Free Radical Polymerization Of Styrene Research Articles

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization.

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Stochastic Modelling and Simulation of Free Radical Polymerization of Styrene in Microchannels using a Hybrid Gillespie Algorithm

Most recently, the production of polystyrene by Free Radical Polymerization (FRP) via microchannels has been a subject of core interest due to the efficiency of a micro-or milli-reactor brings. In addition, especially in pilot experimentations, a micro or milli-reactor has been known widely to be efficient in monitoring the microstructural end-use features or properties of the polymer as the chain propagates and ultimately terminates. However, the limitations posed by using micro or milli-reactors in process intensification such as clogging of pores can be a bottleneck when tracking the common phenomena associated with FRP such as cage, gel, and glass effects. In this work, the simulation of the synthesis of polystyrene in FRP via microchannels is computed using a robust and time-efficient hybrid Gillespie Algorithm (GA) or Hybrid Stochastic Simulation Algorithm (HSSA). The obtained results of the end-use properties of polystyrene such as Monomer conversion, Polydispersity Index, Number-Average Molar Mass and Weight Average Molar Mass were compared to experimental data. The simulation results agree well with the experimental results reported in this work. Hence, stochastic simulations prove to be an effective tool in making decisions in the context of process intensification of chain growth polymerization reactions even at a large scale.

Correntropy based Elman neural network for dynamic data reconciliation with gross errors

BackgroundMeasurement information in chemical processes is inevitably corrupted. Dynamic data reconciliation is an effective method to improve the quality of noisy measurement data. However, except for random errors, possibly gross errors are usually present in the measurement data, which may result in the deterioration and even failure of process control and optimization. MethodsConsidering the problem that dynamic mathematical models of some complex and unknown processes are difficult or impossible to be obtained, this paper combines the neural network with the correntropy estimator to design a robust dynamic data reconciliation scheme for unknown dynamic systems corrupted by both random and gross errors. The correntropy estimator is taken as a new objective function to form the robust Elman neural network (ENN). Furthermore, the processes of weight learning and parameter optimization are designed and described in detail based on the correntropy estimator. FindingsThe performance of the proposed robust ENN is demonstrated through its application on the free radical polymerization of styrene. With mixed gross errors, the mean squared error decreases more than 14-fold and falls below one ten-thousandth. Comparisons with other approaches show that the proposed method can effectively suppress the influence of gross errors and greatly improve the dynamic behavior of systems.

Unscented Kalman Filter-Based Robust State and Parameter Estimation for Free Radical Polymerization of Styrene with Variable Parameters.

The free radical polymerization of styrene (FRPS) is a complex process system with uncertain parameters in its mechanistic model. When the reaction conditions are switched, or the reaction process generates faults, the parameters will change. Therefore, state and parameter estimation (SPE) becomes an important part of the process monitoring and process control for free radical polymerization of styrene. The unscented Kalman filter (UKF) is widely used for nonlinear process systems, but it rarely considers the problem of model parameter uncertainty. UKF can be used for SPE, called UKF-based SPE (UKF-SPE), where the parameters are usually estimated simultaneously as an extension of the state space. However, when the parameters change with system switching, the traditional UKF-SPE cannot detect and track the parameter changes in time, and inaccurate parameters generate modeling errors. To deal with the problem, a UKF-based robust SPE method (UKF-RSPE) for the free radical polymerization of styrene with variable parameters is proposed, introducing a parameter testing criterion based on hypothesis testing and moving windows to directly detect whether the parameters have changed. Based on the detection results, a gradient descent method with adaptive learning rate is used to iteratively update the parameters to speed up the tracking of the parameters and to obtain more accurate parameters and states. Finally, the proposed UKF-based robust SPE is applied to free radical polymerization of styrene in a jacketed continuous stirred tank reactor. The experimental results verify the effectiveness and robustness of the method, which can track the parameters faster and obtain more accurate states.

Gel-Permeation Chromatography as a Tool for the Real-Time-Estimation of the Chain length Distribution in a Polymerization Reactor

Abstract The knowledge of the chain length distribution (CLD) during the operation in a polymerization reactor is necessary in order to control polymer quality. Model-based measurement methods (Kalman filter) provide the possibility of reconstructing on-line not directly accessible process states from easily available measurements. However, the presence of model inaccuracies or disturbances influencing primarily the CLD demand additional measurement information about CLD. An extended Kalman filter composed of two decoupled filters and considering Gel-Permeation chromatographic, (GPC), measurements for the continuous estimation of the CLD in the case of free radical polymerization of styrene is discussed. Although the existence of a time delay, due to the GPC analysis, the consideration of this measurement in the filter algorithm produces the local observability of the states characterizing the CLD, allows continuous access to the CLD and its characteristic values and provides a periodical adaption of the CLD to the reality.

Synthesis, characterization, and performance evaluation of novel terpolymers as pour point depressors and paraffin inhibitors for Egyptian waxy crude oil

Recently, researchers have shown an increased interest in wax precipitation prevention techniques. Herein, novel reactive terpolymer was synthesized by free radical polymerization of styrene, dodecyl acrylate, and maleic anhydride monomers. After that, esterification and imidation modifications for the synthesized terpolymer were performed. The pristine and modified terpolymers various characteristics were investigated by FTIR, 1H-NMR, GPC, and TGA techniques. The performance of the synthesized modified terpolymers as pour point depressors and paraffin inhibitors for the Egyptian waxy crude oil were evaluated. Additionally, the influence of their addition on the wax crystals modification was examined by polarizing optical microscopy. The results show that all the synthesized additives exhibited an efficient depressive effect on the waxy crude oil (i.e., low pour point depressant and high paraffin inhibition). Styrene dodecyl acrylate maleic anhydride imide (IDA) exhibited the best performance at optimum dosage of 200 ppm as it depresses the pour point by 17 °C from the blank and achieved a paraffin inhibition of 95%. So, the synthesized terpolymers can be considered as promising and applicable additives for depressing the pour point and inhibiting the paraffinic precipitates for the waxy crude oils.

Model development and control of an auto-refrigerated polystyrene polymerization reactor

Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

Neural network‐based hybrid models developed for free radical polymerization of styrene

AbstractIn the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.

Initiator Feeding Policies in Semi-Batch Free Radical Polymerization: A Monte Carlo Study

A Monte Carlo simulation algorithm is developed to visualize the impact of various initiator feeding policies on the kinetics of free radical polymerization. Three cases are studied: (1) general free radical polymerization using typical rate constants; (2) diffusion-controlled styrene free radical polymerization in a relatively small amount of solvent; and (3) methyl methacrylate free radical polymerization in solution. The number- and weight-average chain lengths, molecular weight distribution (MWD), and polymerization time were computed for each initiator feeding policy. The results show that a higher number of initiator shots throughout polymerization at a fixed amount of initiator significantly increases average molecular weight and broadens MWD. Similar results are also observed when most of the initiator is added at higher conversions. It is demonstrated that one can double the molecular weight of polystyrene and increase its dispersity by 50% through a four-shot instead of a single shot feeding policy. Similar behavior occurs in the case of methyl methacrylate, while the total time drops by about 5%. In addition, policies injecting initiator at high monomer conversions result in a higher unreacted initiator content in the final product. Lastly, simulation conversion-time profiles are in agreement with benchmark literature information for methyl methacrylate, which essentially validates the highly effective and flexible Monte Carlo algorithm developed in this work.

Does bimolecular termination dominate in benzoyl peroxide initiated styrene free-radical polymerization?

It is believed that polymer chains mainly terminate through bimolecular termination in free-radical polymerization, however, there is few convincing evidence to support it. In benzoyl peroxide (BPO) initiated styrene free-radical polymerization, our studies demonstrate that polymer chain terminates dominantly through primary radical termination rather than by bimolecular termination as described in polymer textbooks. Even at very high primary radical concentration or at very low monomer concentration, primary radical termination still dominates (FPRT&CTI>70%) besides the occurrence of some bimolecular coupling terminations. Two kinds of model polymers were prepared, one for bimolecular coupling termination, and the other for primary radical termination. The 1H-, 13C-, DEPT135-, and 2D HSQC NMR spectra of the polystyrene initiated by BPO are almost the same as those of the model polystyrene for primary radical termination, but they are quite different from those of the model polystyrene for bimolecular coupling termination, demonstrating directly that primary radical termination rather than bimolecular coupling termination dominates in BPO-initiated styrene free-radical polymerization.

Synthesis of polystyrene living nanoparticles (LNPs) in water via nano-confined free radical polymerization

Nano-confined free radical polymerization of styrene within the hydrophobic interior of a unimolecular micelle in water was developed to produce living nanoparticles (LNPs) that contain living mono-disperse polystyrenes for further chain extension.

Robust extended Kalman filter based state estimation for nonlinear dynamic processes with measurements corrupted by gross errors

Abstract The quality of on-line measurement data usually cannot meet the demands of practical process monitoring and control due to the influence of measurement noise. Although extended Kalman filter (EKF) is able to improve the quality by reconciling the measurement data to fit the dynamic process model describing nonlinear and Gaussian processes, it rarely considers the presence of different types of gross errors, such as outliers, bias and drift. However, the three types of gross errors often simultaneously appear in dynamic process systems. We propose a robust EKF combined with measurement compensation (MC-REKF) approach, in which a statistical test method is used to detect the abnormal measurement data, where gross error identification is accomplished within a moving window. Subsequently, the magnitudes of gross errors are estimated and used for measurement compensation. Finally, the compensated measurements are updated to re-estimate the accurate states via EKF. The effectiveness of the proposed MC-REKF is demonstrated through a complex nonlinear dynamic chemical process system, namely the free radical polymerization of styrene. With three different types of gross errors, the mean squared error (MSE) of reconciled measurements based on the MC-REKF decreases 37 fold compared to EKF. The magnitude of the residuals between the estimated states and the true states falls below 1.0E−6 when using the MC-REKF. The implementation results imply that the proposed MC-REKF can identify and estimate different types of gross errors and finally decreases their influence on state estimation and measurement reconciliation.

Autoxidized Oleic Acid Bifunctional Macro Peroxide Initiators for Free Radical and Condensation Polymerization. Synthesis and Characterization of Multiblock Copolymers

Autoxidation of unsaturated fatty acids gives fatty acid macroperoxide initiators containing two functionalities which can lead to free radical and condensation polymerizations in a single pot. The oleic acid macroperoxide initiator obtained by ecofriendly autoxidation (Pole4m) was used in both the free radical polymerization of styrene and the condensation polymerization with amine-terminated polyethylene glycol (PEGNH2) to obtain triblock branched graft copolymers. The narrow molar masses of the poly oleic acid-g-styrene (PoleS) and poly oleic acid-g-styrene-g-PEG (PoSG) graft copolymers were successfully obtained. The inclusion of oleic acid decreased the glass transition temperature of the polystyrene segment because of the plasticizing effect of oleic acid. In addition, a mechanical property of the copolymer was improved when compared with the pure PS. Structural characterization, morphology of the fracture surface, micelle formation, thermal analysis and molar masses of the obtained products were also evaluated.

The reactive compatibilized effect of SMA for immiscible APA6/PS blends via in-Situ polymerization

Styrene maleic anhydride (SMA)-g-APA6 copolymer was firstly synthesized by free radical polymerization of styrene and followed by anionic polymerization of ε-caprolactam. The polyamide 6 (APA6)/polystyrene (PS) blends were successfully prepared via anion polymerization. Among in, SMA was acted as both compatibilizer and macromolecular activator. A series of APA6/PS blends with different SMA addition amounts were synthesized to study the effect of SMA on crystallinity, morphology, water resistance, thermal stability and mechanical properties using X-ray diffraction analysis, differential scanning calorimetry, scanning electron microscopy analysis, contact angle measurement, water absorption measurement, molau test, thermogravimetric analysis, stretching and impact test. Correlational analysis proved that the obtained SMA-g-APA6 copolymer has good dispersion with PS. In addition, APA6/PS blends possess good mechanical properties, thermostability and hydrophobic property.

Effects of the molecular weight and CC functionality of poly 1‐hexene on the properties of poly 1‐hexene‐based high impact polystyrene

ABSTRACTHere we are aimed to unravel the effects of CC functionality and molecular weight of the rubber on the final properties of poly1‐hexene‐based high impact polystyrenes (HIPS). In this regard, various HIPS samples were synthesized by free radical polymerization of styrene in the presence of different weight fractions of various poly1‐hexene‐based impact modifiers including: (i) high molecular weight poly1‐hexene (PHex), (ii) low molecular weight poly1‐hexene (Olig), and (iii) 1‐hexene/1,5‐hexadiene copolymer (Copolym). Results showed that by increasing CC functionality from PHex to Oligm and Copoly, the degree of grafting increases which has its influence on the mechanical, thermal and morphological perspectives of the synthesized HIPSs. Besides CC unsaturation degree, the effect of rubber molecular weight on the final HIPS properties was studied as well. According to the results, molecular weight has significant effect on the final HIPS performance, too. Finally, our obtained results suggest new HIPS/Copolym sample as the one with the highest mechanical and thermal properties which is comparable well with commercial HIPS/polybutadiene grades. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47169.

Simulation of Potentially Possible Reactions at the Initial Stages of Free-Radical Polymerization of Styrene and Methyl Methacrylate in the Presence of Fullerene C60

The quantum-chemical simulation of possible reactions occurring at the initial stage of the free-radical polymerizations of styrene and methyl methacrylate in the presence of fullerene C60 is performed. The reactions of interaction between initiating and model short-chain growing radicals containing from one to three monomer units with fullerene are considered. It is shown that, at the initial stage of styrene polymerization, the addition of short-chain growing radicals to fullerene predominates (with respect to the reaction of chain propagation). In the case of methyl methacrylate polymerization in the presence of fullerene C60, the induction period is absent because of a higher probability of the initiation and chain propagation reactions (compared with the chain-termination reaction of short growing poly(methyl methacrylate) chains on fullerene C60). The formation of bis- and trisadducts of fullerene C60 with short-chain styrene and methyl methacrylate growing radicals is analyzed. The quantum-chemical simulation results are confirmed by electron spectroscopy and ESR studies.

Highly permeable PVDF membrane with PS/ZnO nanocomposite incorporated for distillation process.

In order to enhance the flux and wetting resistance of PVDF membranes for MD applications, we have developed a novel PVDF blend nanocomposite membrane using a polystyrene/ZnO (PS/ZnO) hybrid nanocomposite. The PS/ZnO nanocomposite was synthesized by free radical polymerization of styrene in the presence of vinyltrimethoxysilane (VTMS) grafted on the surface of ZnO nanoparticles. The blend nanocomposite membrane is fabricated via the phase inversion method and we examined the effects of the PS/ZnO nanocomposite on porosity, mechanical properties, hydrophobicity, LEPw, morphology, surface roughness and MD performance. It was found that the addition of the PS/ZnO hybrid nanocomposite (0.25, 0.5 and 0.75%) resulted in an increase in porosity (>70%), which is attributed to increased pore size and reduction of the spongy layer thickness. Furthermore, the addition of the nanocomposite also improved the surface roughness and contact angle. Comparison between the neat and modified membrane shows that with incorporation of the PS/ZnO nanocomposite, the desalination flux of 30 g L−1 saline aqueous solution significantly increased and rejection reached 99.99%. Meanwhile, during 100 hours continuous desalination process, the membranes composed of 0.75% PS/ZnO hybrid nanocomposite exhibited high performance stability (15.79 kg m−2 h−1) compared with the neat PVDF membrane.

One-pot strategy for preparation of photo- and chemo-cleavable polystyrene containing o-nitrobenzyl ester moieties

A new strategy have been developed for preparation of photo- and chemo-cleavable polystyrene by incorporating o-nitrobenzyl ester moiety as a photosensitive linking unit into the main chain of the polymer. The strategy is based on the highly efficient esterification reaction of dicarboxylic acid, 4,4′-azobis(4-cyanovaleric acid), with organic dihalide, 2-nitro-1,3-phenylene bis(methylene-2-chloroacetate) and free radical polymerization of styrene. Multiblock polystyrenes were successfully synthesized by a combination of the esterification reaction and the radical polymerization of styrene by one-pot approach. Moreover, the photocleavage behavior of the multiblock PS in THF solution and in solid state was examined under UV irradiation at room temperature in air atmosphere. The results demonstrated that the multiblock polystyrenes could be cleaved into separate PS blocks not only by UV light, but also by hydrolysis of the ester groups. In addition, the incorporation of o-nitrobenzyl units into the backbone of PS has no obvious influence on the thermal stability.

Robust particle filter for state estimation using measurements with different types of gross errors

For industrial processes, the state estimation plays a key role in various applications, such as process monitoring and model based control. Although the particle filter (PF) is able to deal with nonlinear and non-Gaussian processes, it rarely considers the influence of measurements with gross errors, such as outliers, biases and drifts. Nevertheless, measurements of dynamical systems are often influenced by different types of gross errors. This paper proposes a robust PF approach, in which gross error identification is used to estimate magnitudes of gross error. The gross errors can be removed or compensated so that a feasible set of particle sampling can contain the true states of the system. The proposed robust PF approach is implemented on a complex nonlinear dynamic system, the free radical polymerization of styrene. The application results show that the proposed approach is an appealing alternative to solving PF estimation problems with measurements containing gross errors.

Thermal decomposition reaction of pinacolone diperoxide: its use as radical initiator in the styrene polymerization

Fil: Eyler, Gladys Nora. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Fisica e Ingenieria del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones en Fisica e Ingenieria del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernacion. Comision de Investigaciones Cientificas. Centro de Investigaciones en Fisica e Ingenieria del Centro de la Provincia de Buenos Aires; Argentina