摘要
随着计算机技术的发展,粒子群算法在聚合物的热分解动力学领域广泛应用。虽然粒子群算法可以实现全局寻优,但也存在收敛速度慢且易陷入局部最优解的缺陷。针对标准粒子群算法的缺陷,引入自适应惯性权重与加速常数对粒子群算法进行改进,提出一种动态自适应粒子群算法(DAPSO),并进行6个测试函数的仿真实验。结果表明:DAPSO算法比MPSO及MeanPSO算法收敛速度更快且精度更高。将DAPSO算法与Kissinger法结合得到了K-DAPSO算法,分别利用DAPSO算法与K-DAPSO算法结合聚乙烯DTG曲线,对两步平行反应模型进行参数反演。K-DAPSO算法较DAPSO算法能够更快收敛到最优解。提出的两步平行反应模型能够准确描述聚乙烯热失重曲线复杂的多峰结构。
With the development of computer technology,particle swarm optimization algorithm is widely used in the field of thermal decomposition kinetics of polymers.Although the particle swarm optimization algorithm can achieve global optimization,it also has the defects of slow convergence speed and easy to fall into local optimal solution.Aiming at the defects of the standard particle swarm optimization algorithm,the adaptive inertia weight and acceleration constant are introduced to improve the particle swarm optimization algorithm,and a dynamic adaptive particle swarm optimization algorithm(DAPSO)is proposed.The simulation experiments of six test functions are carried out.The results show that the DAPSO algorithm has faster convergence speed and higher accuracy than the MPSO and MeanPSO algorithms.The K-DAPSO algorithm is obtained by combining the DAPSO algorithm with the Kissinger method.The DAPSO algorithm and the K-DAPSO algorithm are combined with the polyethylene DTG curve to perform parameter inversion on the two-step parallel reaction model.The K-DAPSO algorithm can converge to the optimal solution faster than the DAPSO algorithm.The proposed two-step parallel reaction model can accurately describe the complex multi-peak structure of polyethylene thermal weight loss curve.
作者
孙诗洋
王勇
SUN Shi-yang;WANG Yong(School of Resources and Environmental Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《塑料科技》
CAS
北大核心
2023年第3期67-72,共6页
Plastics Science and Technology
基金
国家自然科学基金项目(51306097)
山东省自然科学基金项目(ZR2019MEE114)
湖北省教育厅科学研究计划(B2021012)。
