Self-optimization
of chemical reactions enables faster optimization
of reaction conditions or discovery of molecules with required target
properties. The technology of self-optimization has been expanded
to discovery of new process recipes for manufacture of complex functional
products. A new machine-learning algorithm, specifically designed
for multiobjective target optimization with an explicit aim to minimize
the number of “expensive” experiments, guides the discovery
process. This “black-box” approach assumes no a priori
knowledge of chemical system and hence particularly suited to rapid
development of processes to manufacture specialist low-volume, high-value
products. The approach was demonstrated in discovery of process recipes
for a semibatch emulsion copolymerization, targeting a specific particle
size and full conversion.
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Highlights The physicochemical parameters required for mathematical modeling were either found in literature or estimated on the basis of experimental data. Thus, the applicability of the developed model is not limited by the dataset or biosensor design. The developed cyclic voltammetry simulator was applied for interpreting the experimental results at various mediator concentrations, membrane thickness/compositions and operating conditions of the proposed multi-layer biosensor system. An accurate electrochemical, morphological and microscopic characterization of the biosensor system coupled with the model predictions allowed to identify the parameters crucial for the stable biosensor response. Based on the model predictions, a more favourable design of the biosensor system was developed, which subsequently reduced the reagent usage and waste generation.
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.
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