Department of Chemical Engineering

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Author: search.filters.author.Pani, Ajaya KumarSubject: search.filters.subject.Chemical Engineering

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Now showing 1 - 10 of 22
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    Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems
    (Elsevier, 2023-03) Pani, Ajaya Kumar
    In process industries, early detection and diagnosis of faults is crucial for timely identification of process upsets, equipment and/or sensor malfunctions. Machine learning techniques using process data can be used as efficient process monitoring tools and is an active research area in the past two decades. The technique of independent component analysis (ICA) is a viable alternative to the widely used principal component analysis method. In this article, the basic ICA technique, its advantages, limitations and the various improvements proposed over the years are reviewed. Further, a detailed survey of ICA based techniques for process monitoring is presented. Finally, the application of ICA along with selection of independent components by negentropy calculation and control limit and monitoring index calculation is illustrated by an industrial case study of multiphase flow system
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    Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1)
    (Springer, 2023-07) Pani, Ajaya Kumar
    In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment process (WWTP) ensures increased efficiency and effluents meeting stringent emission norms. Benchmark simulation model No. 1 (BSM1) provides a simulation platform to researchers for developing efficient data-based process monitoring, quality monitoring, and process control systems for WWTPs. The present article presents a review of all research works reporting applications of various machine learning techniques for sensor and process fault detection of BSM1. The review focuses on process monitoring of biological wastewater treatment process, which uses a series of aerobic and anaerobic reactions followed by secondary settling process. Detailed information on various parameters monitored, different machine learning techniques explored, and results obtained by different researchers are presented in tabular and graphical format. In the review, it was observed that principal component analysis (PCA) and its variants account for the maximum number of research works for process monitoring in WWTPs and there are very few applications of recently developed deep learning techniques. Following the review and analysis, various future scopes of research (such as techniques yet to be explored or improvement of results for a particular fault) are also presented. These information will assist prospective researchers working on BSM1 to take forward the research.
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    An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes
    (IOP, 2024) Pani, Ajaya Kumar
    Principal component analysis (PCA) and independent component analysis (ICA), as well as their kernel extensions, have been widely applied in the past for industrial fault detection with Gaussian or non-Gaussian process data with linear or non-linear characteristics. Kernel-based techniques lead to computational complexity due to the high dimensionality of the dataset in the feature space. In this work, a randomization approach is used to obtain a low-rank approximation of the high-dimensional kernel matrix. A hybrid machine learning technique is proposed that integrates randomized kernel PCA (RKPCA) with ICA and Gaussian mixture modeling (GMM). The proposed approach, ICA-RKPCA-GMM, addresses the Gaussian and non-Gaussian characteristics of non-linear process data. Another hybrid algorithm combining three basic techniques of ICA, PCA and GMM is also developed (ICA-PCA-GMM). The fault detection performances of the proposed techniques (ICA-RKPCA-GMM and ICA-PCA-GMM) are compared with PCA, ICA, KPCA and combined ICA-PCA techniques by applying the techniques to two benchmark systems. Monitoring performances were evaluated by determining the false alarm rate and fault detection rate for different types of process and sensor faults. The simulation results show that the proposed ICA-RKPCA-GMM approach yields better results than individual ICA, PCA and KPCA techniques, the combined ICA-PCA and the proposed ICA-PCA-GMM technique.
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    Neural Network Soft Sensor Application in Cement Industry: Prediction of Clinker Quality Parameters
    (IEEE, 2011) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    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, C 3 S, C 2 S and C 3 A.
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    A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks
    (IEEE, 2013) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    Soft 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.
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    Inferential Sensing of Output Quality in Petroleum Refinery Using Principal Component Regression and Support Vector Regression
    (IEEE, 2017) Pani, Ajaya Kumar
    In this research, linear regression (ordinary least square and principal component) and non-linear regression (standard and least square support vector) models are developed for prediction of output quality from sulphur recovery unit. The hyper parameters associated with standard SVR and LS-SVR are determined analytically using the guidelines proposed in the literature. The relevant input-output data for process variables are taken from open source literature. The training set and validation set are statistically designed from the total data. The designed training data were used for design of the process model and the remaining validation data were used for model performance evaluation. Simulation results show superior performance of the standard SVR model over other models.
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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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    Back Pressure Monitoring of Power Plant Condenser Using Multiple Adaptive Regression Spline
    (Springer, 2020-02) Pani, Ajaya Kumar
    This research work involves the application of multivariate adaptive regression spline (MARS) for estimating back pressure (p) created in a condenser of a coal-fired thermal power plant. MARS employs the plant load (L) and temperature of cooling water (T) as input variables. The output of the MARS is condenser back pressure p^. The designed MARS-based model gives equations for determination of p. Further, the MARS-generated objective function is optimized by randomized search cross validation. Simulation study shows that the accuracy of the reported MARS model is quite satisfactory for the prediction of back pressure.
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    Variable selection and modeling from NIR spectra data: A case study of diesel quality prediction using LASSO and Regression Tree
    (IEEE, 2020-02) Pani, Ajaya Kumar
    The objective of this research is to design a model for predicting diesel fuel parameters from the data obtained from near infrared spectroscopic analysis of the fuel. Due to the complexity and the sheer number of peaks obtained in the spectral data, only those wavelengths that have a significant impact on the parameters are filtered out. Four types of variable selection techniques (LASSO, correlation coefficient, Mallow's Cp criterion, Relative sensitivity ratio) were applied on the NIR spectra data. Following variable selection, two models based on ridge regression and regression tree were developed. The models were used to successfully predict six diesel fuel parameters: cetane number, boiling point, freezing point, total aromatic content, viscosity and density from NIR spectra data. Variable selection by LASSO followed by regression tree modelling produced the best prediction accuracy.
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    Temperature Optimization in Non-isothermal Tubular Reactor using Genetic Algorithm
    (IEEE, 2020) Pani, Ajaya Kumar
    Genetic algorithm (GA) is a heuristic search algorithm that is inspired by evolution. It is a powerful optimization tool that uses the stochastic procedure with populations of initial guesses rather than using a single value like gradient-based methods. This prevents GA from being trapped in a local optimum. In the present work, GA applications to industrial optimization problems are thoroughly reviewed to get a perspective on different variations of genetic algorithms being used in industries. Subsequently, GA is applied to an industrial tubular reactor system where the technique is used to determine the optimum feed temperature at reactor inlet so that the product attains desirable temperature at the reactor outlet. In addition to successful application of GA, some other performances such as effect of mutation function and selection technique on the number of iterations are also investigated.