Estimation of kinetic parameters from adiabatic calorimetric data by a hybrid Particle Swarm Optimization method.
- Resource Type
- Article
- Authors
- Guo, Zi-Chao; Chen, Li-Ping; Chen, Wang-Hua
- Source
- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A. Jun2017, Vol. 122, p273-279. 7p.
- Subject
- *CHEMICAL decomposition kinetics
*PARTICLE swarm optimization
*CALORIMETRY
*NONLINEAR systems
*HYBRID systems
- Language
- ISSN
- 0263-8762
Due to the intense non-linear behavior in the task of estimation of the kinetic parameters from the experimental adiabatic data, a hybrid Particle Swarm Optimization (PSO) is proposed to estimate the kinetic parameters. This method is applied to two real cases: decomposition of DTBP and a nitro-compound under adiabatic conditions. By comparing the experimental and calculated temperature rise rate curve, the accuracy of the fitted parameters is verified. These two cases reasonably prove the validation of this hybrid PSO algorithm in the estimation of kinetic model parameters of adiabatic data. [ABSTRACT FROM AUTHOR]