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Numerous methodologies have been developed for the plant-wide control (PWC) of industrial processes. These methodologies can be classified into heuristics, optimization, mathematical, and mixed approaches. However, most of these methodologies do not involve the extensive usage of rigorous process simulators as part of the control system development. Also, in recent years, the focus has shifted toward the use of mixed approaches, which builds synergies between different techniques. As part of the continuing search for more effective PWC system design methods, an integrated methodology is proposed in this paper. This eight-level framework incorporates heuristics and optimization, together with the use of simulation throughout the procedure. The main intuitive feature of this framework is the identification of key controlled variables for optimal operation; this is achieved by detailed analysis of disturbances, computation of the new optimal set points by reoptimization, and cost breakdown analysis for throughput changes. Another important feature is the use of dynamic performance measures to aid in analyzing the effects of integration. This framework is then applied to the styrene monomer plant case study presented earlier [Vasudevan et al. Ind. Eng. Chem. Res.2009, 48, 10941–10961] to develop a viable control structure. The performance of the resulting control system is compared with the control system developed using the integrated framework of simulation and heuristics. While both the control structures are found to be stable and robust in the face of disturbances, the control structure developed in the present work performs significantly better, in terms of steady-state profit and the dynamic economic index based on deviation from the production target. [ABSTRACT FROM AUTHOR]

The operation of complex production processes is one of the most important research and development problems in process engineering. A safety instrumented system (SIS) performs specified functions to achieve or maintain a safe state of the process when unacceptable or dangerous process conditions are detected. A logic solver is required to receive the sensor input signal(s), to make appropriate decisions based on the nature of the signal(s), and to change its outputs according to user-defined logic. The change of the logic solver output(s) results in the final element(s) taking action on the process (e.g., closing a valve) to bring it back to a safe state. Alarm management is a powerful tool to support the operators’ work to control the process in safe operating regions and to detect process malfunctions. Predictive alarm management (PAM) systems should be able not only to detect a dangerous situation early enough, but also to give advice to process operators which safety action (or safety element(s)) must be applied. The aim of this paper is to develop a novel methodology to support the operators how to make necessary adjustments in operating variables at the proper time. The essential of the proposed methodology is the simulation of the effect of safety elements over a prediction horizon. Since different manipulations have different time demand to avoid the evolution of the unsafe situation (safety time), the process operators should know which safety action(s) should be taken at a given time. For this purpose a method for model based predictive stability analysis has been worked out based on Lyapunov’s stability analysis of simulated state trajectories. The proposed algorithm can be applied to explore the stable and unstable operating regimes of a process (set of safe states), information that can be used for PAM. The developed methodology has been applied to two industrial benchmark problems related to the thermal runaway. [ABSTRACT FROM AUTHOR]