Skip to content
Research Article Open access CC BY 4.0

Review of Neural Network Algorithm and Its Application in Reactive Distillation

Huihui Wang, Ruyang Mo

Asian Journal of Chemical Sciences · pp. 20–29 · Published 8 Mar 2021

10.9734/ajocs/2021/v9i319073

Abstract

Artificial Neural Networks (ANN) can accurately identify and learn the potential relationship between input and output, and have self-learning capabilities and high fault tolerance, which can be used to predict or optimize the performance of complex systems. Reactive distillation integrates reaction and rectification into one device, so that the two processes occur at the same time and at the same place, but at the same time it also produces highly nonlinear robust behavior, making its process control and optimization unable to use conventional methods. Instead, neural network algorithms must be used. This paper briefly describes the research progress of neural network algorithms and reactive distillation technology, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.

Reactive distillation neural network algorithms BP neural network RBF neural network

Cited by 4

Evolutionary artificial neural network for temperature control in a batch polymerization reactor

Francisco Javier Sánchez-Ruiz, Elizabeth Argüelles Hernandez, José Terrones-Salgado · Ingenius · 2023

Mathematical Modeling and Advanced Control of the Refinery Processes: A Review

Laith S. Mahmood, Khalid Alzobai, Salam K. Al-Dawery · Al-Nahrain Journal for Engineering Sciences · 2025

Identification of Peking duck breed based on hyperspectral imaging with machine learning

Changying Shen, Mengping Dong, Juan Du · Fourth International Conference on Signal Processing and Computer Science (SPCS 2023) · 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

4

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.