BITS Faculty Publications
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Item 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 KumarIn 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.Item 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 KumarReal 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.Item Study of fluidized bed freeboard for effect of Geldart A particles shape using particle image velocimetry (PIV)(Elsevier, 2024-01) Mohanta, Hare Krishna; Sande, Priya ChristinaA study was carried out using Particle Image Velocimetry (PIV) to reveal velocity vector flow fields in the freeboard region of a Geldart A fluidized bed. For the first time the effect of particle shape, was studied for three powder types, namely China clay (platelet-like), Diatomite (spherical) and Quartz (irregular). A bench-scale fluidized bed set-up of perspex glass was used for direct PIV imagining over a substantial inlet gas velocity range. Interestingly the freeboard was not found to be highly turbulent as has been reported in the case of Geldart B particles. Rather a transition regime was observed which was dominated by laminar streamlines and punctuated by short-lived eddies with life span even less than 400 μs. Bed expansion was significantly affected by particle shape. This was explained by an ‘effective cluster size’ hypothesis for the three powder types. The ‘uniformity’ of the velocity field was also quantified for each powderItem Machine learning applied to predict key petroleum crude oil constituents(Wiley, 2023-10) Mohanta, Hare Krishna; Sande, Priya ChristinaSulfur compounds are the most important inorganic constituents of petroleum and require to be estimated beforehand because of their corrosive nature and other processing anomalies during crude oil processing. Paraffins, naphthene, and aromatics form the bulk of crude oil. Machine learning (ML) predictions of these constituents were made by training the ML model with a diverse industrial data set of 515 oils. The XGBoost model gave an excellent R2 in the range 0.88–0.99 for the bulk compounds. R2 for sulfur was in the modest range of 0.45–0.6, which improved significantly to 0.8 for additional inputs. ML applicability was thereby found to depend on the nature of the constituent. This work furthers ML-based predictions, with the incentive of reducing expensive spectroscopic analytical methods.Item Digital image analysis of gas bypassing and mixing in gas-fluidized bed: effect of particle shape(Wiley, 2024-10) Mohanta, Hare Krishna; Goyal, Navneet; Sande, Priya Christina; Sharma, Arvind KumarThe study investigates effect of particle shape on gas bypassing and mixing of gas-fluidized Geldart A particles. A shallow fluidized bed (FB), configured at benchscale, was used with digital image analysis (DIA) for the investigation. The extent of scatter of tracer particles throughout the bed was assessed from DIA images of defluidized powder. A novel method employing Jupyter notebook software, was used to directly determine Mixing Index from digital images. Remarkably, platelet-shaped China clay powder displayed the best mixing characteristics (Mixing Index: 0.79) with no significant bypassing. Angular shaped Quartz displayed moderate mixing (Mixing Index: 0.67), but high bypassing (Bypassing Index: 0.75). Contrary to conventional assumptions, spherical-shaped diatomite exhibited poor mixing (Mixing Index: 0.61) with the highest bypassing (Bypassing Index: 0.82). Platelet particles performed well even with fines removal. Most likely, particle shape significantly influenced the number of available particle contact points, tracer migration, and traceronparticle binding.Item PIV investigation on the effect of gas distributor design for fluidization of Geldart A spherical particles(Elsevier, 2024-06) Sande, Priya Christina; Sharma, Arvind Kumar; Mohanta, Hare KrishnaThis is a hydrodynamics study on gas distribution during fluidization of the industrially relevant Geldart A type particles. The impact of 10 different distributor designs comprising perforated distributors (pitch and orifice arrangement pattern varied) and nozzle distributors (nozzle size varied) were studied experimentally on a bench-scale unit. Particle flow homogeneity in air was determined by a novel Particle Image Velocimetry (PIV) analysis of the freeboard velocity field. Over 50 PIV particle phase vector field images are presented and analysed. Bed expansion, Particle attrition with and without fines, velocity profiles with and without fines and effect of fines on bed expansion were ascertained. Best distributor design for a unit such as Fluid Catalytic Cracking (FCC) regenerator and FCC riser were found to be 0.8 cm and 0.6 cm nozzle plates distributors respectively. Confirmation with previous study revealed a correlation between homogeneous distribution of particulate flow and lower number of turbulence zones.Item Particle image velocimetry investigations on multiphase flow in fluidized beds: A review(Elsevier, 2023-03) Mohanta, Hare Krishna; Sande, Priya ChristinaFluidized Beds (FBs) are widely employed in the petroleum and coal energy sector because they offer excellent contact, both in terms of high surface area and long times. The last two decades has seen measurement on multiphase flows shift from conventional pressure sensors to direct flow image acquisition and processing. Particle Image Velocimetry or PIV, and PIV coupled with Digital Image Analysis or DIA, are used to directly and instantaneously acquire flow field data to make hidden flow patterns and flow structures discoverable. Research abounds on Gas-Solid FB hydrodynamics using PIV, but Liquid-Solid and Gas-Liquid-Solid systems are only slowly catching up. Similarly, the use of Geldart B and D particles for such studies is very common, whereas A and C type particle hydrodynamics is as yet largely unexplored by using imaging. Turbulence, high temperature, particle clusters, particle agglomeration and dense particle flows pose particular challenges to using PIV in FB. The two-zone FB and micro-FB warrant further attention. Small sized A & C type particles of rod-like, plate-like and angular shape provide huge scope for PIV investigations on FBs in the future. This review provides a concise account of several PIV studies on all types of FBs with focus on the past two decades, and also details the limitations of PIV measurements with future scope of work.Item BINIVOX catalyst for hydrogen production from ethanol by low temperature steam reforming (LTSR)(Springer, 2017-11) Roy, Banasri; Mohanta, Hare KrishnaNickel doped bismuth vanadate [;BINIVOX] calcined at (BINIVOX-800) catalyst is prepared by a solution combustion method. The catalytic activity study is carried in the temperature range of 250–, and with the molar feed ratios of water: ethanol at 23:1 and 2.5:1. The study reveals an increase in the ethanol conversion and selectivity of carbon dioxide & hydrogen but a decrease in the selectivity of carbon monoxide and methane with an increase in temperature and water: ethanol mole ratio. Fresh and used catalysts are characterized using DTA, TGA, XRD and FTIR. XRD results reveal that the fresh catalyst is phase pure γ-BINIVOX. The phase purity and crystallinity of the catalyst is retained after 30 h of activity study.Item Adaptive soft sensor design using a regression neural network and bias update strategy for non-linear industrial processes(IOP, 2023-05) Rout, Bijay Kumar; Mohanta, Hare Krishna; Pani, Ajaya KumarSoft sensing of quality parameters in process industries has been an active area of research for the past two decades. To improve the performance of soft sensors in the scenario of time varying process states, adaptation capability is incorporated into the soft sensor model. In this work, recursive (R), sliding window (SW) and just-in-time learning (JITL) frameworks are used for adaptive soft sensor design. A rarely explored modeling technique in the adaptation framework, the generalized regression neural network (GRNN) is used as a local modeling strategy. A bias update procedure is applied during the model adaptation activity to improve the prediction accuracy. Further, the performances of the developed models are tested against input–output data dimension mismatch along with various concept drift phenomena by considering a different number of labeled samples for inputs and outputs. The proposed adaptation strategy is applied on two benchmark industrial processes. Simulation results show that the GRNN local modeling approach combined with the bias update strategy gives higher prediction accuracy than other adaptive soft sensors proposed in the literature. Moreover, GRNN local modeling strategy using SW adaptation mechanism has the least computation time among the three adaptation methods due to the use of a low number of samples for model development.Item Optimized adaptive neuro-fuzzy inference system for pH control(IEEE, 2013) Bhanot, Surekha; Mohanta, Hare KrishnapH 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.