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Yes / This work investigates how batch reactors can be optimized to increase the yield of a desired product coupling two appealing techniques for process control and optimization: the nonlinear model predictive control (NMPC) and the dynamic real-time optimization (D-RTO). The overall optimization problem is formulated and applied to calculate the optimal operating parameters of a selected case study and the numerical results are compared to the traditional control/optimization techniques. It has been demonstrated in our previous work (Pahija et al, Selecting the best control methodology to improve the efficiency of discontinuous reactors, Computer Aided Chemical Engineering, 32, 805-810, 2013) that the control strategy can significantly affect optimization results and that the appropriate selection of the control methodology is crucial to obtain the real operational optimum (with some percent of improved yield). In this context, coupling NMPC and D-RTO seems to be the ideal way to improve the process yield. The results presented in this work have been obtained by using gPROMS® and MS C++ with algorithms of BzzMath library.

In recent years, the field of evolutionary dynamic optimisation has seen significant increase in scientific developments and contributions. This is as a result of its relevance in solving academic and real-world problems. Several techniques such as hyper-mutation, hyper-learning, hyper-selection, change detection and many more have been developed specifically for solving dynamic optimisation problems. However, the complex structure of algorithms employing these techniques make them unsuitable for real-world, real-time dynamic optimisation problem using embedded systems with limited memory. The work presented in this thesis focuses on a compact approach as an alternative to population based optimisation algorithm, suitable for solving real-time dynamic optimisation problems. Specifically, a novel compact dynamic optimisation algorithm suitable for embedded systems with limited memory is presented. Three novel dynamic approaches that augment and enhance the evolving properties of the compact genetic algorithm in dynamic environments are introduced. These are 1.) change detection scheme that measures the degree of dynamic change 2.) mutation schemes whereby the mutation rates is directly linked to the detected degree of change and 3.) change trend scheme the monitors change pattern exhibited by the system. The novel compact dynamic optimization algorithm outlined was applied to two differing dynamic optimization problems. This work evaluates the algorithm in the context of tuning a controller for a physical target system in a dynamic environment and solving a dynamic optimization problem using an artificial dynamic environment generator. The novel compact dynamic optimisation algorithm was compared to some existing dynamic optimisation techniques. Through a series of experiments, it was shown that maintaining diversity at a population level is more efficient than diversity at an individual level. Among the five variants of the novel compact dynamic optimization algorithm, the third variant showed the best performance in terms of response to dynamic changes and solution quality. Furthermore, it was demonstrated that information transfer based on dynamic change patterns can effectively minimize the exploration/exploitation dilemma in a dynamic environment.

Yes / Multi-Stage Flash (MSF) desalination process has been used for decades for making fresh water from seawater and is the largest sector in desalination industries. In this work, dynamic optimisation of MSF desalination is carried out using powerful and robust dynamic simulation and optimisation software called gPROMS model builder. For a fixed freshwater demand, a number of optimal combinations of the factors such as heat transfer area, brine flow rate, cooling water flow rate, steam flow in brine heater, Top Brine Temperature, the number of stages, etc. are determined with the objective of maximising the performance ratio of the process (defined as the amount of fresh water produced per unit of energy input) considering the seasonal variations. An attempt has been made to develop an operational schedule for a particular day using dynamic optimisation.

No / A novel threefold optimization algorithm is proposed to simultaneously solve the nonlinear model predictive control and dynamic real-time optimization for batch processes while optimizing the batch operation time. Object-oriented programming and parallel computing are exploited to make the algorithm effective to handle industrial cases. A well-known literature case is selected to validate the algorithm.

yes / This work describes how the design and operation parameters of the Multi-Stage Flash (MSF) desalination process are optimised when the process is subject to variation in seawater temperature, fouling and freshwater demand throughout the day. A simple polynomial based dynamic seawater temperature and variable freshwater demand correlations are developed based on actual data which are incorporated in the MSF mathematical model using gPROMS models builder 3.0.3. In addition, a fouling model based on stage temperature is considered. The fouling and the effect of noncondensable gases are incorporated into the calculation of overall heat transfer co-efficient for condensers. Finally, an optimisation problem is developed where the total daily operating cost of the MSF process is minimised by optimising the design (no of stages) and the operating (seawater rejected flowrate and brine recycle flowrate) parameters.

Yes / The need for the development of clean but still profitable processes and the study of low environmental impact and economically convenient management policies for them are two challenges for the years to come. This paper tries to give a first answer to the second of these needs, limited to the area of discontinuous productions. It deals with the development of a robust methodology for the profitable and clean management of (fed-)batch units under uncertainty, which can be referred to as a robust sustainability-oriented model-based optimization & control strategy. This procedure is specifically designed to ensure elevated process performances along with low-cost utilities usage reduction in real-time, simultaneously allowing for the effect of any external perturbation. In this way, conventional offline methods for process sustainable optimization can be easily overcome since the most suitable management policy, aimed at process sustainability, can be dynamically determined and applied in any operating condition. This leads to a significant step forward with respect to the nowadays options in terms of sustainable process management, that drives towards a cleaner and more energy-efficient future. The proposed theoretical framework is validated and tested on a case study based on the well-known fed-batch version of the Williams-Otto process to demonstrate its tangible benefits. The results achieved in this case study are promising and show that the framework is very effective in case of typical process operation while it is partially effective in case of unusual/unlikely critical process disturbances. Future works will go towards the removal of this weakness and further improvement in the algorithm robustness.

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.

Dans ce travail, une approche d’optimisation dynamique hybride est développée et utilisée pour simuler et optimiser les procédés d’adsorption modulée en pression (PSA). Elle est principalement basée sur la formulation hybride du modèle du procédé et sur l’utilisation de la méthode du système adjoint.Le problème de simulation qui consiste à déterminer le régime stationnaire cyclique (CSS) est formulé comme un problème d’optimisation où le critère de performance est défini par la condition de CSS, les variables de décision sont données par les valeurs initiales des variables d’état, et les contraintes par le modèle hybride du procédé avec les conditions aux limites associées. En optimisation, le vecteur des variables de décision contient, en plus des valeurs initiales de l’état, les paramètres de dimensionnement et de fonctionnement. La condition de CSS devient, dans ce cas, une contrainte à satisfaire par chaque solution optimale. Plusieurs modèles de procédés, allant du plus simple au plus compliqué, sont ´étudiés.Il s’agit notamment de procédés isothermes et non isothermes avec et sans états gelés. Les critères de performance considérés sont la pureté, la récupération et l´énergie. Les résultats obtenus aussi bien au niveau des performances des procédés considérés que de la robustesse de l’algorithme d’optimisation mis en œuvre, sont tout `a fait intéressants et montrent le grand potentiel de l’approche développée pour le dimensionnement et le fonctionnement optimaux des procédés PSA / The objective of the work was to develop a hybrid dynamic optimisation approach for simulation and optimisation of pressure swing adsorption (PSA) processes. It is mainly based on the hybrid formulation of the process model and on the use of adjoint system method. The simulation problem which consists in determining the cyclic steady state (CSS) is formulated as an optimisation problem where the CSS condition is considered as the performance index, initial values of state variables as decision variables and process model along with associated conditions as constraints. In optimisation, the decision vector consists of design and operation parameters in addition to the initials values of state variables whereas the CSS condition is considered in this case as a constraint to be satisfied for each optimal solution. Several process models with a varied degree of complexity have been studied. These models are isothermal and non isothermal with and without frozen states. The performance index considered are purity, recovery and energy. The results obtained are interesting vis-a-vis the performance of the processes considered as well as the robustness of the optimisation algorithm and show the great potential of the approach developed for the optimal design and operation of PSA processes