Skip to content
Research Article Open access CC BY 4.0

Application of Artificial Neural Network in Distillation System: A Critical Review of Recent Progress

Chunli Li, Chunyu Wang

Asian Journal of Research in Computer Science · pp. 8–16 · Published 9 Aug 2021

10.9734/ajrcos/2021/v11i130252

Abstract

Distillation is a unit operation with multiple input parameters and multiple output parameters. It is characterized by multiple variables, coupling between input parameters, and non-linear relationship with output parameters. Therefore, it is very difficult to use traditional methods to control and optimize the distillation column. Artificial Neural Network (ANN) uses the interconnection between a large number of neurons to establish the functional relationship between input and output, thereby achieving the approximation of any non-linear mapping. ANN is used for the control and optimization of distillation tower, with short response time, good dynamic performance, strong robustness, and strong ability to adapt to changes in the control environment. This article will mainly introduce the research progress of ANN and its application in the modeling, control and optimization of distillation towers.

Artificial neural network distillation BPNN RBFNN

Cited by 2

ATFSAD: Enhancing Long Sequence Time-Series Forecasting on Air Temperature Prediction

Bin Yang, Tinghuai Ma, Xuejian Huang · IEEE Access · 2023

Study on polyvinyl butyral purification process based on Box-Behnken design and artificial neural network

Huihui Wang, Wenwen Luan, Li Sun · Chemical Engineering Research and Design · 2022

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

2

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.