Inferential control strategies using neural soft sensor in a high purity distillation column

Authors

  • Arioston Araújo de Morais Júnior
  • José Carlos Finiz Filho
  • Leopoldo Oswaldo Alcazar Rojas
  • Romildo Pereira Brito

DOI:

https://doi.org/10.54033/cadpedv21n5-160

Keywords:

High Purity Column, Soft Sensor, Artificial Neural Network, Inferential Control

Abstract

High-purity distillation columns are processes in which you want to minimally increase the purity of a key component by separation. These processes are sensitive to disturbances, where small changes in the feed flowrate stream cause drastic changes in product compositions. Furthermore, when one wants to apply traditional control and optimization techniques to these processes, some difficulties have to be faced: the process is generally non-linear and has a long response time, there are many immeasurable disturbances, it is difficult to keep the process in steady-state, and the bottom and top compositions are highly coupled. Therefore, this paper presents a methodology for the development of soft sensors (SS), here in applied to an industrial 1,2-Dichloroethane separation plant. The process was simulated and validated with real data from the industrial plant. In the step second an algorithm based on multivariate statistics was developed for the selection of inputs SS variables. The configuration for training multilayer artificial neural network (ANN's) was optimize, accounting for the ANN's prediction capacity of responses and the rejection of disturbances inserted in the process. Thereafter, a temperature control was implemented to maintain the impurity compositions within specifications but failed due to the small temperature variations in the high purity column. To overcome these difficulty three inferential control strategies (impurity composition estimated by the SS) were implemented: a classic feedback control, a cascade control, and a ratio-cascade control. All control strategies acted to minimize the process disturbances effects. However, the inferential ratio-cascade control shown to be the most robust, because of the lowest integral error criteria value and percentage overshoot.

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Published

2024-05-21

How to Cite

Morais Júnior, A. A. de, Finiz Filho, J. C., Rojas, L. O. A., & Brito, R. P. (2024). Inferential control strategies using neural soft sensor in a high purity distillation column. Caderno Pedagógico, 21(5), e4402 . https://doi.org/10.54033/cadpedv21n5-160

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Articles