Hare Krishna Mohanta

Development and comparison of neural network based soft sensors for online estimation of cement clinker quality.

The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models.

A Survey of Data Treatment Techniques for Soft Sensor Design

Soft sensors have proved themselves as valuable alternatives to the traditional means for the acquisition of critical process variables, process monitoring and other tasks relating to process control. Most of the present day soft sensors for complex chemical processes are designed from actual industrial data because of the various difficulties associated with developing first principle models such as poor process understanding, impossible or difficult to determine model parameters and mathematical complexity of the models. This paper discusses characteristics of the process industry data which are critical for the design and development of data driven soft sensors. The focus of this paper is on the different shortcomings of the raw process data collected from the historical database and a review of different techniques available for processing of the raw data so as to make the data suitable for design of data driven soft sensors.

Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network

Particle 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.

Implementing Edge Detection for Detecting Neurons from Brain to Identify Emotions

Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Edge detection is a basic and important subject in computer vision and image processing In this Paper we discuss several Digital Image Processing Techniques applied in edge feature extraction. Firstly, Linear filtering of Image is done is used to remove noises from the image collected. Secondly, some edge detection operators such as Sobel, Log edge detection, canny edge detection are analyzed and then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the canny operator can obtain better edge feature. Finally, Edge detection is applied to identify neurons in Brain. After this the Neurons are classified and feature vector will be calculated. KeywordsFilters, Sobel, Canny, Log, Distortion, Edge Detection Introduction (Heading 1)

Data driven soft sensor of a cement mill using generalized regression neural network

Soft sensors play an important role in predicting the values of unmeasured process variables from knowledge of easily measured process variables. Most of the present day soft sensors for complex chemical processes are designed from actual industrial data because of the various difficulties associated with developing first principle models such as poor process understanding, impossible or difficult to determine model parameters and mathematical complexity of the models. No hardware sensors for online estimation of particle size is available for solid grinding units. In the present work a generalized regression neural network based model of a cement mill is developed based on the actual plant data for estimation of cement fineness. The collected raw industrial data is pre processed to get rid of the outliers and missing values followed by model development. Optimal selection of smoothing parameter for the regression model has been done by investigating the model performance at different spread values. Simulations results show satisfactory prediction capabilities of the developed model over that of linear regression model and quadratic response surface model.

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

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.

Self-tuned fuzzy logic control of a pH neutralization process

On-line implementation of self-tuning mechanism based adaptive fuzzy logic control of a pH neutralization process which takes care of steady state error and time taken to reach steady state under varying operating conditions has been presented in this paper. The pH neutralization system is Armfield pH Sensor Accessory (PCT42) in conjunction with Process Vessel Accessory (PCT41) and Multifunction Process Control Teaching System (PCT40). The proposed adaptive scheme updates the normalized universe of discourse of output fuzzy membership functions with varying scaling factors based on error and change of error values. The speed of response of the adaptive controller is taken care by use of coarse control technique whereas amount of deviation under steady state is accounted with the help of fine control technique. The performance of adaptive scheme is tested for pH control at equivalence point. LabVIEW software is used for online communication, control and display.

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control. General Terms Nonlinear process control, fuzzy logic control, neural network control.

Optimized adaptive neuro-fuzzy inference system for pH control

pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which genetic algorithm is used to optimize the various membership functions of fuzzy inference system. Further, using this optimized fuzzy inference system, adaptive neuro-fuzzy inference system for pH neutralization process is developed. Performances of both control schemes are compared for servo and regulatory operations. Results indicate that adaptive neuro-fuzzy inference system based control uses fewer rules as compared to optimized fuzzy logic based control.

Differential evolution based optimal fuzzy logic control of pH neutralization process

Differential evolution (DE) is a member of evolutionary algorithm family which has gained popularity due to its conceptual simplicity and better convergence. This paper presents fuzzy logic based pH control scheme for neutralization process in which DE is used to optimize the input and output membership functions of fuzzy inference system (FIS). The fitness function for optimization is integral of squared errors (ISE). DE is able to converge and find optimal global solution over narrow as well as wide search spaces. Finally the controller performance has been evaluated for servo and regulatory operations.