The transfer of a semi‐batch emulsion copolymerization to a single feed continuous recipe is investigated herein. A single feed continuous tubular reactor is used in the presented study in accordance with the requirement of minimizing the number of dosing streams. The correlation between operating parameters and the product properties are experimentally presented for the semi‐batch as well as for the single feed continuous tubular reactor. It is shown that the transfer from semi‐batch to continuous smart‐scale tubular reactor needs to be direct as opposed to via a batch reactor. The particle sizes and molecular masses obtained in the tubular reactor are similar to those of the semi‐batch process. However, the adjustment of the polymer composition is challenging. Using monomer addition as a means to influence chemical composition is not applicable. Thus, in the tubular reactor the chemical composition of the resulting copolymer depends on the initial monomer composition and reaction temperature. Nevertheless, the mass transfer of the emulsion polymerization damps the copolymer composition distribution (CCD) drift for higher initial monomer contents compared to bulk polymerization. It is shown that transfer of a semi‐batch product to a continuous recipe is possible in early stages of process development.

This work aims at the development of computationally efficient schemes under the discrete time multivariable globally linearized control (GLC) framework. Unconstrained and constrained GLC schemes are developed using a discrete quadratic perturbation model. The structure of QPM facilitates analytical treatment of the unconstrained controller synthesis problem for square multi-input multi-output (MIMO) nonlinear processes and makes it possible to develop closed form nonlinear control law. For non-square MIMO systems and for handling input constraints, an optimization based discrete GLC formulation is developed. The effectiveness of the proposed GLC formulations is demonstrated by conducting simulation studies on a CSTR system and the benchmark Tennessee Eastman (TE) control problem.

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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.

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