Kinetic modelling of thermal processes using a modified Sestak-Berggren equation

Gibson, Rebecca L.; Simmons, Mark; Stitt, E. Hugh; West, John R.; Wilkinson, Sam K.; Gallen, Robert W.

doi:10.1016/j.cej.2020.127318

Cited by 19 publications

(22 citation statements)

References 16 publications

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“…Nonlinear least‐squares regression has been used in Athena Visual Studio, with default tolerances 24, with the aim of minimizing the residual on d α /d t , the dimensionless signal. The model reported elsewhere 11 is extended with the addition of the contribution from the unknown baseline.

true \begin{matrix} left \frac{normald α}{normald t} = \sum_{normali = 1}^{{normaln}_{normalevents}} F_{normalv, normali} A_{normali} normalexp (\frac{E_{a, i}}{R T_{b, i}} (1 - \frac{T_{normalb, normali}}{T})) {(1 - α_{normali})}^{{normaln}_{normali}} α_{normali}^{{normalm}_{normali}} + ε_{normalBL} \end{matrix}

…”

Section: Modeling Methodsmentioning

confidence: 99%

“…This study of baseline correction methods will be combined with the methodology reported by Gibson et al. 11, which does not require peak deconvolution, hence overall baselines are considered in this work.…”

Section: Introductionmentioning

confidence: 99%

“…It is common, but not necessary [11], for peaks observed in thermal analysis experiments to be deconvoluted prior to kinetic modeling. Michael et al [12] and Hemminger and Sarge [5] both recommend that this deconvolution be carried out prior to the baseline correction, such that each thermal event has its own baseline.…”

Section: Introductionmentioning

confidence: 99%

“…This means that using this method of baseline correction would not account for these types of baseline drifts, though use of an overall baseline correction would. This study of baseline correction methods will be combined with the methodology reported by Gibson et al [11], which does not require peak deconvolution, hence overall baselines are considered in this work.…”

Section: Introductionmentioning

confidence: 99%

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Selection of Formal Baseline Correction Methods in Thermal Analysis

et al. 2021

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Baseline correction is a key step in processing of thermal analysis data. Whilst this is a common step, techniques range from linear baselines to use of high‐order polynomials. When considering a formal baseline correction (those without physical or experimental justification), only linear correction methods should be used: linear with time, linear with temperature, and linear with extent of reaction. The absence of baseline correction should also be considered. An in silico study shows that the wrong baseline correction can significantly impact the parameters obtained from kinetic modeling. The four baseline correction methods are demonstrated with a mass spectrometry dataset. It is recommended that the selection of correction method should be based on comparison of Akaike weights.

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true \begin{matrix} left \frac{normald α}{normald t} = \sum_{normali = 1}^{{normaln}_{normalevents}} F_{normalv, normali} A_{normali} normalexp (\frac{E_{a, i}}{R T_{b, i}} (1 - \frac{T_{normalb, normali}}{T})) {(1 - α_{normali})}^{{normaln}_{normali}} α_{normali}^{{normalm}_{normali}} + ε_{normalBL} \end{matrix}

…”

Section: Modeling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selection of Formal Baseline Correction Methods in Thermal Analysis

et al. 2021

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“…Plotting and linear fitting ln((dα/dt)exp(E a /RT)) versus ln(α p (1 − α)) in the range of 0.1 < α < 0.9, n and lnA can be extracted from the slope and the intercept of the fitting, respectively, calculating the value of m according to p = m/n [33][34][35]. The values of m, n, and lnA were basically the same at different heating rates, indicating that the Málek method can ensure the uniqueness of the rate equation during non-isothermal curing.…”

Section: Determine the Curing Reaction Modelmentioning

confidence: 99%

A Study on Temperature Distribution within HVDC Bushing Influenced by Accelerator Content during the Curing Process

et al. 2022

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Power transmission technology plays an important role in energy sustainability. Bushing is an indispensable type of equipment in power transmission. In production, the accelerator changes the temperature distribution during the curing process, influencing the formation of defects and thus the safety output of renewable energy. In this study, uncured epoxy resin samples with different accelerator contents were prepared and measured by differential scanning calorimetry (DSC). The obtained heat flow curves were analyzed for curing kinetics. Then, the curing process of large length–diameter ratio bushings was simulated by using the finite element method combined with a curing kinetics model, transient Fourier heat transfer model, and stress–strain model. The study reveals that the curing system can be established by the Sestak–Berggren autocatalytic model with different accelerator contents. The overall curing degree and the maximum radial temperature difference of the capacitor core tend to increase and then decrease with the accelerator content. This is mainly attributable to the rapid exotherm excluding the participation of some molecular chains in the reaction, resulting in permanent under-curing. As the accelerator content increases, the strain peak decreases and then increases. This paper provides guidance for the comprehensive evaluation and manufacturing of the low-defect capacitor cores of large-size high voltage direct current (HVDC) bushings.

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Kinetics of Plasticized Poly(vinyl chloride) Thermal Degradation, Induction, Autocatalysis, Glass Transition, Diffusion

Krongauz

2024

ChemistrySelect

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Kinetics of non‐oxidative thermal degradation of poly(vinyl chloride) (PVC) plasticized with di(2‐ethylhexyl)phthalate and epoxidized linseed oil (ELO) were studied using differential scanning calorimetry. PVC thermal degradation autocatalyzed by forming HCl is delayed in the presence of acid scavenging ELO. Direct HCl elimination rates during steady state degradation (induction period) were almost independent on ELO concentration, with rate constants ranging from ≈3.2×10−6 s−1 at 463 K to ≈7.0×10−5 s−1 at 503 K. Activation energy of HCl elimination increased with glass transition temperature (Tg) increase from 136.0 kJ mol−1 at Tg=260.3 K to 141.9 kJ mol−1at Tg=274.2 K. Diffusion coefficients of HCl diffusion in plasticized PVC deduced using Williams‐Landel‐Ferry equation were of the order of 10−4 cm2 sec−1. Calculated activation energy of HCl diffusion in plasticized PVC was ≈30 kJ mol−1. The rate constants of PVC autocatalytic degradation were derived from the experimental data (for the 1st time) using Šesták‐Berggren approach and ranged from 3.2×10−2 sec−1 at 503 K to 4.4×10−3 sec−1 at 463 K. Autocatalysis activation energies ranged from 70.4 kJ mol−1 to 85.1 kJ mol−1 and increased with increase of glass transition temperature. It was established that PVC degradation kinetics was not controlled by HCl diffusion. Compensation effect was observed for initial and catalyzed PVC degradation.

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Kinetic modelling of thermal processes using a modified Sestak-Berggren equation

Cited by 19 publications

References 16 publications

Selection of Formal Baseline Correction Methods in Thermal Analysis

Selection of Formal Baseline Correction Methods in Thermal Analysis

A Study on Temperature Distribution within HVDC Bushing Influenced by Accelerator Content during the Curing Process

Kinetics of Plasticized Poly(vinyl chloride) Thermal Degradation, Induction, Autocatalysis, Glass Transition, Diffusion

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