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Articles by M. Ramasamy
Total Records ( 11 ) for M. Ramasamy
  N. Yusoff and M. Ramasamy
  This study proposes an integrated framework of scheduling and Real-Time Optimization (RTO) of a Refrigerated Gas Plant (RGP). At the top layer, a high fidelity dynamic model of RGP is subjected to scheduling of plant operating mode from natural gas liquids to sales gas and vice-versa. Set points from mode scheduling are passed down to the steady-state RTO layer. Modeling mismatch is minimized by rigorously exchanging values of key variables between dynamic and steady-state models. Optimal trajectories of set points are obtained using sequential quadratic programming algorithm with constraints. These trajectories are disjointedly implemented by Model Predictive Control (MPC) scheme and Proportional-Integral (PI) controllers for comparison. Four case studies for each mode scheduling are performed to illustrate efficacy of the proposed approach.
  D.T. Lemma , M. Ramasamy and M. Shuhaimi
  The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous Input (ARX) and Finite Impulse Response (FIR) models. Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error, in addition ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model structures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few number of parameters (parsimonious in parameters). In addition, the resulting OBF models are consistent in parameters. The model parameters can be easily developed using linear least square method. In this study, OBF model development for simulation and real case studies is presented.
  Mohanad Osman and M. Ramasamy
  Distillation column is one of the most widely used unit operations in process industries and the operation and control of this unit was always a challenging task due to the complexity of operational parameters interactions and the lack of a real time measurement of the products composition. Time delay involved with GC measurement of the product composition prevented the utilization of effective closed-loop control. To overcome this problem an inferential control scheme based on soft sensor estimation of the products composition is now widely adopted. In this work, a neural network based soft sensor is developed to be used in an inferential control scheme of a pilot-scale binary distillation column. Data were collected from the distillation column under different operating conditions with forced disturbances in number of operation variables. The collected data is pre-processed for the removal of outliers, normalized and segmented into training and test data subsets. The most important process variables in the model and their lags have been chosen systematically. Different neural networks have been trained using the preprocessed data. Trial and error method is used to find the optimum number of neuron in the hidden layer for each network. The performance of different networks is discussed. The developed soft sensor can be utilized in an inferential control scheme on the distillation column.
  N. Yusoff and M. Ramasamy
  This study presents a procedure for selecting optimization variables in a Refrigerated Gas Plant (RGP) using Taguchi method with L27 (39) orthogonal arrays. A dynamic RGP model developed under HYSYS environment is utilized as a test bed. This model comprises 762 variables and 21 regulatory control loops. However, only 9 variables or factors with three level each are studied to determine their relative significance in maximizing RGP profit. These factors are prudently selected due to their relevance in maintaining product qualities. Feed Gas (FG) flow rate is found dominant with 97.3% contribution in the first case study. Two additional case studies are performed to magnify the contributions of other factors. FG costs and temperature of FG after coldbox E-101, refrigeration cooler duty and demethanizer reboiler duty are found to be significant factors.
  Aisha Osman Mohamed Ahmed , Abdulhalim Shah Maulud , M. Ramasamy and Shuhaimi Mahadzir
  The aim of this study is to obtain a steady state model that can simulate an industrial Residue Fluid Catalytic Cracking (RFCC) unit. This unit is one of the technologies for producing more gasoline from residue. The yield of gasoline in RFCC strongly depends on certain process variables. In this work, an RFCC model is developed by combining the material and energy balance equations with a 7-lump kinetic model and a modified two-dimensional hydrodynamic model. Simulation has been performed based on the data from an operating unit at Khartoum Refinery Company (KRC) and the results are reported. Optimum values of process variables for a required cracking efficiency, such as space velocity, catalyst to oil ratio and catalyst circulation rate, are also reported.
  Lemma D. Tufa , M. Ramasamy and M. Shuhaimi
  Closed-loop identification scheme using OBF-ARMAX model structure is presented. The proposed structure can be used to identify both open-loop stable and open-loop unstable processes that are stabilized by a feedback controller. The algorithm for estimating the model parameters and the formula for the multi-step ahead prediction are derived. The proposed identification scheme is demonstrated using two simulation case studies: One for open-loop stable and another for open-loop unstable. Both case studies demonstrate that the proposed scheme can be effectively used for closed-loop identification of both open-loop stable and open-loop unstable systems that are stabilized by a feedback control system.
  U.B. Deshannavar , M.S. Rafeen , M. Ramasamy and D. Subbarao
  Fouling in crude preheat train heat exchangers in refineries is a complex phenomenon. Crude oil fouling undergoes different mechanisms at different stages of preheating. Understanding the fouling mechanisms is essential in formulating appropriate fouling mitigation strategies. The use of the concept of threshold fouling conditions is one of the approaches for mitigating fouling through operating conditions. In this study, an attempt has been made to review the various fouling models available in literature, their advantages and limitations.
  Z.A. Chandio , M. Ramasamy and H. Mukhtar
  All of the organic fouling in petroleum refinery crude preheat train is caused by insoluble asphaltenes and the effect of bulk temperature on solubility of asphaltenes is somewhat uncertain. In this study, the effect of bulk temperature on flocculation and precipitation of asphaltenes in crude oil residue has been investigated at different temperatures from 20-95°C using automated flocculation titrimeter. It is observed that the solubility parameter and solvating power of the oil increased with the increase in temperature. The results indicate that solubility of the particles in oil and overall stability of the oil increased with the increase in temperature in the range studied.
  H. Zabiri , M. Ramasamy , L.D. Tufa and A. Maulud
  Model Predictive Control (MPC) is one of the most successful controllers in industries and widely applied in petroleum refining and petrochemical processes. Its inherent model-based strategy, however, renders it sensitive to changes that occur when the plants operate outside the boundaries of its original operating conditions. In this study, a nonlinear empirical model based on parallel orthonormal basis function-neural network structure, which has been shown to be able to extend the applicable regions of the model, is evaluated for its multi-step ahead prediction capability and compared to the conventional neural network models with different scaling procedures. It has been shown that the nonlinear model exhibited sufficient multi-step ahead prediction capability that renders it a promising candidate for MPC applications that can potentially improve the closed-loop control performance in extended regions and this is important in retaining the positive benefits of MPC in industries.
  Aisha Ahmed , A. Maulud , M. Ramasamy , K.K. Lau and S. Mahadzir
  The riser of an industrial RFCC unit is simulated using a steady-state multi-fluid Eulerian 3D model in ANSYS FLUENT workbench 14. The comprehensive hydrodynamics model together with 7-lump kinetic model describes the flow behavior and cracking reactions inside the riser very well. The radial variation in the axial particle velocity and particle volume fraction is found. The product distribution and temperature distribution along the riser shows very good agreement with the industrial RFCC plant data.
  Sampath Emani , M. Ramasamy and Ku Zilati Ku Shaari
  Crude oil fouling in processing equipment have been a major unresolved problem in petroleum industries. The underlying behavior of fouling precursors present in the crude oil have to be investigated to mitigate the deposit formations. In the present research, an attempt has been made to predict the asphaltenes deposition rate from crude oil in a heat exchanger tube through discrete phase CFD simulations. Various forces such as stochastic collision, thermophoretic, Saffman lift, coalescence and drag are applied on the particles to understand the transportation and adhesion mechanism of the asphaltenes particles. As asphaltenes have the tendency to aggregate irreversibly with different particle sizes, the transportation of asphaltenes particles is studied through varying the particles diameters. The propensity of asphaltenes particles in a Lagrangian frame is studied through the available discrete phase models in the commercial CFD Software Ansys Fluent.
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