Department of Chemical Engineering

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Author: search.filters.author.Pani, Ajaya Kumar

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Now showing 1 - 10 of 28
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    Designing intelligence: harnessing soft sensors and advanced analytics in petroleum refining for industry 4.0
    (CRC Press, 2025) Pani, Ajaya Kumar
    Industry 4.0 is revolutionizing process industries by transforming vast data sets into actionable insights, improving efficiency, reducing downtime, and ensuring consistent product quality while meeting environmental standards. A key driver of this transformation is the development of intelligent, software-driven soft sensors, which provide real-time analytics for monitoring crucial process parameters traditionally measured through slower offline methods. This chapter explores the design and implementation of soft sensors, emphasizing the integration of artificial intelligence (AI) and machine learning (ML) in real-time quality monitoring within the petroleum refining industry. Over the past 15 years, these advanced sensors have significantly enhanced monitoring and quality assurance, enabling precise control over raw material composition and final product quality. By examining different soft sensor models and their applications, the chapter highlights their impact on industrial operations, as well as challenges such as managing large data streams and ensuring sensor reliability. Through real-world case studies, it demonstrates the practical benefits of soft sensors and their role in advancing the oil and gas sector toward Industry 4.0, paving the way for innovation and enhanced operational intelligence.
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    Industrial process monitoring using support vector data description: A systematic review and application for fault detection in multiphase flow system
    (Elsevier, 2025-12) Pani, Ajaya Kumar
    In the era of Industry 4.0, machine learning based data-driven techniques are increasingly explored for industrial process monitoring. This article presents a review on application of support vector data description (SVDD) for industrial process monitoring followed by the design of SVDD model for fault detection in a multiphase flow system. In the review section, the basic technique, open design issues and a detailed survey on industrial applications are presented. In the application part, PRONTO benchmark multiphase flow dataset, is used to design SVDD model for detection of three faults: air leakage, air blockage and diverted flow. The Gaussian kernel parameter of the SVDD model is determined using particle swarm optimization (PSO) and the starting value for PSO, is obtained from literature provided analytical formula. Simulation of PSO-SVDD models shows promising results for fault detection in multiphase flow system.
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    Non-negative matrix factorization combined with Fuzzy C-means enhanced k-nearest neighbor for fault detection and diagnosis in process industries
    (Elsevier, 2026-01) Mohanta, Hare Krishna; Garg, Girish Kant; Pani, Ajaya Kumar
    In the context of Industry 4.0, modern industrial processes generate high-dimensional, non-negative and potentially non-linear data streams, posing significant challenges for effective fault detection and diagnosis. Traditional statistical and multivariate techniques mostly assume restrictions such as a Gaussian distribution and linear relationships, which limit their use in real-world problems. This paper proposes a novel hybrid technique, Non-negative Matrix Factorization (NMF)–Enhanced Local Weighting Fuzzy C-Means (FCM) with Distance-Based k-Nearest Neighbors (NEFkNN), for fault detection. Initially, NMF is applied for dimensionality reduction. This is followed by FCM clustering, where cluster centers were refined with an enhanced local weighting (ELW) strategy. Detection threshold is determined by calculating the Euclidean distance between each sample and the enhanced cluster centers. A cluster-sensitive feature attribution method called Cluster-Aware Residual Contribution Analysis (CARCA) is proposed for fault diagnosis, which adjusts each feature's contribution to a fault by accounting for the local variance within its assigned cluster, enhancing interpretability. The NEFkNN technique was evaluated on two benchmark systems of a wastewater treatment plant(WWTP) and a continuous stirred tank reactor(CSTR) and achieved high fault detection rates and low false alarm rates. The diagnosis indicates that the fault is highly localized and attributable to a single process variable.
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    Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach
    (Elsevier, 2022-04) Mohanta, Hare Krishna; Pani, Ajaya Kumar
    Real time estimation of target quality variables using soft sensor relevant to time varying process conditions will be a significant step forward in effective implementation of Industry 4.0. Generalized Regression neural network (GRNN) has been used as a steady state quality monitoring soft sensor with reasonable estimation accuracy. However, the accurate prediction capability of GRNN has rarely been explored in a time varying environment. This article reports design of adaptive soft sensor using GRNN as a local model in Just-in-Time learning (JITL-GRNN) framework. The JITL-GRNN adaptive soft sensing technique is further investigated in various dimensions such as, the effect of different similarity index criteria and relevant dataset size on model prediction accuracy and model computation time. Performance of the proposed JITL-GRNN soft sensor is investigated by assessing its prediction accuracy on two benchmark industrial datasets. In addition, dynamic Non-linear autoregressive with exogenous inputs (NARX) neural network model is also developed and the performance of NARX model was compared with the proposed JITL-GRNN model. Results show that the JITL-GRNN adaptive soft sensor has at par or better prediction capability than the NARX model and many other models reported in literature.
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    Optimizing process monitoring efficiency through control limit adjustment in multivariate ewma charts
    (IEEE, 2024-10) Pani, Ajaya Kumar
    Statistical process control charts have proven to be helpful in process monitoring. The majority of previous research on SPC charts has been on univariate scenarios. This study builds a multivariate exponentially moving average (MEWMA) chart to perform process monitoring in a continuous stirred tank reactor (CSTR). The normal and faulty data were obtained from the Simulink model of CSTR. Smoothing parameter of EWMA was optimized to maximize process monitoring efficiency. False alarm rate (FAR) and fault detection rate (FDR) were used for calculating the monitoring efficiency. A novel control limit calculation combining T2 and square prediction error (SPE) is proposed to increase the accuracy of MEWMA technique.
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    Asymmetric mixed matrix membranes with zeolite imidazolate frameworks (ZIF-8, ZIF-67, bimetallic ZIF-8/67) and polyethersulfone for high flux and high selective hydrogen separation
    (Wiley, 2025-03) Pani, Ajaya Kumar; Kuncharam, Bhanu Vardhan Reddy
    For producing high-purity hydrogen (H2) from hydrocarbon reforming, membrane-based separation can be used. In this study, mixed matrix polymer membranes using metal–organic framework (MOF) nanoparticles are explored to overcome the permeability-selectivity trade-off of traditional polymeric membranes. Highly permeable and highly H2 selective MMM using ZIF-8, ZIF-67, and bimetallic ZIF-8/67 MOFs were fabricated via a non-solvent induced phase inversion method by incorporating an intermediate solvent evaporation step. MMMs with 5, 10, and 15 wt.% of nanofillers loadings were prepared and tested for single gas (H2, CO2, CH4, and N2) permeability at 1–2 bar pressures. MMMs permeability and selectivities exceeded the Robeson upper bound (2008) for H2/CO2 separation, demonstrating the potential for obtaining high-purity hydrogen at low pressures. H2/N2 selectivity of 43.4, H2/CO2 selectivity of 27.86 for and H2/CH4 selectivity of 31.36 were obtained. Analytical techniques such as XRD, FTIR, and DSC were used to explain the transport mechanism in the MMMs. The cross-sectional structure and morphology of MMMs were analyzed with field-emission scanning electron microscope (FESEM) to provide insights into the membrane's porous structure.
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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.