Department of Chemical Engineering

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Author: search.filters.author.Pani, Ajaya KumarSubject: search.filters.subject.Soft sensors

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    Software sensor development for product concentration monitoring in fed-batch fermentation process using dynamic principal component regression
    (IEEE, 2018) Pani, Ajaya Kumar
    Monitoring and control of batch processes is more complicated than that of a continuous process. This is due to the fact that the properties change with time in a batch process. Penicillin production using fed-batch fermentation technique is one such process which is dynamic and highly non-linear in nature. In thsi research, a dynamic pricnipal component regression based soft-sensor model is proposed for continuous monitoring of penicillin concentration in the fed-bath fermentation reactor. The available data (generated using pensim simulator) were divided into training and validation data. The model was developed from the training data and accuracy testing was done by simulation of the model with validation data. Results show that the dynamic PCR model proposed in this work is able to capture the collinearity and dynamic nature of the data quite effectively and is able to predict the product concentration with good accuracy.
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    Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique
    (Elsiever, 2016-12) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    This article addresses the issue of outlier detection in industrial data using robust multivariate techniques and soft sensing of clinker quality in cement industries. Feed-forward artificial neural network (back propagation, radial basis function and regression neural network) and fuzzy inference (Mamdani and Takagi-Sugeno (T-S)) based soft sensor models are developed for simultaneous prediction of eight clinker quality parameters (free lime, lime saturation factor, silica modulus, alumina modulus, alite, belite, aluminite and ferrite). Required input-output data for cement clinkerization process were obtained from a cement plant with a production capacity of 10000 t of clinker per day. In the initial data preprocessing activity, various distance based robust multivariate outlier detection techniques were applied and their performances were compared. The developed soft-sensors were investigated for their performance by computing various statistical model performance parameters. Results indicate that the accuracy and computation time of the T-S fuzzy inference model is quite acceptable for online monitoring of clinker quality.
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    Application of Soft Sensors in Process Monitoring and Control: A Review
    (SSRN, 2010-01-04) Mohanta, Hare Krishna; Pani, Ajaya Kumar
    A major problem in product quality control in process industries is the difficulty of continuous online measurement of certain output variables especially related to composition. Although analytical instruments are available in some cases, significant time delays associated with most of such instruments make timely control difficult and sometimes impossible. Soft sensor is a modeling approach to estimate hard-to-measure process variables (primary variables) from easy-to-measure online process variables (secondary variables). The important steps of soft sensor development are collection of historical plant data for different variables and their processing, development of a model based on the available data and validation of the model. This paper presents the need and advantages of soft sensor implementation in process industries and does a critical review of various techniques available for data handling and modeling.
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    A survey of data treatment techniques for soft sensor design. Chemical Product and Process Modeling
    (https://www.degruyter.com/document/doi/10.2202/1934-2659.1536/html, 2011-01-03) Mohanta, Hare Krishna; Pani, Ajaya Kumar
    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.