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.

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.

Raman spectroscopy is used to determine the concentrations of monomers and polymers in the second stage of a twostage emulsion polymerization process. The indirect hard modeling (IHM) approach is applied to monitor inline the reaction in semi-batch operation in lab scale and pilot scale. This method requires the spectra of all pure components. While the pure spectra of water and monomers are easily measured as pure components, the spectra of two different copolymers are constructed from reaction spectral data containing mixtures. Good predictive capabilities of the developed model are demonstrated.

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