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Abstract: This paper proposes an approach for obtaining reduced-order models of chemical processes with application to the design of plantwide control systems. The approach is based on the inherent structure that exists in a chemical plant and identifies units or groups of units which determine the steady-state and dynamic behaviour of the plant. Specific reduction techniques with different accuracy are applied to each unit, followed by the coupling of the reduced models, according to the process flowsheet structure. By means of a case study, the assessment of plantwide control structures for the iso-butane–butene alkylation plant, the effectiveness of the approach is proven. Moreover, it is shown that the approach is able to retain the nonlinearity of the original plant model and preserves the significance of important model variables. [Copyright &y& Elsevier]

This paper presents two nonlinear model predictive control based methods for solving closed-loop stochastic dynamic optimisation problems, ensuring both robustness and feasibility with respect to state output constraints. The first one is a new deterministic approach, using the wait-and-see strategy. The key idea is to specifically anticipate violation of output hard-constraints, which are strongly affected by instantaneous disturbances, by backing off of their bounds along the moving horizon. The second method is a stochastic approach to solve nonlinear chance-constrained dynamic optimisation problems under uncertainties. The key aspect is the explicit consideration of the stochastic properties of both exogenous and endogenous uncertainties in the problem formulation (here-and-now strategy). The approach considers a nonlinear relation between uncertain inputs and the constrained state outputs. The performance of the proposed methodologies is assessed via an application to a semi-batch reactor under safety constraints, involving strongly exothermic reactions. [ABSTRACT FROM AUTHOR]