Implementation of a Control Strategy for Hydrodynamics of a Stirred Liquid–Liquid Extraction Column Based on Convolutional Neural Networks,ACS Engineering Au

当前位置： X-MOL 学术 › ACS Eng. Au › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Implementation of a Control Strategy for Hydrodynamics of a Stirred Liquid–Liquid Extraction Column Based on Convolutional Neural Networks
ACS Engineering Au ( IF 4.3 ) Pub Date : 2022-04-29 , DOI: 10.1021/acsengineeringau.2c00014
Laura M. Neuendorf ₁ , Fatemeh Z. Baygi ₁ , Pia Kolloch ₁ , Norbert Kockmann ₁

Affiliation

Online process supervision is enabled by smart sensor development, hence attracting increasing attention in pharmaceutical and chemical process engineering. Additional sensor data enable more precise process control as additional process parameters can be monitored. They are easy to integrate into modular plants, and their provided additional process parameters enable a more flexible operation of the apparatus due to the quick and more sensitive reaction to changing circumstances. An artificial intelligence-based optical sensor for the investigation of different operating states and droplet sizes within a liquid–liquid stirred DN32 extraction column in counter current flow is developed and presented in this work. Two operating states, the flooding state and the column’s regular operating state, are differentiated as observable states. Additionally, the diameter of the rising liquid droplets of the disperse phase is categorized into different diameter classes. A control strategy for the extraction column is derived based on the results of the convolutional neural network-based image analysis. Thus, a robust soft sensor controlling the hydrodynamics of an extraction column was developed. The developed control strategy automatically leads the extraction column into a favorable hydrodynamically stable operation state.

中文翻译：

基于卷积神经网络的搅拌液-液萃取塔流体动力学控制策略的实现

智能传感器的开发使在线过程监控成为可能，因此在制药和化学过程工程中引起了越来越多的关注。额外的传感器数据可实现更精确的过程控制，因为可以监控额外的过程参数。它们很容易集成到模块化工厂中，并且由于对不断变化的环境做出快速和更敏感的反应，它们提供的附加工艺参数使设备的操作更加灵活。本文开发并介绍了一种基于人工智能的光学传感器，用于研究逆流中液-液搅拌 DN32 萃取塔内的不同操作状态和液滴尺寸。两种运行状态，溢流状态和塔的常规运行状态，被区分为可观察状态。此外，分散相上升液滴的直径分为不同的直径等级。基于基于卷积神经网络的图像分析结果推导出了提取列的控制策略。因此，开发了一种控制萃取柱流体动力学的稳健软传感器。开发的控制策略自动引导萃取塔进入良好的流体动力学稳定运行状态。

更新日期：2022-04-29

点击分享查看原文

点击收藏

阅读更多本刊最新论文