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Particle morphology is a key property of hybrid waterborne polymer dispersions that are used in high-end applications. However, evolving these product-by-process material properties is still mainly achieved by trial-and-error guided semi-empirical knowledge. In this work, a recently published mathematical model for the development of the particle morphology and a dynamic optimization method based on the direct single shooting approach are used to demonstrate in silico the feasibility of process optimization aimed at controlling particle morphology of waterborne polymer dispersions. This knowledge-based approach is assessed for existing and new materials with distinct particle morphologies. The batch times can be reduced by up to 13%.

Particle morphology strongly affects the performance of waterborne polymer dispersions, which are products-by-process materials produced by emulsion polymerization, a process prone to suffer run-to-run irreproducibility. Therefore, on-line control of particle morphology would be tremendously beneficial, but this is an unsolved issue because particle morphology is neither on-line measurable nor observable from available online monitoring techniques. This article explores in-silico the possibility to overcome this problem by using a mathematical model able to calculate the particle morphology from the monomer conversion and reactor temperature as a “soft sensor”. These estimates are used in combination with an economic nonlinear model predictive controller. It is shown that this controller is able to achieve a good control of the particle morphology even in the presence of serious disturbances such as the presence of a strong inhibition or the failure of the reactor cooling system. Under these circumstances, neither open-loop control nor tracking of off-line optimized reaction trajectories are able to control the particle morphology tightly. The limits of the soft sensor are investigated by considering non-detectable disturbances such as the use of a seed of unknown glass transition temperature and the benefits of a hypothetical online morphology measurement available for control is explored.

Mineral carbonation is a promising method to sequester large amounts of carbon dioxide and to produce value-added substitutes for the cement, paper, and plastic industries. In understanding carbonation reaction mechanisms in batch operation, dynamic models play a crucial role, and they allow to evaluate the effects of important process quantities such as temperature, pressure, particle size of solid phases and additives on reaction kinetics. We develop a mechanistic, dynamic forsterite carbonation model that accounts for gas, liquid, and multiple solid phases. In this model, gas-liquid and dissociation equilibria and surface-controlled reactions between solids and liquid phase are based on nonideal thermodynamics. We account for particle size distribution of raw material and product phases by formulating population balances considering nucleation and growth of particles. We model gas and liquid phases each as a homogeneous phase and we use isopotential conditions to describe equilibrium. The resulting high index system of differential and algebraic equations (DAE) is reformulated to obtain a DAE of differential index 1. Model predictions qualitatively match experimental data taken from literature and thus, we can quantify influences of key process conditions by simulation.

Microgels are functional colloidal polymer networks with diverse applications. Various applications require microgels of different sizes. The microgel size is determined during the synthesis and depends among other conditions on the reactor type, reactor temperature, monomer-to-initiator ratio, cross-linker concentration, and surfactant concentration. While experimental data covering these synthesis conditions are available in literature, a model taking into account all of the above factors has not yet been proposed. We present a mechanistic model considering all of the above conditions that agrees with experimental data from various literature sources. The effect of surfactant type and concentration is included by addition of a term in the coagulation kinetics. The reactor type and Reynolds number in the reactor is accounted for with a semi-empirical parameter describing the kinetics of particle coagulation. This parameter is fitted to the data of one of the available experiments, while all other experiments are used for validation. The model predictions quantitatively match the experimental data for stirred batch reactors. For unstirred batch reactors, the agreement with the experimental data is only qualitative. The mechanistic model enables model-based design of functional microgels for new applications.