Thesis (Ph. D.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 195).

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: fixed bed; heat transfer. Includes bibliographical references (p. 67-71).

Thesis (doctoral)--Techische Universiteit Delft, 1998. / Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xvi, 190 p.; also includes graphics (some col.). Includes bibliographical references (p. 182-190). Available online via OhioLINK's ETD Center

Abstract The tightening environmental legislation and technological development in automotive engineering form a challenge in reactor design of catalytic reactors for automotive exhaust gas abatement. The catalytic reactor is the heart of the exhaust aftertreatment processes, but it can be seen also just as one subsidiary part of vehicles. The aim of this work is to reveal applicable kinetic models to predict behaviour of the particular catalysts and to establish guidelines for modelling procedures and experimentation facilitating catalytic reactor design, especially in the field of automotive exhaust gas abatement. The studies in this thesis include catalyst kinetics with synthetic exhaust gas composition in stoichiometric and net oxidative conditions, DRIFT measurements, and the warm-up of three-way catalysts in real conditions. Knowledge on surface concentrations facilitates kinetic model construction and discrimination. For example, identification of even semi-quantitative surface concentrations may lead to a successful falsification of incorrect kinetic model candidates. Especially, that is clearly seen in cases where models predict the same kind of gas phase behaviour but different kinds of surface concentration profiles. The transient kinetic experiments could give a hint on predominant reaction mechanism, support quantifying of the adsorption capacity and reveal the impact of surface phenomena on reactor dynamics. The level of model complexity should be adapted depending on the purpose of the model. For example, it is mostly convenient for reactor design purposes to perceive only one type of active sites even in a case of mechanical mixture of different catalytic materials; whereas the optimisation of catalyst content demands the management of every prominent site type separately. Or, when a catalytic material has been selected, the stationary kinetic model is, in most cases, adequate for the catalytic converter design and structural optimization for warm-up conditions.

This thesis is concerned with the design of a decoupling compensator and a time-delay compensator for a nonisothermal continuous flow stirred tank reactor (CFSTR). An expression for the analysis of interaction of the two-variable CFSTR was theoretically derived by using the relative gain method (RGM). For the purpose of improving the stability of the decoupling control system, undercompensation for a decoupled CFSTR system was suggested and the robustness test of such undercompensation decoupler to the modelling error was studied. On the other hand, the proposed time-delay compensation method, unlike conventional Smith's scheme, can rely on the basic property of gain-invariant time-delay. The stability of this time-delay compensation method is not affected by the CFSTR control system time-variant time-delay, while its compensation structure has the same features as the Smith compensator. The design of a decoupler and that of a time-delay compensator are independent of each other. All compensation structures are physically realizable. The theoretical results are supported by simulation. Simulation results for a CFSTR demonstrate that the undercompensation decoupling control can tolerate a relatively wide modelling error and reduce the sensitivity of the CFSTR process to parameter variations and unwanted disturbances. Also, simulation results show that the proposed time-delay compensator can provide an improvement over the conventional Smith compensator. / Applied Science, Faculty of / Chemical and Biological Engineering, Department of / Graduate

Abstract: Model-based control and optimization is the predominant paradigm in process systems engineering. The performance of model-based methods, however, rely heavily on the accuracy of the process model, which declines over the operation cycle due to various causes, such as catalyst deactivation, equipment aging, feedstock variability, and others. This work aims to tackle this challenge by considering two alternative approaches. The first approach replaces existing control and optimization methods with model-free reinforcement learning (RL). We apply a state-of-the-art reinforcement learning algorithm to a network of reactions, evaluate the performance of the RL controller in terms of setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, and optimize the reward function to achieve the desired control and optimization performance. The second approach presents a novel framework for integrating Economic Model Predictive Control (EMPC) and RL for online model parameters estimation. In this framework, EMPC optimally operates the closed-loop system while maintaining closed-loop stability and recursive feasibility. At the same time, the RL agent continuously compares the measured state of the process with the model’s predictions, and modifies the model parameters accordingly to optimize the process. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance.

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Master of Science