Melt index (MI) is a crucial indicator in determining the product specifications and grades of polypropylene (PP). The prediction of MI, which is important in quality control of the PP polymerization process, is studied in this work. Based on RBF (radial basis function) neural network, a soft-sensor model (RBF model) of the PP process is developed to infer the MI of PP from a bunch of process variables. Considering that the PP process is too complicated for the RBF neural network with a general set of parameters, a new ant colony optimization (ACO) algorithm, N-ACO, and its adaptive version, A-N-ACO, which aim at continuous optimizing problems are proposed to optimize the structure parameters of the RBF neural net-work, respectively, and the structure-best models, N-ACO-RBF model and A-N-ACO-RBF model for the MI prediction of propylene polymerization process, are presented then. Based on the data from a real PP production plant, a detailed comparison research among the models is carried out. The research results confirm the prediction accuracy of the models and also prove the effectiveness of proposed N-ACO and A-N-ACO optimization approaches in solving continuous optimizing problem. (C) 2010 Wiley Periodicals, Inc. J Appl Polym Sci 119: 3093-3100, 2011