Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development
نویسندگان
چکیده
Data management systems are increasingly used in industrial processes. However, data collected as part of process operations, such sensor or measurement instruments data, contain various sources errors that can hamper analysis and decision making. The authors propose an operating-regime-based processing framework for was designed to increase the quality take advantage available use make informed offline strategic business operation decisions, i.e., environmental, cost energy analysis, optimization, fault detection, debottlenecking, etc. approach synthesized from best practices derived improved upon its predecessor by putting forward combination expertise data-driven approaches. This systematic structured includes following stages: (1) scope (2) signal processing, (3) steady-state operating periods (4) reconciliation (5) regime detection identification. proposed is applied brownstock washing department a dissolving pulp mill. Over 5-month period, found be 32% time. Twenty (20) distinct regimes were identified. Further with help techniques, principal component k-means clustering showed main drivers explaining level tanks, density, shower wash water flow rate. Additionally, it concluded top four persistently problematic sensors across spans would need verified three meters (06FIC137, 06FIC152, 06FIC433), one consistency (06NIC423). information relayed experts contacts at plant further investigation.
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ژورنال
عنوان ژورنال: Processes
سال: 2023
ISSN: ['2227-9717']
DOI: https://doi.org/10.3390/pr11082376