# Neural network algorithm and its application in temperature control of distillation tower

arXiv: https://arxiv.org/abs/2101.00582 · HTML: https://mlsanity.com/paper/2101.00582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild](https://mlsanity.com/paper/1911.11130/) (2019)
- [Nonparallel Emotional Speech Conversion](https://mlsanity.com/paper/1811.01174/) (2018)
- [Distributionally Robust Games: f-Divergence and Learning](https://mlsanity.com/paper/1702.05371/) (2017)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots](https://mlsanity.com/paper/2406.15515/) (2024)

## Abstract (arXiv)

Distillation process is a complex process of conduction, mass transfer and heat conduction, which is mainly manifested as follows: The mechanism is complex and changeable with uncertainty; the process is multivariate and strong coupling; the system is nonlinear, hysteresis and time-varying. Neural networks can perform effective learning based on corresponding samples, do not rely on fixed mechanisms, have the ability to approximate arbitrary nonlinear mappings, and can be used to establish system input and output models. The temperature system of the rectification tower has a complicated structure and high accuracy requirements. The neural network is used to control the temperature of the system, which satisfies the requirements of the production process. This article briefly describes the basic concepts and research progress of neural network and distillation tower temperature control, and systematically summarizes the application of neural network in distillation tower control, aiming to provide reference for the development of related industries.