Simultaneous Process Design and Control of the Williams–Otto Reactor Using Infinite Horizon Model Predictive Control

Abstract

In this work, we study a simultaneous process design and control methodology using infinite horizon model predictive control (IHMPC). The methodology is based on the change in market conditions caused by the difference in the raw material and product prices. The economic and dynamic objectives are integrated into a single structure in the formulation of the optimization problem. The presented solution divides the problem into three stages and the final problem is solved using two strategies: goal attainment and quadratic cost. In the first two stages, utopian designs are calculated by the isolated solution of economic and dynamic problems. Finally, an integrated problem is solved to achieve the utopian values calculated in the previous stages, in order to produce simultaneously the plant with the best economic and dynamic performance. The results demonstrate the advantages of the methodology for process design, compared with the traditional sequential methodology.

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

“…The solutions of the subset of problems are then reconciliated by an iterative procedure. In the literature, it is possible to find many studies addressing the integration of different layers of enterprise management, such as the integration of planning, scheduling, and control , and the integration of design and control. − …”

State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

“…Understanding and predicting this behavior is essential for ensuring that the process operates safely, efficiently, and within the desired operating range [40,41]. To address these process behaviors, petrochemical processes use a variety of control techniques, such as feedback control, feedforward control, and advanced control algorithms, to keep the process variables at desired levels that fall within acceptable ranges [42][43][44]. Fig.…”

Robust multi‐objective model predictive control for constrained nonlinear systems with disturbances

“…After solving the robust utopia-tracking optimal control problem (20), the optimal input sequence u * (k) ∶= {u * (0|k), u * (1|k), … , u * (N − 1|k)} can be obtained. On the basis of the receding horizon control principle, the robust MoMPC law here is defined as…”

“…In that work, the dynamic utopia-tracking MoMPC framework was established, and the feasibility of the controller was analysed. Lately, the ideal of utopia-tracking MoMPC was also introduced into the field of integrating process design and control for complex non-linear systems [20,21]. Furthermore, from the application perspective, in recent years, the utopia-tracking MoMPC has been widely used in various fields, such as microbial manufacturing processes [2], intelligent transportation systems [22], wastewater treatment processes [23], wet flue gas desulphurization processes [24], and the assessment of rural distributed energy systems [25].…”