Department of Chemical Engineering
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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 Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit(Elsiever, 2021-08-15) Pani, Ajaya Kumar; Mohanta, Hare KrishnaPrediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries. This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point (IBP) and end boiling point (EBP) in crude distillation unit. In this work, adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning (JITL) approach. The different types of local models designed are locally weighted regression (LWR), multiple linear regression (MLR), partial least squares regression (PLS) and support vector regression (SVR). In addition to model development, the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated. Results show that the JITL model based on support vector regression with iterative single data algorithm optimization (ISDA) local model (JITL-SVR:ISDA) yielded best prediction accuracy in reasonable computation time.Item Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table(IJCA, 2021-10-20) Mohanta, Hare Krishna; Bhanot, SurekhaOver 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.