Quantifying the effects of process design on controllability and operability

The plant design and control tasks are usually carried out sequentially. Initially the plant is designed and then the control structure is determined as a function of the design. Such a formulation neglects the coupling between design and control. This may lead to processes where a suitable control scheme cannot be synthesized for a given design. Even if a control scheme can be generated, it has been shown that it is, in general, sub-optimal [1]. This has further implications since with increasing competition and rising costs, plants are expected to operate at different operating conditions to make different grades of product. These factors motivate the study of the coupling between design and control for chemical plants. Comprehensive studies on the coupling of design and control has been minimally addressed. Generally, some controllability measures such as the relative gain array, minimum singular value, etc. have been used to include control analysis in the design exercise [2]. Some research has focused on solving a design and control optimization problem simultaneously [3, 4, 5] but issues such as feasibility of a solution and computing burden have not been treated rigorously. Other work includes the decision-based methodology of Vasbinder et al. [6] for plantwide control synthesis, wherein a particular decomposition of the design flowsheet is done that prioritizes among the competing objectives of the controlled variables and design constraints. In this work, a novel bi-level optimization strategy first proposed by [1] that takes into consideration the coupling between design and control is investigated. The bi-level strategy is compared with the usual sequential and sequential iterative strategy. The effect of tighter operational constraints and uncertainty on the coupling term derived by [1] is discussed. The optimization strategy may be mathematically represented as follows [1]: