General and more precise relationships between molecular weight, blend ratio, and melt index of binary polyethylene blends

2014

In the propylene polymerization process, the melt index (MI), as a critical quality variable in determining the product specification, cannot be measured in real time. What we already know is that MI is influenced by a large number of process variables, such as the process temperature, pressure, and level of liquid, and a large amount of their data are routinely recorded by the distributed control system. An alternative data-driven model was explored to online predict the MI, where the least squares support vector machine was responsible for establishing the complicated nonlinear relationship between the difficult-to-measure quality variable MI and those easy-to-measure process variables, whereas the independent component analysis and particle swarm optimization technique were structurally integrated into the model to tune the best values of the model parameters. Furthermore, an online correction strategy was specially devised to update the modeling data and adjust the model configuration parameters via adaptive behavior. The effectiveness of the designed data-driven approach was illustrated by the inference of the MI in a real polypropylene manufacturing plant, and we achieved a root mean square error of 0.0320 and a standard deviation of 0.0288 on the testing dataset. This proved the good prediction accuracy and validity of the proposed data-driven approach.

Section: Introductionmentioning

confidence: 99%

Quality control in the polypropylene manufacturing process: An efficient, data‐driven approach

Cheng

2014

“…The chemical and physical reactions in the reactors are so complicated that modeling the reactors or the reaction processes that happen in the reactors7–10 becomes a task with huge difficulty. Despite the simplification of the mechanical models, it still needs great efforts to fit the inner relationship between MI and some of the factors in the reaction 10–12…”

Section: Introductionmentioning

confidence: 99%

Melt index prediction by adaptively aggregated RBF neural networks trained with novel ACO algorithm

Jiang

et al. 2011

Three estimation models of polypropylene (PP) process to infer the melt index, an important quality indicator determining product specification, are presented. Radial basis function (RBF) neural network (NN) is used to develop the models because of its capacity of fitting the complex relationship in PP process. A novel ant colony optimization (ACO) algorithm is also proposed and used to solve the optimization problem of the continuous linking weights when training the RBF NN. Based on the RBF NN and the novel ACO algorithm, a single NN model is developed. However, a single network cannot always work well due to some defects (such as overfitting) of a NN. Thus, as an improvement of the single NN model, several RBF NN trained with a certain objective are combined, and the aggregated NN model is obtained. To make the aggregated NN more robust and effective, an adaptive method of assigning the combinational weight to every individual network is applied to the former aggregated NN model and finally an adaptive aggregated NN model is achieved. Further researches of the three models are carried out on the data from a real industrial plant, and the prediction result shows that the performance of the obtained prediction models is better and better with every improvement step taken as above. The adaptive aggregated NN model works best, and the satisfying prediction error it provides depicts its prediction accuracy and universality, as well as an application prospect in PP process. V C 2011 Wiley Periodicals, Inc. J Appl Polym Sci 125: [943][944][945][946][947][948][949][950][951] 2012

“…The chemical and physical reactions in the reactors are so complicated that it is very difficult to model the reactors or the reaction processes happen in the reactors 7, 8. Even simplified mechanical models need to be developed with great effort to fit the inner relationship between MI and some of the factors in the reaction 9–11…”

Section: Introductionmentioning

confidence: 99%

Melt index prediction by RBF neural network optimized with an adaptive new ant colony optimization algorithm

2010

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 network, 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.