Dynamic optimization of an emulsion copolymerization process for product quality using a deterministic kinetic model with embedded Monte Carlo simulations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F19%3A43919973" target="_blank" >RIV/60461373:22340/19:43919973 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0098135419305472?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0098135419305472?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compchemeng.2019.106566" target="_blank" >10.1016/j.compchemeng.2019.106566</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic optimization of an emulsion copolymerization process for product quality using a deterministic kinetic model with embedded Monte Carlo simulations

Popis výsledku v původním jazyce

We present the dynamic optimization of an emulsion copolymerization process described by a deterministic kinetic ordinary differential equation model including a stochastic Monte Carlo submodel, describing the growth of polymer chains within particles. For the considered semi-batch operation, the time dependent input trajectories for monomer and initiator flow rates are optimized along with the isothermal reactor temperature. We use the surrogate-model-based optimizer MATSuMoTo for the optimization to avoid the need to compute derivatives of the stochastic model. Radial basis functions with linear polynomial tails are selected as surrogate functions which are updated during optimization by the newly evaluated points. A relevant application problem formulation together with results for two case studies are presented. Qualitatively similar input trajectories are obtained for different optimization runs due to the stochastic process and the limited number of iterations. All solutions reduce the batch time significantly. © 2019

Název v anglickém jazyce

Dynamic optimization of an emulsion copolymerization process for product quality using a deterministic kinetic model with embedded Monte Carlo simulations

Popis výsledku anglicky

We present the dynamic optimization of an emulsion copolymerization process described by a deterministic kinetic ordinary differential equation model including a stochastic Monte Carlo submodel, describing the growth of polymer chains within particles. For the considered semi-batch operation, the time dependent input trajectories for monomer and initiator flow rates are optimized along with the isothermal reactor temperature. We use the surrogate-model-based optimizer MATSuMoTo for the optimization to avoid the need to compute derivatives of the stochastic model. Radial basis functions with linear polynomial tails are selected as surrogate functions which are updated during optimization by the newly evaluated points. A relevant application problem formulation together with results for two case studies are presented. Qualitatively similar input trajectories are obtained for different optimization runs due to the stochastic process and the limited number of iterations. All solutions reduce the batch time significantly. © 2019

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20401 - Chemical engineering (plants, products)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTERS &amp; CHEMICAL ENGINEERING

ISSN

0098-1354

e-ISSN

—

Svazek periodika

130

Číslo periodika v rámci svazku

2. listopad 2019

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

—

Kód UT WoS článku

000489333200016

EID výsledku v databázi Scopus

2-s2.0-85072187729