Modelling and control strategies for extractive alcoholic fermentation: partial control approach
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The vast majority of chemical and biochemical process plants are normally characterized by large number of measurements and relatively small number of manipulated variables; these thin plants have more output than input variables. As the number of manipulated variables restricts the number of controlled variables, thin plant has presented a daunting challenge to the engineers in selecting which measured variables to be controlled. In general, this is an important problem in modern process control today, because controlled variables selection is one of the key questions which must be carefully addressed in order to effectively design control strategies for process plants. While the issue relating to controlled variables selection has remained the key question to be resolved since the articulation of CSD problem by Foss in 1970s, the work described in this thesis points out to another equally important question in CSD, that is, what is the sufficient number of controlled variables required? Thinking over this question leads one to the necessity for gaining a rational understating of the governing principle in partial control design, namely the variables interaction.In this thesis, we propose a novel dataoriented approach to solving the control structure problem within the context of partial control framework. This approach represents a significant departure from the mainstream methods in CSD, which currently can be broadly classified into two major categories as the mathematicaloriented and heuristichierarchical approaches. The key distinguishing feature of the proposed approach lies in its adoption of technique based on the Principal Component Analysis (PCA), which is used to systematically determine the suitable controlled variables. Conversely, the determination of the controlled variables in mathematicaloriented and heuristichierarchical approaches is done via the mathematical optimization and process knowledge/engineering experience, respectively. It is important to note that, the dataoriented approach in this thesis emerges from the fusion of two important concepts, namely the partial control structure and PCA. While partial control concept provides the sound theoretical framework for addressing the CSD problem in a systematic manner, the PCAbased technique helps in determining not only the suitable controlled variables but also the sufficient number of controlled variables required.Since the classical framework of partial control is not amendable to a systematic way in the identification of controlled variables, it is necessary to develop a new framework of partial control in this thesis. Within this new framework the dominant variable can be clearly defined, and which in turn allows the incorporation of PCAbased technique for the systematic identification of controlled variables.The application of the dataoriented approach is demonstrated on a nonlinear multivariable bioprocess case study, called the twostage continuous extractive (TSCE) alcoholic fermentation process. The system consists of 5 interlinked units: 2 bioreactors in series, a centrifuge, vacuum flash vessel and treatment tank. The comparison of the twostage design with that of singlestage design reported in literature shows that: (1) both designs exhibit comparable performance in term of the maximum allowable tradeoff values between yield and productivity, and (2) twostage design exhibits stronger nonlinear behaviour than that of singlestage. Thus, the design of control strategies for the former is expected to be more challenging.Various partial control strategies are developed for the case study, such as basic partial control strategy, complete partial control strategies with and without PID enhancement technique and optimal size partial control strategy. Note that, this system consists of 16 output variables and only 6 potential manipulated variables, which has approximately 4,000,000 control structure alternatives. Therefore, the application of mathematical approach relying on optimization is not practical for this case study – i.e. assuming that evaluation of each alternative takes 30 seconds of optimization time, thus, complete screening will require almost 4 years to complete.Several exciting new insights crystallize from the simulation study performed on the case study, where two of them are most important from the perspective of effective design of partial control strategy: 1) There is an optimal size of partial control structure where too many controlled variables can lead to the presence of bottleneck controlloop, which in turn can severely limit the dynamic response of overall control system. On the other hand, too few controlled variables can lead to unacceptable variation or loss in performance measures. 2) The nature of variables interaction depends on the choice of control structure. Thus, it is important to ensure that the nature of openloop variables interaction is preserved by the implementation of a particular control strategy. When this is achieved, then we say that this control system works synergistically with the inherent control capability of a given process – i.e. achieving the synergistic externalinherent control system condition.The proposed approach has been successfully applied to the case study, where the optimal partial control structure is found to be 3x3 i.e. 3 controlled variables are sufficient to meet all 3 types of control objectives: overall (implicit) performance objectives, constraint and inventory control objectives. Finally, the proposed approach effectively unifies the advantages of both mathematicaloriented and heuristichierarchical approaches, and while at the same time capable of overcoming many limitations faced by these two mainstream approaches.
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