19 research outputs found
Simulation Studies of Low-density Polyethylene Production in a Tubular Reactor
No full textAbstractThis paper deals with the model based of an industrial low-density polyethylene (LDPE) tubular reactor. As the LDPE industry becomes more competitive, manufacturers have to come out with solutions to debottleneck the reactor output while abiding to the stringent product specification. In other words, they have to deal with maximization of the production (maximization of the monomer conversion for a given feed flow rate) and minimization of unwanted product (ethyl, butyl, vinyl and vinylidene groups), while maintaining the polymer product quality with regards to its molecular weight distribution (MWD). To achieve these goals, understanding the effects of operating variables manipulation as well as the dynamic behavior of tubular reactor is essential to develop high performance tubular reactor. In this study, the dynamic model of a tubular reactor for the production of low-density polyethylene (LDPE) is simulated using MATLAB R2015a® in order to predict the temperature profile and monomer conversion percentage along the tubular reactor. The model consists of feed stream, reactor jacket, initiator injector and outlet stream. Several operating variables are involved, notably the feed flow rates, the inlet pressure and temperature with varying parameters to analyze the effect on the reactor productivity. Plots of reactor temperature profile and monomer conversion percentage are presented and from the result, it can be concluded that feed temperature gives significant impact as it effects monomer conversion rate
Dynamic optimisation and control of batch reactors. Development of a general model for batch reactors, dynamic optimisation of batch reactors under a variety of objectives and constraints and on-line tracking of optimal policies using different types of advanced control strategies.
No full textBatch reactor is an essential unit operation in almost all batch-processing
industries. Different types of reaction schemes (such as series, parallel and complex)
and different order of model complexity (short-cut, detailed, etc. ) result in different sets
of model equations and computer coding of all possible sets of model equations is
cumbersome and time consuming. In this work, therefore, a general computer program
(GBRM - General Batch Reactor Model) is developed to generate all possible sets of
equations automatically and as required. GBRM is tested for different types of reaction
schemes and for different order of model complexity and its flexibility is demonstrated.
The above GBRM computer program is lodged with Dr. I. M. Mujtaba.
One of the challenges in batch reactors is to ensure desired performance of
individual batch reactor operations. Depending on the requirement and the objective of
the process, optimisation in batch reactors leads to different types of optimisation
problems such as maximum conversion, minimum time and maximum profit problem.
The reactor temperature, jacket temperature and jacket flow rate are the main control
variables governing the process and these are optimised to ensure maximum benefit. In
this work, an extensive study on mainly conventional batch reactor optimisation is
carried out using GBRM coupled with efficient DAEs (Differential and Algebraic
Equations) solver, CVP (Control Vector Parameterisation) technique and SQP
(Successive Quadratic Programming) based optimisation technique. The safety,
environment and product quality issues are embedded in the optimisation problem
formulations in terms of constraints. A new approach for solving optimisation problem
with safety constraint is introduced. All types of optimisation problems mentioned
above are solved off-line, which results to optimal operating policies.
The off-line optimal operating policies obtained above are then implemented as
set points to be tracked on-line and various types of advanced controllers are designed
for this purpose. Both constant and dynamic set points tracking are considered in
designing the controllers. Here, neural networks are used in designing Direct Inverse
and Inverse-Model-Based Control (IMBC) strategies. In addition, the Generic Model
Control (GMC) coupled with on-line neural network heat release estimator (GMC-NN)
is also designed to track the optimal set points. For comparison purpose, conventional
Dual Mode (DM) strategy with PI and PID controllers is also designed. Robustness tests
for all types of controllers are carried out to find the best controller. The results
demonstrate the robustness of GMC-NN controller and promise neural controllers as
potential robust controllers for future. Finally, an integrated framework
(BATCH REACT) for modelling, simulation, optimisation and control of batch
reactors is proposed.University Sains Malaysi
Neural-Wiener-based Model Predictive Control (NWMPC) for Methyl Tert-butyl Ether Catalytic Distillation
No full textThe reactive distillation of methyl tert-butyl ether (MTBE) involves strong
interactions between variables and is a highly nonlinear process. Here, a nonlinear
model predictive control (MPC) was proposed to tackle the nonlinearity and the
interaction involved in controlling the tray temperature in MTBE reactive distillation. To
improve the performance of the MPC, an advanced nonlinear block-oriented model
known as the neural Wiener model was employed. The control study was successfully
simulated using Simulink (Matlab), which is integrated with the Aspen dynamic model.
Set-point tracking, disturbance rejection and robustness tests were conducted to evaluate
the neural-Wiener-based MPC (NWMPC) performance. The results achieved show that
the NWMPC is able to maintain the product purity at its set-point of 99%, with isobutene
conversion exceeding 99.98%. NWMPC is also able to reject disturbances, as shown in
disturbance rejection study performed by changing the feed flowrate to 30% of the
nominal value. This controller is also very robust and thus able to control the MTBE
reactive distillation, even when the column efficiency was reduced to 80%
Comparison of various Wiener model identification approach in modelling nonlinear process
No full textSimulation of DMC Transesterification Reaction using ASPEN PLUS
No full textComputer simulation has been widely used in chemical engineering processes and its implementation in biodiesel industry is very useful. In this study, a pilot plant scale of DMC transesterification reaction is simulated and validated using ASPEN PLUS software
Implementation of model predictive control in tracking dynamic optimal profiles of semi batch autocatalytic esterification reactor
No full textDevelopment And On-Line implementation Of Nonlinear Model Predictive Control Strategies In Batch Reactor Process
No full textFiring of Limestone in JPN Pilot Plant
No full textLimestone is a natural occurring mineral throughout the world. It is widely used in the cement manufacturing, construction and building, metal refining processes, quicklime industries, agricultural industries, flue gases treatments, etc. The chemical composition of the limestone varies from one region to the other region, but it mainly consists of calcium carbonate. In the Peninsular Malaysia, 7 billion tonnes reserve of limestone has been identified by the Geological Survey Department, Malaysia. It is found that 55% of the limestone in Peninsular Malaysia contains 95% calcium carbonate (Sulaiman, 1996). It is feasible to be used as raw materials to produce quicklime
Feed Forward Neural Network Model for Isopropyl Myristate Production in Industrial-scale Semi-batch Reactive Distillation Columns
No full textThe application of the artificial neural network (ANN) model in chemical
industries has grown due to its ability to solve complex model and online application
problems. Typically, the ANN model is good at predicting data within the training range
but is limited when predicting extrapolated data. Thus, in this paper, selected optimum
multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) models
are used to predict the bottom (xb) compositions of extrapolated data. The MIMO and
MISO models both managed to predict the extrapolated data with MSE values of 0.0078
and 0.0063 and with R2 values of 0.9986 and 0.9975, respectively
Online Dynamic Optimization Strategy for Handling Disturbance in Semi Batch Autocatalytic Esterification Process: Application of Hybrid Optimizer and Simple Re-optimization Activator
No full text
