For the optimization of dynamic systems, it is customary to use measurements to combat the effect of uncertainty. In this context, an approach that consists of tracking the necessary conditions of optimality is gaining in popularity. The approach relies strongly on the ability to formulate an appropriate solution model, i.e. an approximate parameterization of the optimal inputs with a precise l...

Iterative learning control (ILC) is a technique to realize system inversion in a run-to-run manner. Though most of the techniques presented in the literature consider zero tracking error between the desired and achieved outputs, perfect inversion is often not feasible and in many cases not even desirable. Approximate inversion with good convergence and robustness properties (at the cost of a no...

This paper presents the development of a simple model which describes the product quality and production over time of an experimental batch distillation column, including start-up. The model structure is based on a simple physical framework, which is augmented with fuzzy logic. This provides a way to use prior knowledge about the dynamics, which have a general validity, while additional informa...

E nergy costs are the largest percentage of a hydrocarbon plant’s operating expenditures. This is especially true of the distillation process, which requires substantial energy consumption. Concerns over recent high costs and economic pressures continually emphasise the need for efficient distillation design and operation without a loss of performance. This article illustrates how energy-effici...

This article introduces a framework for including different uncertainties at the chemical plant design stage. Through an integrated robust optimization approach and problem formulation, equipment, operating, control, and quality costs are simultaneously taken into account, leading to system, parameter, and tolerance design. Rather than using single pointwise solutions in the decision space, ope...