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Distillation is undoubtedly the most important unit operation in chemical engineering. During design a significant effort is normally put into steady-state optimization of the column with respect to its size, feed location and reflux ratio. However, operating the column close to this optimal point requires reasonably tight control of the product compositions. This is usually not achieved in industrial practice due to stability problems. Improved strategies for distillation control offer a viable means for significant economic savings as compared to the existing ad hoc techniques. This thesis addresses robust control of distillation columns in the face of model-plant mismatch caused by model uncertainty, nonlinearity and changes in operating conditions. The robust control paradigm, introduced by Doyle and coworkers, is used as the basis for controller design and analysis. An important tool is the Structured Singular Value (SSV) which enables the evaluation of a plant's achievable control performance. This provides a consistent basis for comparing controllers and design alternatives. Achievable performance is also related to other commonly used measures such as the RGA and the condition number.

Physical insight is used to derive low-order column models which address the issues most important for feedback control. It is shown that the dynamic behavior can be explained in terms of the fundamental difference between external and internal flows. This difference manifests itself both at steady-state and in the dynamic response. Furthermore, the initial response, which is of principal importance for feedback control, is affected much less by changes in the operating conditions than is the steady-state response. The initial response is even less markedly affected when logarithmic compositions are used.

An important issue in distillation control is which two of the possible five manipulated inputs should be selected for composition control; each configuration may yield entirely different control performance. Issues which must be addressed include model uncertainty and dynamic response as well as rejection of flow disturbances by the level loops.

Finally, a design method for robust decentralized controllers, which generalizes the SSV-interaction measure of Grosdidier and Morari, is introduced. Each loop is designed independently such that robust performance of the overall system is guaranteed.

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