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A Procedural Approach for Finding Kinetic Parameters of Polypropylene Gasification in Super Critical Water Using Genetic Algorithm
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This study developed a genetic algorithm-based approach to find kinetic parameters for modeling polypropylene gasification in supercritical water. The method improved the accuracy of predicting how different reaction pathways compete during plastic breakdown. Understanding plastic gasification kinetics is relevant to developing chemical recycling technologies that convert plastic waste into fuel or chemical feedstocks.
Abstract Plastic gasification under super critical conditions could yield a wide range of hydrocarbons depending on operating conditions. Phenomenological models describing such process involve multiple competing reaction routes. Different reaction mechanisms and kinetic parameters could be used to model the process. Introduced in this study, is a robust/procedural approach for finding the kinetic parameters. In this approach, genetic algorithm is utilized to find the rate constants at specific temperatures prior to finding the kinetic parameters (pre-exponential factor and activation energy). Rate constants are obtained sequentially, starting with the highest temperature and ending with the lowest temperature. Then, linear regression is used to calculate the kinetic parameters for all reactions. Values of kinetic parameters, obtained via linear regression, are used to set the limits for the genetic algorithm to find more accurate values of the kinetic parameters. The relative/normalized deviation, from the original data (main species concentration versus residence time), using this approach is 1.2%. The respective normalized deviation using linear regression was 2.7%.
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