Department of Chemical Engineering
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Item A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks(IEEE, 2013) Pani, Ajaya Kumar; Mohanta, Hare KrishnaSoft sensors play an important role in predicting the values of unmeasured process variables from knowledge of easily measured process variables. Online estimation of particle size is vital for efficient control of a grinding circuit. Due to high energy consumption in cement grinding processes and unavailability of reliable hardware sensors for continuous monitoring, soft sensors have tremendous scope of application in cement mills. Modern cement plants are increasingly using vertical roller mills for clinker grinding. While there have been some works reported in the literature about modelling of ball mills, very few research work is available on vertical roller mill modelling. In the present work a PCA based neural network model of a cement mill is developed based on the actual plant data for estimation of cement fineness. Real time data for all process variables relevant to cement grinding process were collected from a cement plant having a clinker grinding capacity of 235 TPH. The collected raw industrial data were pre processed for outlier removal and missing value imputation. Principal component analysis of the input data was performed to transform the original variables to a less number of un correlated principal components. The selected principal component scores were divided to a training set and a validation set using Kennard-Stone subset selection algorithm. The training set was used to develop a back propagation neural network model which was subsequently tested with the validation set. Simulations results show satisfactory prediction capabilities of the developed model over that of linear regression and principal component regression models.Item Quality monitoring in petroleum refinery with regression neural network: Improving prediction accuracy with appropriate design of training set(Elsiever, 2019-02) Pani, Ajaya Kumar; Mohanta, Hare KrishnaThe objective of this research is twofold. First, design of training set from the available plant data which is followed by use of training set for developing data driven linear and non-linear soft sensor models for continuous quality monitoring in petroleum refinery. Three data sets from three different processes in the petroleum refinery were investigated. The three data sets belong to ethane-ethylene distillation, debutanization and sulphur recovery process. Five different training set design techniques were applied separately to the three process datasets. These include Kennard-Stone, Duplex, SPXY, KSPXY and SPXYE techniques. Different sets of training data and validation data are designed for the three processes using the five techniques. The resulting training set data are used to develop linear (Multiple Linear Regression) and non-linear (Regression Neural Network) models of the three processes. The resulting validation set data are used to test the generalization ability of the developed models. Subsequently, the function computation time for all five techniques on the three process datasets were determined. It was observed that the duplex technique resulted in the best representative training set. However, the training sets designed from Kennard-Stone and SPXYE techniques resulted in models with best prediction performance with unknown data. The regression neural network models developed from the training set obtained by using Kennard-Stone algorithm for the debutanizer column and sulphur recovery unit are also found to perform better than some other data driven models reported in the literature.Item Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network(Elsiever, 2015-05) Mohanta, Hare Krishna; Pani, Ajaya KumarParticle size soft sensing in cement mills will be largely helpful in maintaining desired cement fineness or Blaine. Despite the growing use of vertical roller mills (VRM) for clinker grinding, very few research work is available on VRM modeling. This article reports the design of three types of feed forward neural network models and least square support vector regression (LS-SVR) model of a VRM for online monitoring of cement fineness based on mill data collected from a cement plant. In the data pre-processing step, a comparative study of the various outlier detection algorithms has been performed. Subsequently, for model development, the advantage of algorithm based data splitting over random selection is presented. The training data set obtained by use of Kennard–Stone maximal intra distance criterion (CADEX algorithm) was used for development of LS-SVR, back propagation neural network, radial basis function neural network and generalized regression neural network models. Simulation results show that resilient back propagation model performs better than RBF network, regression network and LS-SVR model. Model implementation has been done in SIMULINK platform showing the online detection of abnormal data and real time estimation of cement Blaine from the knowledge of the input variables. Finally, closed loop study shows how the model can be effectively utilized for maintaining cement fineness at desired value.