کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5132154 1491509 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A similarity elastic window based approach to process dynamic time delay analysis
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
A similarity elastic window based approach to process dynamic time delay analysis
چکیده انگلیسی


- A dynamic time delay analysis by elastic windows (e-DTA) is proposed.
- The proposed e-DTA method aims at estimating transfer time delay between variables.
- The performances of the proposed e-DTA method are validated through two case studies.

Due to a large number of correlated process variables involved in industrial processes, dynamic characteristics of time delays between correlated process variables are generally major concerns. Traditionally, the time delay is approximately estimated by static sliding time windows, which could not better deal with the dynamics of time delays. In response to this problem, this paper proposes a dynamic time delay analysis (e-DTA, dynamic time delay analysis by elastic windows) method based on similarity elastic windows, which is aiming at effectively estimating the transfer time delay between process variables. According to contrast similarities between correlated variables, the size of the elastic window is self-tuned and the dynamic delay time can be estimated offline. Subsequently, through an additional correlation analysis for time series of the time delay estimated from historical data, main variables influencing the time delay can be obtained. By providing relevant trend variables, an improved fuzzy interpolation prediction method is suggested to estimate the transfer time delay between process correlated variables online. In addition, an e-DTA dynamic directed time graph is created by combining dynamic transfer time delays of mutually dependent variables. Finally, performances of the e-DTA method are tested through a numerical study and a distillation column simulation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 170, 15 November 2017, Pages 13-24
نویسندگان
, ,