Control of Reaction Systems via Rate Estimation and Feedback Linearization

<b>Abstract of the conference paper</b><br> The kinetic identification of chemical reaction systems often represents a time-consuming and complex task. This contribution presents an approach that uses rate estimation and feedback linearization to implement effective control without a kinetic model. The reaction rates are estimated by numerical differentiation of reaction variants. The approach is illustrated in simulation through the temperature control of a continuous stirred-tank reactor. <br><br> <b>Extended abstract</b><br> Model identification and controller design are often seen as closely related tasks, since the control law is calculated using the plant model. Previous control approaches based on extensive variables or inventories are examples of this strong dependence on the model [1, 2]. Since the identification of chemical reaction systems can be a time-consuming and complex task, one would ideally like to avoid it as much as possible. The concept of variant and invariant states allows isolating the different rates in chemical reaction systems, thereby facilitating analysis, monitoring and control [3-5]. Using this concept, one can estimate dynamic effects without the need of identifying the corresponding kinetic models. <br><br> This contribution presents a feedback linearization approach that is based on the estimation of unknown rates, such as the rates of reaction and mass transfer, thus allowing efficient control without the use of kinetic models. <br><br> Rate estimation uses the numerical differentiation of appropriately transformed extensive variables called rate variants that are invariant with respect to the manipulated variables. A rate variant contains all the information about the corresponding rate and, as such, is decoupled from the other unknown rates. Since it is possible to estimate the unknown rates this way, the controller does not require kinetic information. However, because of the differentiation step, the controller is most effective with frequent and precise measurements of several output variables. <br><br> Feedback linearization sets a rate of variation for the controlled variables, thereby guaranteeing quick convergence of these variables to their set points. For open chemical reactors, the parameters of the feedback linearization controller are determined by readily available information, such as the reaction stoichiometry, the heats of reaction, the inlet composition or the inlet and outlet flow rates. This novel control strategy is illustrated in simulation for the control of both concentration and temperature in a continuous stirred-tank reactor. <br><br> [1] Georgakis, Chem. Eng. Sci., <b>1986</b>, 41, 1471<br> [2] Farschman et al., AIChE J., <b>1998</b>, 44, 1841<br> [3] Asbjørnsen and Fjeld, Chem. Eng. Sci., <b>1970</b>, 25, 1627<br> [4] Bhatt et al., Ind. Eng. Chem. Res., <b>2011</b>, 50, 12960<br> [5] Srinivasan et al., IFAC Workshop on Thermodynamic Foundations of Mathematical Systems Theory, Lyon, <b>2013</b>.<br>

Published in:
Computer Aided Chemical Engineering, 37, 137-142
Presented at:
25th European Symposium on Computer Aided Process Engineering (ESCAPE) - PSE 2015, Copenhagen (Denmark), May 31 - June 4, 2015
Presented as a Keynote lecture

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 Record created 2014-08-31, last modified 2020-10-24

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