Multi-objective dynamic optimization of seeded suspension polymerization process

Jayaweera, Chamanthi Denisha; Narayana, Mahinsasa

doi:10.1016/j.cej.2021.130797

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…A two‐step procedure must be employed for synthesizing particles with a core‐shell structure, where the core particles can be obtained by suspension polymerization and the second step monomer added afterward. It is also possible to obtain this type of morphology from seeded suspension polymerization, which uses polymer particles as seeds and adds the components of suspension polymerization to form the shell [26] …”

Section: Core‐shell Polymeric Particlesmentioning

confidence: 99%

“…It is also possible to obtain this type of morphology from seeded suspension polymerization, which uses polymer particles as seeds and adds the components of suspension polymerization to form the shell. [26] Gonçalves and collaborators (2008) synthesized core-shell particles of suitable sizes for rigid foams from seeded suspension polymerization of methyl methacrylate (MMA) using polystyrene (PS) particles as seeds. In this study, three synthetic strategies were implemented, differing in how the monomer was fed into the system.…”

Section: Suspension Polymerizationmentioning

confidence: 99%

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A Comprehensive Review on Core‐Shell Polymeric Particles for Enzyme Immobilization

et al. 2022

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The search for ideal solid support that promotes enzyme stability, easy separation and reuse cycles in different processes has grown widely. In this sense, core‐shell polymer particles draw attention because they are already widely used in adsorption, catalysis, and drug delivery, due to their remarkable advantages in surface properties adjustment, excellent mechanical stability, and high chemical resistance, as a result of the combination of the characteristics of the polymers that form the core and shell of the structure. Thus, this review article provides an overview of the synthesis processes of these particles, highlighting the characteristics that can be obtained from each synthesis technique, as well as the most recently used techniques to obtain polymeric particles with core‐shell morphology. The advantages and main challenges for the application of these structures in the immobilization of enzymes are also discussed, providing a general summary of what has been explored in the literature. Despite the potential of application, the use of these materials in the immobilization of enzymes is still little explored, with studies focused mainly on lipases and around the same classes of polymers. Therefore, there is an opportunity window regarding the investigation of the immobilization of other enzymes of commercial interest on these polymeric supports.

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Section: Core‐shell Polymeric Particlesmentioning

confidence: 99%

Section: Suspension Polymerizationmentioning

confidence: 99%

A Comprehensive Review on Core‐Shell Polymeric Particles for Enzyme Immobilization

et al. 2022

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“…The kinetic methods vary depending on the type of substance and the environmental conditions. 46,47 Some kinetic studies rely on empirical or semiempirical formulas, while others depend on the understanding of the reaction mechanism. 48 This makes it difficult to apply traditional algorithms to kinetic studies.…”

Section: Introductionmentioning

confidence: 99%

“…However, the prediction of the dynamic behavior of substances, especially polymers, using artificial intelligence is still an open challenge. − This is mainly because the kinetic studies are more “specific” than the thermodynamic studies. The kinetic methods vary depending on the type of substance and the environmental conditions. , Some kinetic studies rely on empirical or semiempirical formulas, while others depend on the understanding of the reaction mechanism . This makes it difficult to apply traditional algorithms to kinetic studies.…”

Section: Introductionmentioning

confidence: 99%

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

Wang,

Sadowski,

2024

ACS Sustainable Chem. Eng.

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With the environmental protection requirements brought about by the large-scale application of polymers in industrial fields, understanding the viscosities of polymers is becoming increasingly important. The different arrangements and crystallinity of the polymers make their viscosities difficult to calculate. To address this challenge, new strategies based on artificial intelligence algorithms are proposed. First, the strategy trains three artificial intelligence algorithms [extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP)] based on molecular descriptors of the polymer molecular properties. Next, the PC-SAFT parameters are input into the XGBoost and CNN algorithms as molecular descriptors representing the thermodynamic properties of the polymer to improve the accuracy of the algorithm prediction results. Subsequently, the Molecular ACCess Systems chemical fingerprinting was combined with the XGboost algorithm and CNN algorithm to further improve the accuracy of predicting viscosities. The XGboost algorithm was identified as the best predictive algorithm for predicting the viscosities of the polymer in different states. This discovery is expected to provide effective information for screening polymers for applications in medicine and the chemical industry.

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“…The uncertainty of the parameters on suspension performance was analyzed using polynomial chaos expansion theory. Then, the statistical properties of QZSAS performance were taken as the objective function of optimization, which is a typical Pareto optimization problem [24,25]. Genetic algorithm, which is widely used for passive and semi-active suspension optimization [26][27][28], is adopted to perform a multi-objective optimization of QZSAS with considering parameter uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis and Optimization of Quasi-Zero Stiffness Air Suspension Based on Polynomial Chaos Method

Liu

Jiang

et al. 2022

Chin. J. Mech. Eng.

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To improve the vibration isolation performance of suspensions, various new structural forms of suspensions have been proposed. However, there is uncertainty in these new structure suspensions, so the deterministic research cannot reflect the performance of the suspension under actual operating conditions. In this paper, a quasi-zero stiffness isolator is used in automotive suspensions to form a new suspension−quasi-zero stiffness air suspension (QZSAS). Due to the strong nonlinearity and structural complexity of quasi-zero stiffness suspensions, changes in structural parameters may cause dramatic changes in suspension performance, so it is of practical importance to study the effect of structural parameter uncertainty on the suspension performance. In order to solve this problem, three suspension structural parameters d0, L0 and Pc0 are selected as random variables, and the polynomial chaos expansion (PCE) theory is used to solve the suspension performance parameters. The sensitivity of the performance parameters to different structural parameters was discussed and analyzed in the frequency domain. Furthermore, a multi-objective optimization of the structural parameters d0, L0 and Pc0 of QZSAS was performed with the mean and variance of the root-mean-square (RMS) acceleration values as the optimization objectives. The optimization results show that there is an improvement of about 8%−10% in the mean value and about 40%−55% in the standard deviation of acceleration (RMS) values. This paper verifies the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems, which provide a reference for the future structural design and optimization of such suspension systems.

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Multi-objective dynamic optimization of seeded suspension polymerization process

Cited by 10 publications

References 28 publications

A Comprehensive Review on Core‐Shell Polymeric Particles for Enzyme Immobilization

A Comprehensive Review on Core‐Shell Polymeric Particles for Enzyme Immobilization

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

Uncertainty Analysis and Optimization of Quasi-Zero Stiffness Air Suspension Based on Polynomial Chaos Method

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