کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
689261 | 889600 | 2013 | 12 صفحه PDF | دانلود رایگان |

• An integrated just-in-time least squares support vector regression is proposed for quality prediction of multi-grade processes.
• A probabilistic analysis approach is presented for judgment of the current state before prediction of a new sample.
• The prediction is based on either a special steady grade or the transitional grade.
• The proposed soft sensor has been successfully applied to online prediction of the melt index in an industrial plant.
Multi-grade processes have played an important role in the fine chemical and polymer industries. An integrated nonlinear soft sensor modeling method is proposed for online quality prediction of multi-grade processes. Several single least squares support vector regression (LSSVR) models are first built for each product grade. For online prediction of a new sample, a probabilistic analysis approach using the statistical property of steady-state grades is presented. The prediction can then be obtained using the corresponding LSSVR model if its probability of the special steady-state grade is large enough. Otherwise, the query sample is considered located in the transitional mode because it is not similar to any steady-state grade. In this situation, a just-in-time LSSVR (JLSSVR) model is constructed using the most similar samples around it. To improve the efficiency of searching for similar samples of JLSSVR, a strategy combined with the characteristics of multi-grade processes is proposed. Additionally, the similarity factor and similar samples of JLSSVR can be determined adaptively using a fast cross-validation strategy with low computational load. The superiority of the proposed soft sensor is first demonstrated through a simulation example. It is also compared with other soft sensors in terms of online prediction of melt index in an industrial plant in Taiwan.
Journal: Journal of Process Control - Volume 23, Issue 6, July 2013, Pages 793–804