and C3S values
Key words: Soft sensor, neural network, clinker quality prediction.
Description
Keywords
Soft Sensor, Neural Network, Clinker Quality Prediction