研究目的
To explore the possibility of adding the DSC curve as a new target in the numerical optimization process for estimating kinetic and thermal parameters of thermal decomposition, specifically for PMMA, to simultaneously fit experimental and simulated TGA and DSC curves.
研究成果
The study concludes that simultaneously optimizing TGA and DSC curves is challenging due to higher errors in DSC fitting. The best approach is to first optimize the TGA curve and then adjust the heat of reaction parameters for DSC. The obtained parameters are valid for simulating TGA curves at different heating rates but less accurate for DSC curves, indicating a need for further investigation into DSC optimization.
研究不足
The DSC curve cannot be fitted with the same accuracy as the TGA curve when used simultaneously in optimization, leading to increased errors. The methodology is specific to PMMA and may not generalize to other materials. The approach requires prior fitting of TGA before adjusting DSC, indicating a sequential rather than simultaneous optimization limitation.
1:Experimental Design and Method Selection:
The study uses Simultaneous Thermal Analysis (STA) combining TGA and DSC tests, following ASTM-E1131 standard, with oxygen concentration of 21% and heating rates of 30 K/min, 10 K/min, and 50 K/min. A numerical optimization method (Shuffled Complex Evolution, SCE) is applied to fit kinetic and thermal parameters based on the FDS pyrolysis model, which uses Arrhenius equations for decomposition reactions.
2:Sample Selection and Data Sources:
Poly(methyl methacrylate) (PMMA) is the material analyzed. Laboratory tests were repeated three times to ensure repeatability.
3:List of Experimental Equipment and Materials:
Equipment includes a Simultaneous Thermal Analyzer (STA) for TGA and DSC measurements. Materials include PMMA samples.
4:Experimental Procedures and Operational Workflow:
Samples are heated from 50°C to 800°C at specified rates. Mass loss (TGA) and energy changes (DSC) are recorded. The SCE algorithm provides input parameters to FDS for simulation, and errors between experimental and simulated curves are minimized.
5:Data Analysis Methods:
Error functions (Eq. 4 and 5) are used to evaluate differences between experimental and simulated TGA and DSC curves, with coefficients α and β weighting the errors. Statistical analysis involves comparing errors across different optimization attempts.
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