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This paper concerns a case study on control structure selection for an almost binary distillation column. The column is energy integrated with a heat pump to transfer heat from the condenser to the reboiler. This integrated configuration renders the possible control structure somewhat different from what is usually seen. Further the heat pump enables disturbances to propagate faster through the system. The plant has six possible actuators of which three must be used to stabilize the system. Hereby three actuators are left for product purity control and/or pressure control. A MILP screening method based on a linear state space model is used to determine an economically optimal set of controlled and also manipulable variables. The generated set of inputs and outputs are analysed with frequency dependent RGA and singular values to determine the best pairing of the variables in terms disturbance rejection and setpoint tracking. The paring and controller design are implemented and evaluated through nonlinear simulation. The suggested control structure is also compared to a control structure applied experimentally.
A conventional diagonal controller can be designed from very limited process information. In the SVD control structure, a diagonal controller is applied to orthogonal sums and differences of the inputs and outputs. In this work, the process knowledge needed for the design of an SVD controller is analyzed. In contrast to conventional decentralized controllers, the SVD controller can compensate for the process directionality of ill-conditioned processes. The dual composition control problem of distillation columns is of particular interest, since reliable models for these columns are quite hard to obtain.
Control of an experimental in-line pH process exhibiting varying nonlinearity and deadtime is described. A radial basis function (RBF) artificial neural network is used to model the nonlinear dynamics of the process. Accommodation of the varying process deadtime in the neural model is achieved by the generation of a feed-forward signal, for input to the neural network, from a downstream pH measurement. The feedforward signal is derived from a variable delay model based on process knowledge and a flow measurement. The neural model is then used to realise a predictive control scheme for the process. Development of the neural process model is described and results are presented to illustrate the performance of the neural predictive control scheme which is tested as a regulator at different setpoints.
The paper presents the design of a robust control for a real plant at laboratory scale. The process considered consists of the production of a C1H dissolution. The control system aim is to maintain the concentration at prescribed values despite variations in the plant, the presence of disturbances and the inherent nonlinearity of the process. The uncertainties in the model are studied and characterized as complex or real uncertainties. Using this information the controller was designed by optimization of the structured singular value (μ-synthesis). The obtained controller was tested in real time in the plant, fulfilling the design requirements and obtaining good performance in varying conditions.
