Batch 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 Malaysia
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/4402 |
| Date | January 2001 |
| Creators | Aziz, Norashid |
| Contributors | Mujtaba, Iqbal M. |
| Publisher | University of Bradford, Department of Chemical Engineering |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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