Optimization of process parameters on grain size of Fe3O4 nanoparticles by support vector regression

Support vector machine (SVM), which is a novel technology for solving classification and regression issues, has been successfully used in many fields. In this study, according to an experimental dataset on the average grain size of Fe₃O₄ nanoparticles, a predicting and optimizing model using support vector regression (SVR) was developed. In this model, the estimated result of SVR agreed with the experimental data well. In addition, the particle swarm optimization (PSO) algorithm is employed for optimizing the parameters of SVR models and obtaining the optimal process parameters for preparing Fe₃O₄ nanoparticles. The minimum grain size of Fe₃O₄ nanoparticles is forecasted to be 10nm while the Fe₃O₄ nanoparticles are synthesized by using the optimal process parameters. Meanwhile, multifactor analysis is conducted for investigating the influence of process parameters on the average grain size of Fe₃O₄ nanoparticles. The results suggest that SVR is capable of providing important theoretical and practical guide in research and development of Fe₃O₄ nanoparticles possessing ideal grain size.