A review of neural network algorithms and their applications in supercritical extraction

• URL: http://arxiv.org/abs/2011.05279v1
• Date: Sat, 31 Oct 2020 01:51:02 GMT
• Title: A review of neural network algorithms and their applications in supercritical extraction
• Authors: Yu Qi, Zhaolan Zheng
• Abstract summary: This paper briefly describes the basic concepts and research progress of neural networks and supercritical extraction. It aims to provide reference for the development and innovation of industry technology.
• Score: 5.455337487096457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network realizes multi-parameter optimization and control by simulating certain mechanisms of the human brain. It can be used in many fields such as signal processing, intelligent driving, optimal combination, vehicle abnormality detection, and chemical process optimization control. Supercritical extraction is a new type of high-efficiency chemical separation process, which is mainly used in the separation and purification of natural substances. There are many influencing factors. The neural network model can quickly optimize the process parameters and predict the experimental results under different process conditions. It is helpful to understand the inner law of the experiment and determine the optimal experimental conditions. This paper briefly describes the basic concepts and research progress of neural networks and supercritical extraction, and summarizes the application of neural network algorithms in supercritical extraction, aiming to provide reference for the development and innovation of industry technology.

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