Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes

Abstract

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.

Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform

“…17 Considering 14 input variables, Houben et al demonstrated the multitarget optimization of a target particle size and full conversion for a emulsion copolymerization process. 18 These and many additional examples are wellreviewed elsewhere. 13,19,20 Model-based optimization strategies with continuously operating microfluidic reaction systems allow the efficient scale-up of reactions because the intrinsic kinetic parameters can be determined in the absence of heat and mass transfer limitations.…”

Automatic discovery and optimization of chemical processes

“…MOAL algorithm was recently applied as a decision making element of a closed-loop system in a process of experimental discovery of recipes for semi-batch emulsion co-polymerization [63]. For this the algorithm was extended to incorporate a Gaussian Process (GP) binary classification model [64].…”

An autonomous microreactor platform for the rapid identification of kinetic models

“…There are a large number of black-box closed loop systems reported in the literature, and they are often called "self-optimising reactors". [12][13][14]17,25,30,32,35,[45][46][47][48][49][50] Some of the most impressive applications of self-optimising reactors include the automation of a multistep sequence including reaction and separation steps 23 and the use of multiple objective functions where an optimal compromise between two competing variables (such as profitability and environmental impact) has to be identified. 51 However, while the self-optimising reactors offer many advantages, they are normally of little value in developing a kinetic model, as the experiments are usually performed only in the region around the optimum operating point.…”