This paper provides a systematic approach for the design of buffer tanks. We consider mainly the case where the objective of the buffer tank is to dampen ("average out") the fast (i.e., high-frequency) disturbances, which cannot be handled by the feedback control system. We consider separately design procedures for (I) mixing tanks to dampen quality disturbances and (II) surge tanks with averaging level control to handle flow-rate disturbances.¢ also affiliated with Norsk

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Conceptually, a multivariable controller uses the two basic principles of "Feedforward" action, based mainly on the model (for example the off-diagonal decoupling elements of the controllers), and feedback correction, based mainly on the measurements. The basic differences between feedback and feedforward control are well-known, and these differences also manifest themselves in the multivariable controller.Feedforward control may improve the performace significantly, but is sensitive to uncertainty, especially at low frequencies. Feedback control is very effective at lower frequencies where high feedback gains are allowed.In this paper we aim at obtaining insight into how a multivariable feedback controller works, with special attention to serial processes. Serial processes are important in the process industry, and the structure of this process makes it simple to classify the different elements of the multivariable controller.An example of neutralization of an acid in a series of three tanks is used to illustrate some of the ideas.

In this paper, a laboratory experiment has been used to investigate some aspects related to integral action in model predictive control (MPC). Simulations using the same model as that used for control design may indicate that integral action is present and that disturbances are handled well with no steady-state offset, but in practice, unmodeled phenomena may give a poor response, including a steady-state offset. The reason is that the controller may not contain feedback with integral action, although the zero offset seems to indicate it. The experiments on a two-tank process verify that output feedback with input-disturbance estimation is efficient, provided that the disturbances to estimate are correctly chosen.

In this paper we study the effect of model errors on the performance of feedforward controllers. In accordance with the sensitivity function for feedback control, we define the feedforward sensitivities, © (feedforward from disturbance) and © (feedforward from set-point), as measures for the reduction in the output error obtained by the feedforward control. For "ideal" feedforward controllers based on the inverted nominal model, the feedforward sensitivities equal the relative model errors, which must thus remain less than for feedforward control to have positive (dampening) effect.For some common model error classes we provide rules for when the feedforward controller is effective, and we also design -optimal feedforward controllers.