BITS Faculty Publications
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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.