Department of Chemical Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1923

Browse

Author: search.filters.author.Mohanta, Hare KrishnaSubject: search.filters.subject.Chemical engineering

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    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 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.
  • No Thumbnail Available
    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 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.
  • No Thumbnail Available
    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 Christina
    A 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 powder
  • No Thumbnail Available
    Item
    Machine learning applied to predict key petroleum crude oil constituents
    (Wiley, 2023-10) Mohanta, Hare Krishna; Sande, Priya Christina
    Sulfur 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.
  • No Thumbnail Available
    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 Kumar
    The 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.