Data mining and knowledge discovery in chemical processes: Effect of alternative processing techniques
نویسندگان
چکیده
Abstract Data mining and knowledge discovery (DMKD) focuses on extracting useful information from data. In the chemical process industry, tasks such as monitoring, fault detection, control, optimization, etc., can be achieved using DMKD. However, selection of appropriate method for each step in DMKD process, namely data cleaning, sampling, scaling, dimensionality reduction (DR), clustering, clustering analysis visualization to obtain meaningful insights is far trivial. this contribution, a computational environment (F ast M an ) introduced used illustrate how affects Two case studies, simulated natural gas liquid plant real industrial pyrolysis unit, were conducted demonstrate applicability these methodologies real-life scenarios. Sampling normalization methods found have great impact quality results. Also, neighbor graphs DR, t-distributed stochastic embedding, outperformed principal component analysis, matrix factorization frequently industry identifying both local global changes.
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ژورنال
عنوان ژورنال: Data-centric engineering
سال: 2022
ISSN: ['2632-6736']
DOI: https://doi.org/10.1017/dce.2022.21