Modeling of ammonia conversion rate in ammonia synthesis based on a hybrid algorithm and least squares support vector regression

Abstract

AbstractIn ammonia synthesis production, the ammonia conversion rate reflects how well the synthesis proceeds. In this paper, a model, which characterizes the relationship between operational variables and ammonia conversion rate, is established using least squares support vector regression (LSSVR). A hybrid algorithm of particle swarm optimization and differential evolution (HPSODE) is proposed to identify the hyperparameters of LSSVR, i.e. the regulation parameter and the width of the kernel function. HPSODE is first tested through benchmark functions and the performance is evaluated with traditional particle swarm optimization (PSO), differential evolution (DE), and a hybrid particle swarm optimization with differential evolution operator (DEPSO). It is then applied to the modeling of ammonia synthesis process. Results using other modeling methods [back propagation neural network (BPNN), LSSVR, PSO–LSSVR, and DE–LSSVR] are presented for comparison purpose. The proposed HPSODE–LSSVR modeling shows good feasibility of the algorithm and reliability of global convergence. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.