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.
SupervisorMujtaba, Iqbal M.
Control vector parameterisation
Generic model control
Neural network techniques
Batch processing industries
GBRM - General Batch Reactor Model
Rights© 2001 Aziz, N. This work is licensed under a Creative Commons Attribution-Non-Commercial-Share-Alike License (http://creativecommons.org/licenses/by-nc-nd/2.0/uk).
InstitutionUniversity of Bradford
DepartmentDepartment of Chemical Engineering
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AbstractBatch 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.
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Dynamic Modelling and Optimization of Polymerization Processes in Batch and Semi-batch Reactors. Dynamic Modelling and Optimization of Bulk Polymerization of Styrene, Solution Polymerization of MMA and Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors using Control Vector Parameterization Techniques.Mujtaba, Iqbal M.; Ibrahim, W.H.B.W. (University of BradfordSchool of Engineering, Design & Technology, 2012-02-29)Dynamic modelling and optimization of three different processes namely (a) bulk polymerization of styrene, (b) solution polymerization of methyl methacrylate (MMA) and (c) emulsion copolymerization of Styrene and MMA in batch and semi-batch reactors are the focus of this work. In this work, models are presented as sets of differential-algebraic equations describing the process. Different optimization problems such as (a) maximum conversion (Xn), (b) maximum number average molecular weight (Mn) and (c) minimum time to achieve the desired polymer molecular properties (defined as pre-specified values of monomer conversion and number average molecular weight) are formulated. Reactor temperature, jacket temperature, initial initiator concentration, monomer feed rate, initiator feed rate and surfactant feed rate are used as optimization variables in the optimization formulations. The dynamic optimization problems were converted into nonlinear programming problem using the CVP techniques which were solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software. The process model used for bulk polystyrene polymerization in batch reactors, using 2, 2 azobisisobutyronitrile catalyst (AIBN) as initiator was improved by including the gel and glass effects. The results obtained from this work when compared with the previous study by other researcher which disregarded the gel and glass effect in their study which show that the batch time operation are significantly reduced while the amount of the initial initiator concentration required increases. Also, the termination rate constant decreases as the concentration of the mixture increases, resulting rapid monomer conversion. The process model used for solution polymerization of methyl methacrylate (MMA) in batch reactors, using AIBN as the initiator and Toluene as the solvent was improved by including the free volume theory to calculate the initiator efficiency, f. The effects of different f was examined and compared with previous work which used a constant value of f 0.53. The results of these studies show that initiator efficiency, f is not constant but decreases with the increase of monomer conversion along the process. The determination of optimal control trajectories for emulsion copolymerization of Styrene and MMA with the objective of maximizing the number average molecular weight (Mn) and overall conversion (Xn) were carried out in batch and semi-batch reactors. The initiator used in this work is Persulfate K2S2O8 and the surfactant is Sodium Dodecyl Sulfate (SDS). Reduction of the pre-batch time increases the Mn but decreases the conversion (Xn). The sooner the addition of monomer into the reactor, the earlier the growth of the polymer chain leading to higher Mn. Besides that, Mn also can be increased by decreasing the initial initiator concentration (Ci0). Less oligomeric radicals will be produced with low Ci0, leading to reduced polymerization loci thus lowering the overall conversion. On the other hand, increases of reaction temperature (Tr) will decrease the Mn since transfer coefficient is increased at higher Tr leading to increase of the monomeric radicals resulting in an increase in termination reaction.
Optimization of Batch and Semi-batch ReactorsPahija, E.; Manenti, F.; Mujtaba, Iqbal M. (2013)Batch and semi-batch reactors are widely used for fine chemical productions. The target in the fine chemical industry is to produce a high quality product and operational optimization is the key-element to match it. This work investigates how batch and semi-batch reactors can be optimized in order to increase the yield of a desired product. Optimization problem is formulated and applied to calculate the optimal operating parameters such as the reactor temperature and the feed flow rate. Comparison and considerations on the two reactor configurations are given.
Neural network based modelling and control of batch reactor.Mujtaba, Iqbal M.; Aziz, Norashid; Hussain, M.A. (2006)The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better.