An automated recipe generator for semi-batch solution radical copolymerization via comprehensive stochastic modeling and derivative-free algorithms

Nasresfahani, Amin; Schiavi, David; Grady, Michael C.; Hutchinson, Robin A.

doi:10.1016/j.cej.2020.127920

Cited by 12 publications

(19 citation statements)

References 67 publications

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“…In particular, Lemos and Pinto [ 158 ] have coupled k MC modeling with residence time distributions to model continuous stirred tank reactors in a hybrid analytical/stochastic manner. In the series of articles by Hutchinson et al, solution k MC models have been expanded toward semi-batch reactors with instantaneous addition of the reactants at predetermined times [ 159 , 160 , 161 , 162 , 163 , 164 ]. This method is efficient for non-starved feed conditions and for abundant chemical components.…”

Section: Connection Of Lower-scale Modeling With Higher-scale Modelingmentioning

confidence: 99%

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

et al. 2021

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In recent decades, quantum chemical calculations (QCC) have increased in accuracy, not only providing the ranking of chemical reactivities and energy barriers (e.g., for optimal selectivities) but also delivering more reliable equilibrium and (intrinsic/chemical) rate coefficients. This increased reliability of kinetic parameters is relevant to support the predictive character of kinetic modeling studies that are addressing actual concentration changes during chemical processes, taking into account competitive reactions and mixing heterogeneities. In the present contribution, guidelines are formulated on how to bridge the fields of computational chemistry and chemical kinetics. It is explained how condensed phase systems can be described based on conventional gas phase computational chemistry calculations. Case studies are included on polymerization kinetics, considering free and controlled radical polymerization, ionic polymerization, and polymer degradation. It is also illustrated how QCC can be directly linked to material properties.

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Section: Connection Of Lower-scale Modeling With Higher-scale Modelingmentioning

confidence: 99%

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

et al. 2021

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“…[ 9 ] Conversions of >90% and very clean chain extensions ( Ð of 1.1–1.2) were achieved, with reaction rates sufficiently high under room temperature operation such that “starved‐feed” operation could be maintained during the 3 h feed period; i.e., the polymer production rate is controlled by the rate at which monomer is fed to the reactor, the method commonly used to produce polymer resins for coatings applications. [ 10,11 ] These results were obtained without the addition of supplemental copper during chain extension through the judicious use of additional ligand as well as ascorbic acid (ASC) as reducing agent, [ 12,13 ] thus lowering copper concentration in the polymer product to levels below 100 ppm. [ 4,13,14 ]…”

Section: Introductionmentioning

confidence: 99%

Toward an Efficient Process for the Cu(0)‐Mediated Synthesis and Chain Extension of Poly(methyl acrylate) Macroinitiator Using PMDETA as Ligand

Cooze

Barr

Hutchinson

2021

Macro Chemistry & Physics

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Me6TREN (tris[2‐(dimethylamino)ethyl]amine) is commonly used in Cu(0)‐mediated polymerization of acrylates due to its ability to promote high reaction rates while maintaining excellent control of chain growth. However, its cost presents a significant barrier to commercialization. Thus, the use of PMDETA (N,N,Nʹ,Nʹʹ,Nʹʹ‐pentamethyldiethylenetriamine), a ligand that is significantly less expensive than Me6TREN but is known to reduce the polymerization rate as well as control, is explored. The continuous production of low molecular weight (MW) poly(methyl acrylate) using PMDETA ligand in a copper tubular reactor is demonstrated, with conversions >70% achieved with a residence time of 32 min at 70 °C. The resulting macroinitiator solution can be stored and chain extended with methyl acrylate (MA) to high conversion in a semibatch reactor using either PMDETA or Me6TREN as additional ligand, increasing the versatility of a newly developed process to efficiently produce block copolymers using either ligand, or a combination of the two. First results suggest that the two‐step process can also be used to chain extend the macroinitiator with methacrylates, thus extending the range of materials that can be produced.

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“…In addition, Nasresfahani et al. [ 48 ] developed an automated k MC‐based recipe generator based on derivative‐free optimization algorithms to optimize the production of acrylic‐based copolymers. Seyedi et al.…”

Section: Introductionmentioning

confidence: 99%

Exploiting (Multicomponent) Semibatch and Jacket Temperature Procedures to Safely Tune Molecular Properties for Solution Free Radical Polymerization of n‐Butyl Acrylate

Edeleva

Marien

D’hooge

et al. 2021

Macro Theory & Simulations

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Free radical polymerization (FRP) of acrylates is under conventional lab‐scale batch conditions characterized by strong nonisothermicity. Hence, side reactions with a high activation energy such as β‐scission are highly relevant already at intermediate temperatures (313–333 K), broadening the log‐molar mass distribution and decreasing the average molar masses. To avoid thermal runaway, one can design jacket temperature profiles or exploit semibatch reactor operations. Model‐based design with stochastic solvers is a strong tool to support the identification of optimal reactor settings although rarely applied upon mutually addressing reactor temperature changes and concentration variations due to (multicomponent) feeding strategies. Here such coupled design is performed for lab‐scale solution FRP of n‐butyl acrylate, benefiting from i) many inputted kinetic parameters (e.g., Arrhenius parameters) that have been benchmarked based on individual experimental techniques, ii) previous kinetic Monte Carlo validation under batch nonisothermal conditions including a prediction of reactor temperature extrema; and iii) systematic screening of semibatch operations in combination with flat and stepwise (average) jacket temperature profiles. It is demonstrated that many molecular properties are safely within hand for different total batch times, taking 2,2′‐azobis(2‐methylpropionitrile) as initiator. The model for viscosity control is also employed, expanding the traditional output of FRP simulations.

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An automated recipe generator for semi-batch solution radical copolymerization via comprehensive stochastic modeling and derivative-free algorithms

Cited by 12 publications

References 67 publications

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

Toward an Efficient Process for the Cu(0)‐Mediated Synthesis and Chain Extension of Poly(methyl acrylate) Macroinitiator Using PMDETA as Ligand

Exploiting (Multicomponent) Semibatch and Jacket Temperature Procedures to Safely Tune Molecular Properties for Solution Free Radical Polymerization of n‐Butyl Acrylate

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