Due to hardening competition and increased focus on resource efficiency, efforts are made to develop advanced industrial optimization and control systems with the goal to shift the (semi‐)batch production from recipe‐based to a state‐based approach. This study illustrates the steps needed for the implementation of optimization and on‐line control of semibatch emulsion copolymerization involving the development of process model, its validation and connection with control software, and the realization at pilot plant scale. The process model must be fast and robust enough to provide estimation of the process trajectory reliably and quickly. Moreover, in connection with nonlinear model predictive control (NMPC), the model has to be able to learn from the process and to update parameter values in real time, e.g., due to change of reactor jacket heat transfer. The Cybernetica CENIT software is employed for NMPC. The industrial pilot‐scale semibatch emulsion copolymerization of four comonomers (two of them water soluble) is used for the demonstration of NMPC functionality for: (i) reactor temperature control, (ii) minimization of batch time while preserving product quality, and (iii) minimization of batch duration with desired simultaneous shift in product quality.

The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools.The case-study first looks into the use of NMPC for "conventional" pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the 'slugging' phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed.

An event‐driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot‐plant reactors are presented.

Nonlinear model predictive control applications have been deployed on two large pilot plants for post combustion CO2 capture. The control objective is formulated in such a way that the CO2 capture ratio is controlled at a desired value, while the reboiler duty is formulated as an unreachable maximum constraint. With a correct tuning, it is demonstrated that the controllers automatically compensate for disturbances in flue gas rates and compositions to obtain the desired capture ratio while the reboiler duty is minimized. The applications are able to minimize the transient periods between two different capture rates with the use of minimum reboiler duty.

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