OPTIMIZATION OF FRACTIONAL DISTILLATION COLUMN IN CRUDE OIL REFINERY USING ARTIFICIAL NEURAL NETWORK
https://doi.org/10.55452/1998-6688-2022-19-1-6-16
Abstract
The paper outlines the methods, which improve the controlling process of separating methanol from water in the distillation column to produce crude oil products. Nowadays, many industries use PID controllers to control process variables like temperature, flow, pressure, level, which helps maintain good performances. However, PID controllers can have slightly bad performances in complicated control systems, such as in Multiple Input and Multiple Output (MIMO) systems; due to this, optimization methods of improving PID are considered. А tremendous amount of work has been done rеfіnіng, studyingаndimprоvіng the PID controlling techniquesand methods. However, PID still faces challenges in a variety of common control problems. This article represents NеuralNеtworkAlgоritmbаsеd PID cоntrollеr, whіchіsusеdtоcоntrоlthе separating process of methanol from water in the distillation column, due to Nеuralnеtwork’s good generalization results. The Wood and Berry mathematical Model was chosen as the main control object.
About the Author
Dana Maksutkyzy Ismagulova
JSC “Wood KSS”
Kazakhstan
Kazakhstan
Master, Associate Automation and Control Enginee
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Review
For citations:
Ismagulova D.M. OPTIMIZATION OF FRACTIONAL DISTILLATION COLUMN IN CRUDE OIL REFINERY USING ARTIFICIAL NEURAL NETWORK. Herald of the Kazakh-British technical university. 2022;19(1):6-16. https://doi.org/10.55452/1998-6688-2022-19-1-6-16
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