化学过程中的数据挖掘和知识发现:替代处理技术的影响

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
L. Briceno-Mena, M. Nnadili, M. G. Benton, J. Romagnoli
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引用次数: 2

摘要

摘要数据挖掘和知识发现(DMKD)侧重于从数据中提取有用的信息。在化工过程工业中,可以使用DMKD实现过程监控、故障检测、过程控制、优化等任务。然而,为DMKD过程中的每个步骤选择合适的方法,即数据清理、采样、缩放、降维(DR)、聚类、聚类分析和数据可视化,以获得有意义的见解,绝非易事。在这篇文章中,引入了一个计算环境(FastMan),并用它来说明方法选择如何影响化学过程数据中的DMKD。使用模拟天然气液体工厂的数据和工业热解装置的真实数据进行了两个案例研究,以证明这些方法在现实场景中的适用性。采样和归一化方法对DMKD结果的质量有很大影响。此外,用于DR的邻居图方法,t-分布随机邻居嵌入,优于主成分分析,主成分分析是化学过程工业中经常用于识别局部和全局变化的矩阵分解方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques
Abstract Data mining and knowledge discovery (DMKD) focuses on extracting useful information from data. In the chemical process industry, tasks such as process monitoring, fault detection, process control, optimization, etc., can be achieved using DMKD. However, the selection of the appropriate method for each step in the DMKD process, namely data cleaning, sampling, scaling, dimensionality reduction (DR), clustering, clustering analysis and data visualization to obtain meaningful insights is far from trivial. In this contribution, a computational environment (FastMan) is introduced and used to illustrate how method selection affects DMKD in chemical process data. Two case studies, using data from a simulated natural gas liquid plant and real data from an industrial pyrolysis unit, were conducted to demonstrate the applicability of these methodologies in real-life scenarios. Sampling and normalization methods were found to have a great impact on the quality of the DMKD results. Also, a neighbor graphs method for DR, t-distributed stochastic neighbor embedding, outperformed principal component analysis, a matrix factorization method frequently used in the chemical process industry for identifying both local and global changes.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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