Styrene ATRP using the new initiator 2,2,2-tribromoethanol: Experimental and simulation approach

Orange juice is submitted to thermal treatment to increase its shelf life, which may result in loss of ascorbic acid. This paper uses mathematical models and simulations to reproduce the experimental data of ascorbic acid thermal degradation kinetics. Two different models were considered, and all kinetic parameters were identified using linear regression on the logarithmic curves of experimental data. A quick analytical method based on ultra high-performance liquid chromatography with tandem mass spectrometry was developed to quantify ascorbic acid degradation during the process at three different operating temperatures, allowing the determination of Arrhenius' equations. Ascorbic acid losses were simulated in a tubular industrial pasteurization system by developing an appropriate mathematical modeling, and the residence time presents a higher influence on the process than temperature. In addition, some thermodynamic parameters were evaluated, confirming that the process is not spontaneous but irreversible. PRACTICAL APPLICATIONSThe mathematical modeling developed in this research can be used to estimate the ascorbic acid losses during industrial pasteurization processes. The case study of this paper concerns on tubular systems. However, the kinetic parameter estimation and the degradation rate obtained can be used in any industrial situation by an appropriate modeling adaptation, e.g., considering the common plate systems.

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“…) (Vieira et al . ).

k = k_{0} e^{- E a / R T}

with k 0 as the pre‐exponential factor (h), E a as the activation energy (J/mol) and R as the gas constant (8.314 J/mol/K).…”

Section: Methodsmentioning

confidence: 97%

“…where CA expresses the AA concentration (mg/L) at any time t, CA0 is the initial concentration of AA and k is the degradation rate constant that depends on temperature according the Arrhenius law (Eq. 3) (Vieira et al 2015).…”

Section: Kinetic Modelingmentioning

confidence: 99%

Mathematical Modeling of Ascorbic Acid Thermal Degradation in Orange Juice during Industrial Pasteurizations

Vieira

Mokochinski

Sawaya

2015

J Food Process Engineering

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“…Ainda em relação à Figura 2, verifica-se que há uma redução da perda de carga quando a concentração de B passa de 0,2 M para 0,4 M. Este resultado está intimamente relacionado com a conversão de A, que aumenta para uma mesma massa de catalisador (MAT NOOR et al, 2010;VIEIRA et al, 2015). Dispondo-se das vazões molares de cada componente e da massa molar do componente C, construiu-se o gráfico da Figura 4, que ilustra o custo de catalisador (linha reta em azul) e o faturamento com a produção de C (linhas exponenciais em vermelho) no primeiro mês de operação do sistema, ambos em função da massa de catalisador.…”

Section: Resultsunclassified

“…Por exemplo, nas fases iniciais do projeto, tal ferramenta contribui para a análise e dimensionamento do sistema, bem como na identificação de seus parâmetros chave (VIEIRA; LONA, 2015). Por outro lado, uma vez que um processo está consolidado, a modelagem e simulação podem fornecer informações sobre as melhores condições de operação, viabilizando o trabalho em um sistema otimizado (CHACHUAT; LATIFI, 2003;SILVA et al, 2016;LONA, 2016).…”

Section: Introductionunclassified

Influência Da Massa De Catalisador Nos Custos E Receitas De Um Reator Catalítico De Leito Fixo

Ribeiro¹,

Vieira²

2017

Enci Bio

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RESUMOEste trabalho consiste em uma análise da influência da massa de catalisador nos custos e receitas de um reator catalítico de leito fixo (PBR), que pode ser empregada para fins didáticos em cursos de graduação em engenharia química. Desenvolveuse um modelo matemático para o PBR considerando um sistema genérico em fase gasosa, com perda de carga no leito. Em seguida, dispondo do sistema de equações e de seus parâmetros, uma simulação forneceu a influência da concentração de um dado reagente "B" em excesso sobre o lucro obtido na venda de um produto "C". Obteve-se um lucro de 115 % em relação à massa de catalisador adquirida (10 kg) para C B0 = 0,4 M; enquanto que para C B0 = 0,2 M houve um prejuízo de 17 % para a mesma massa de catalisador utilizada no primeiro mês de operação do sistema. A análise foi proposta de forma simples, sendo assim, o problema proposto pode ser uma nova ferramenta para complementar o ensino de engenharia química. PALAVRAS-CHAVE: avaliação econômica, PBR, reator de leito fixo. INFLUENCE OF CATALYST MASS ON THE COSTS AND INCOMES OF A CATALYTIC PACKED BED REACTORABSTRACT This work presents an analysis of catalyst mass influence on the costs and incomes of a catalytic PBR, which can be used for didactic purposes in chemical engineering undergraduate courses. A PBR mathematical modeling was developed considering a generic system, in the gas phase, with pressure drop. Then, having the equations system and their parameters, simulation provided the influence of a given reagent "B" concentration in excess on the profit by selling "C". A gain of 115% was obtained with respect to the mass of catalyst purchased (10 kg) for C B0 = 0.4 M; While for C B0 = 0.2 M, there was a 17% loss for the same mass of catalyst used in the first month of system operation. The analysis was proposed in a simple way, so the proposed problem could be a new tool to complement the teaching of chemical engineering.

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“…This assumption does not bias the nature of ATRP qualitatively and has been practiced in almost all previous ATRP simulation research. (Weiss et al, 2015;Preturlan et al, 2016;Vieira & Lona, 2016b;Li et al, 2011;Vieira et al, 2015) In some of our simulations, we altered the rate constants by up to ±30% to account for possible inaccuracies in the measurement of these values and other unpredictable situations such as turbulence in the reactor temperature. We employed the VODE (Brown et al, 1989;Byrne & Hindmarsh, 1975;Hindmarsh & Byrne, 1977;Hindmarsh, 1983;Jackson & Sacks-Davis, 1980) integrator implemented in SciPy 0.19 using a maximum internal integration step of 5000, which is sufficient to achieve final MWDs with high accuracy.…”

Section: Simulating Atrpmentioning

confidence: 99%

Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

Collins

Ribelli

et al. 2018

Mol. Syst. Des. Eng.

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We devise a novel technique to control the shape of polymer molecular weight distributions (MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent advances in both simulation-based, modelfree reinforcement learning (RL) and the numerical simulation of ATRP. A simulation of ATRP is built that allows an RL controller to add chemical reagents throughout the course of the reaction. The RL controller incorporates fully-connected and convolutional neural network architectures and bases its decision upon the current status of the ATRP reaction. The initial, untrained, controller leads to ending MWDs with large variability, allowing the RL algorithm to explore a large search space. When trained using an actorcritic algorithm, the RL controller is able to discover and optimize control policies that lead to a variety of target MWDs. The target MWDs include Gaussians of various width, and more diverse shapes such as bimodal distributions. The learned control policies are robust and transfer to similar but not identical ATRP reaction settings, even under the presence of simulated noise. We believe this work is a proof-of-concept for employing modern artificial intelligence techniques in the synthesis of new functional polymer materials.

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Styrene ATRP using the new initiator 2,2,2-tribromoethanol: Experimental and simulation approach

Cited by 16 publications

References 55 publications

Mathematical Modeling of Ascorbic Acid Thermal Degradation in Orange Juice during Industrial Pasteurizations

Mathematical Modeling of Ascorbic Acid Thermal Degradation in Orange Juice during Industrial Pasteurizations

Influência Da Massa De Catalisador Nos Custos E Receitas De Um Reator Catalítico De Leito Fixo

Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

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