Browsing by Author "Negash, Mihreteab"

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Neural Network Based Data-Driven Clinker Quality Prediction: Case Study on Mugher Cement Factory
    (Addis Ababa University, 2012-10) Negash, Mihreteab; Alemu, Getachew(PhD)
    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.