Department of Chemical Engineering

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

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

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Now showing 1 - 10 of 22
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    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 Krishna
    This 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.
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    Particle image velocimetry investigations on multiphase flow in fluidized beds: A review
    (Elsevier, 2023-03) Mohanta, Hare Krishna; Sande, Priya Christina
    Fluidized 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.
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    BINIVOX catalyst for hydrogen production from ethanol by low temperature steam reforming (LTSR)
    (Springer, 2017-11) Roy, Banasri; Mohanta, Hare Krishna
    Nickel 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.
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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.
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    A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks
    (IEEE, 2013) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    Soft sensors play an important role in predicting the values of unmeasured process variables from knowledge of easily measured process variables. Online estimation of particle size is vital for efficient control of a grinding circuit. Due to high energy consumption in cement grinding processes and unavailability of reliable hardware sensors for continuous monitoring, soft sensors have tremendous scope of application in cement mills. Modern cement plants are increasingly using vertical roller mills for clinker grinding. While there have been some works reported in the literature about modelling of ball mills, very few research work is available on vertical roller mill modelling. In the present work a PCA based neural network model of a cement mill is developed based on the actual plant data for estimation of cement fineness. Real time data for all process variables relevant to cement grinding process were collected from a cement plant having a clinker grinding capacity of 235 TPH. The collected raw industrial data were pre processed for outlier removal and missing value imputation. Principal component analysis of the input data was performed to transform the original variables to a less number of un correlated principal components. The selected principal component scores were divided to a training set and a validation set using Kennard-Stone subset selection algorithm. The training set was used to develop a back propagation neural network model which was subsequently tested with the validation set. Simulations results show satisfactory prediction capabilities of the developed model over that of linear regression and principal component regression models.
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    Soft sensing of product quality in the debutanizer column with principal component analysis and feed-forward artificial neural network
    (Elsiever, 2016-06) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    In this work, data-driven soft sensors are developed for the debutanizer column for online monitoring of butane content in the debutanizer column bottom product. The data set consists of data for seven process inputs and one process output. The total process data were equally divided into a training set and a validation set using the Kennard–Stone maximal intra distance criterion. The training set was used to develop multiple linear regression, principal component regression and back propagation neural network models for the debutanizer column. Performances of the developed models were assessed by simulation with the validation data set. Results show that the neural network model designed using Levenberg–Marquardt algorithm is capable of estimating the product quality with nearly 95% accuracy. The performance of the neural network model reported in this article is found to be better than the performances of least square support vector regression and standard support vector regression models reported in the literature earlier.
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    Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique
    (Elsiever, 2016-12) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    This article addresses the issue of outlier detection in industrial data using robust multivariate techniques and soft sensing of clinker quality in cement industries. Feed-forward artificial neural network (back propagation, radial basis function and regression neural network) and fuzzy inference (Mamdani and Takagi-Sugeno (T-S)) based soft sensor models are developed for simultaneous prediction of eight clinker quality parameters (free lime, lime saturation factor, silica modulus, alumina modulus, alite, belite, aluminite and ferrite). Required input-output data for cement clinkerization process were obtained from a cement plant with a production capacity of 10000 t of clinker per day. In the initial data preprocessing activity, various distance based robust multivariate outlier detection techniques were applied and their performances were compared. The developed soft-sensors were investigated for their performance by computing various statistical model performance parameters. Results indicate that the accuracy and computation time of the T-S fuzzy inference model is quite acceptable for online monitoring of clinker quality.
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    Quality monitoring in petroleum refinery with regression neural network: Improving prediction accuracy with appropriate design of training set
    (Elsiever, 2019-02) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    The objective of this research is twofold. First, design of training set from the available plant data which is followed by use of training set for developing data driven linear and non-linear soft sensor models for continuous quality monitoring in petroleum refinery. Three data sets from three different processes in the petroleum refinery were investigated. The three data sets belong to ethane-ethylene distillation, debutanization and sulphur recovery process. Five different training set design techniques were applied separately to the three process datasets. These include Kennard-Stone, Duplex, SPXY, KSPXY and SPXYE techniques. Different sets of training data and validation data are designed for the three processes using the five techniques. The resulting training set data are used to develop linear (Multiple Linear Regression) and non-linear (Regression Neural Network) models of the three processes. The resulting validation set data are used to test the generalization ability of the developed models. Subsequently, the function computation time for all five techniques on the three process datasets were determined. It was observed that the duplex technique resulted in the best representative training set. However, the training sets designed from Kennard-Stone and SPXYE techniques resulted in models with best prediction performance with unknown data. The regression neural network models developed from the training set obtained by using Kennard-Stone algorithm for the debutanizer column and sulphur recovery unit are also found to perform better than some other data driven models reported in the literature.
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    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 Krishna
    Prediction 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.
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    Application of Wavelet Transform in Controls: A Review
    (IUP, 2005-03) Mohanta, Hare Krishna
    Wavelet analysis is an emerging field of mathematics that has provided new tools and algorithms suited for the type of problems encountered in process monitoring and control. In this paper, a review is presented for the applications of wavelet transform in advanced control systems, particularly in Model Predictive Control, Intelligent Control, Robust Control, Adaptive Control, Nonlinear Control, Process Modeling and Control, Process Identification and Control, Process Monitoring, Diagnosis and Control, Statistical Process Control and Optimal Control. The underlying principles in each of these control strategies have also been briefly discussed. Over the past few years, wavelets and wavelet-based analysis have found their way into many different fields of science and engineering. The wavelet transform is a tool that provides descriptions of functions or signals in the time-frequency plane (Daubechies, 1992). Using traditional mathematical tools, one can study the properties of a phenomenon either in time domain or frequency domain. Although the Fourier transform and its inverse allow a passage from one domain to another domain, it does not give a simultaneous view of the phenomenon in both domains. Wavelets, on the other hand, are the basis functions, which are localized in both time and frequency domains. A basis, which preserves the time-frequency information of the signal, is of significant advantage in control system synthesis and analysis. Wavelets, because of their time-frequency localization and multiresolution properties offer an efficient framework for representation and characterization of signals, especially that non-stationary in nature. Wavelets can also be useful in obtaining process models that describe the process behavior on different time scales. These properties are very attractive from process identification and controller design prospective and recently have been shown to be of significant advantage for some of the process engineering problems (Bakshi and Stephanopoulos, 1993; Palavajjhala et al., 1994).