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• W3033597564 abstract "The ongoing Quality-by-Design paradigm shift in the pharmaceutical industry has sparked a new interest in exploring advanced process control techniques to aid the efficient manufacture of high value products. One important process in the manufacturing is crystallization, a key process in purification of active pharmaceutical ingredients (APIs). There has been little crystallization control research in the area of global input/output linearization, otherwise referred to as state-feedback linearization (SFL). The global linearization allows a nonlinear model to be linearized over the whole domain for which the model is valid and can be embedded into a model predictive controller (MPC). MPC allows the control of a process based on a model which captures the physical understanding and constraints, but a widely reported challenge with the SFL technique is the poor ability of explicitly handling the plant constraints, which is not ideal for a highly regulated production environment such as pharmaceutical manufacturing. Therefore, the first purpose of this research is to explore the use of SFL and how it can be applied to controlling batch and continuous MSMPR crystallization processes with the incorporation of plant constraints in the MPC (named SFL-Plant constraints). The contribution made from this research is the exploration of the SFL MPC technique with successful implementation of SFL-Plant constraints. The novelty in this method is that the technique builds on existing SFL-MPC frameworks to incorporate a nonlinear constraints routine which handles plant constraints. The technique is applied on numerous scenarios of batch and continuous mixed suspension mixed product removal (MSMPR) supersaturation control of paracetamol in water, both seeded and unseeded, which all show that the SFL-Plant constraints technique indeed produces feasible control over crystallization subject to constraints imposed by limitations such as heat transfer. The SFL-MPC with SFL-Plant constraints was applied to single-input single-output (SISO) and multiple-input multipleoutput (MIMO) systems, demonstrating consistent success across both schemes of control. It was also determined that the SFL-Plant constraints do increase the computational demand by 2 to 5 times that of the SFL when unconstrained. However, the difference in absolute time is not so significant, typically an MPC which acted on a system each minute required less than 5 seconds of computation time with inclusion of SFL-Plant constraints. This technique 5 presents the opportunity to use the SFL-MPC with real system constraints with little additional computation effort, where otherwise this may have not been possible. A further advancement in this research is the comparison between the SFL-MPC technique to an MPC with a data-driven model - AutoRegression model with eXogenous input (ARX) – which is widely used in industry. An ARX model was identified for batch supersaturation control using a batch crystallization model of paracetamol in isopropyl alcohol (IPA) in gPROMS Formulated Products as the plant, and an ARX model developed in an industrial software for advanced process control – PharmaMV. The ARX-MPC performance was compared with SFL-MPC performance and it was found that although the ARX-MPC performed well when controlling a process which operated around the point the ARX-MPC was initially identified, the capability of tracking the supersaturation profile deteriorated when larger setpoints were targeted. SFL-MPC, on the other hand, saw some deterioration in performance quantified through an increase in output tracking error, but remained robust at tracking a wide range of supersaturation targets, thus outperforming the ARX-MPC for trajectory tracking control. Finally, single-objective and multi-objective optimization of a batch crystallization process is investigated to build on the existing techniques. Two opportunities arose from the literature review. The first was the use of variable-time decision variables in optimization, as it appears all pre-existing crystallization optimization problems to determine the ideal crystallization temperature trajectory for maximising mean-size are constructed of piecewise-constant or piecewise-continuous temperature profiles with a fixed time step. In this research the timestep was added as a decision variable to the optimization problem for each piecewise continuous ramp in the crystallization temperature profile and the results showed that for the maximisation of mean crystal length in a 300-minute batch simulation, when using 10 temperature ramps each of variable length resulted in a 20% larger mean size than that of 10 temperature ramps, each over a fixed time length. The second opportunity was to compare the performance of global evolution based Nondominated Sorting Genetic Algorithm – II (NSGA-II) with a deterministic SQP optimization method and a further hybrid approach utilising first the NSGA-II and then the SQP algorithm. It was found that for batch crystallization optimization, it is possible for the SQP to converge a global solution, and the convergence can be guaranteed in the shortest time with little compromise using the hybrid 6 method if no information is known about the process. The NSGA-II alone required excessive time to reach a solution which is less refined. Finally, a multi-objective optimization problem is formed to assess the ability to gain insight into crystallization operation when there are multiple competing objectives such as maximising the number weighted mean size and minimizing the number weighted coefficient of variation in size. The insight gained from this is that it is more time efficient to perform single-objective optimization on each objective first and then initialize the multi-objective NSGA-II algorithm with the single-objective optimal profiles, because this results in a greatly refined solution in significantly less time than if the NSGA-II algorithm was to run without initialization, the results were approximately 20% better for both mean size and coefficient of variation in 10% of the time with initialization." @default.
• W3033597564 created "2020-06-12" @default.
• W3033597564 creator A5043467155 @default.
• W3033597564 date "2020-06-02" @default.
• W3033597564 modified "2023-09-26" @default.
• W3033597564 title "Multi-objective optimization and model-based predictive control using state feedback linearization for crystallization" @default.
• W3033597564 cites W100464641 @default.
• W3033597564 cites W1481834144 @default.
• W3033597564 cites W1486238322 @default.
• W3033597564 cites W1532361062 @default.
• W3033597564 cites W1593833610 @default.
• W3033597564 cites W1602240847 @default.
• W3033597564 cites W1963646571 @default.
• W3033597564 cites W1963885647 @default.
• W3033597564 cites W1967824933 @default.
• W3033597564 cites W1968433538 @default.
• W3033597564 cites W1969172950 @default.
• W3033597564 cites W1971516068 @default.
• W3033597564 cites W1971923872 @default.
• W3033597564 cites W1972660405 @default.
• W3033597564 cites W1973790385 @default.
• W3033597564 cites W1981023943 @default.
• W3033597564 cites W1981734779 @default.
• W3033597564 cites W1985214721 @default.
• W3033597564 cites W1986310839 @default.
• W3033597564 cites W1986382822 @default.
• W3033597564 cites W1990425910 @default.
• W3033597564 cites W1991929271 @default.
• W3033597564 cites W1995877162 @default.
• W3033597564 cites W1996116266 @default.
• W3033597564 cites W1998506774 @default.
• W3033597564 cites W2009308555 @default.
• W3033597564 cites W2016026634 @default.
• W3033597564 cites W2016953731 @default.
• W3033597564 cites W2018080506 @default.
• W3033597564 cites W2020817180 @default.
• W3033597564 cites W2021774977 @default.
• W3033597564 cites W2025376439 @default.
• W3033597564 cites W2026145906 @default.
• W3033597564 cites W2028960364 @default.
• W3033597564 cites W2029359592 @default.
• W3033597564 cites W2031282207 @default.
• W3033597564 cites W2032964752 @default.
• W3033597564 cites W2033552200 @default.
• W3033597564 cites W2034115122 @default.
• W3033597564 cites W2035800336 @default.
• W3033597564 cites W2036601277 @default.
• W3033597564 cites W2037305294 @default.
• W3033597564 cites W2042974401 @default.
• W3033597564 cites W2043720622 @default.
• W3033597564 cites W2043837315 @default.
• W3033597564 cites W2044754264 @default.
• W3033597564 cites W2044838247 @default.
• W3033597564 cites W2044846792 @default.
• W3033597564 cites W2045312668 @default.
• W3033597564 cites W2045667156 @default.
• W3033597564 cites W2046347241 @default.
• W3033597564 cites W2049274051 @default.
• W3033597564 cites W2051065252 @default.
• W3033597564 cites W2051719523 @default.
• W3033597564 cites W2052585066 @default.
• W3033597564 cites W2055181122 @default.
• W3033597564 cites W2060668314 @default.
• W3033597564 cites W2060837182 @default.
• W3033597564 cites W2060844281 @default.
• W3033597564 cites W2065165510 @default.
• W3033597564 cites W2065694799 @default.
• W3033597564 cites W2067516819 @default.
• W3033597564 cites W2073787051 @default.
• W3033597564 cites W2073970522 @default.
• W3033597564 cites W2078296033 @default.
• W3033597564 cites W2079289120 @default.
• W3033597564 cites W2082792118 @default.
• W3033597564 cites W2084388913 @default.
• W3033597564 cites W2086544713 @default.
• W3033597564 cites W2088267743 @default.
• W3033597564 cites W2089580030 @default.
• W3033597564 cites W2089754337 @default.
• W3033597564 cites W2092074614 @default.
• W3033597564 cites W2094410628 @default.
• W3033597564 cites W2105940172 @default.
• W3033597564 cites W2109339972 @default.
• W3033597564 cites W2115955659 @default.
• W3033597564 cites W2117111271 @default.
• W3033597564 cites W2118938511 @default.
• W3033597564 cites W2122432234 @default.
• W3033597564 cites W2123189127 @default.
• W3033597564 cites W2124802932 @default.
• W3033597564 cites W2126105956 @default.
• W3033597564 cites W2130166249 @default.
• W3033597564 cites W2134673975 @default.
• W3033597564 cites W2135853794 @default.
• W3033597564 cites W2136397590 @default.
• W3033597564 cites W2142363776 @default.
• W3033597564 cites W2146545054 @default.
• W3033597564 cites W2147973136 @default.
• W3033597564 cites W2149416133 @default.
• W3033597564 cites W2153222991 @default.
• W3033597564 cites W2153247018 @default.
• W3033597564 cites W2166533645 @default.

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