The path to digitalization has many obstacles, and data quality is one of the biggest. Especially for online systems that process signals from meters, the plant DCS or a historical database, incorrect or missing input data pose a great danger, as users quickly begin to distrust the system, judging by the resulting useless system output. The dusty keyboard syndrome can emerge and undermine all efforts to create a system aimed at improving quality and productivity, leaving only costs and dissatisfaction.
ENEXSA is an Austrian expert company with focus on technical consultancy and software systems for the power and energy-intensive process industry. Our online products include Fuel Demand Models (FDM) and plant accounting and settlement systems (PASS) for long-term power purchase agreements in which information about the current operating mode and ambient conditions is processed to determine the guaranteed fuel consumption of the plant, as well as performance monitoring systems in which current performance is compared to expected performance on an equipment level. In both cases, a detailed physics-based thermodynamic model is fed with online input data which – for the calculation to produce valid and correct results – must be COMPLETE, CORRECT and CONSISTENT.
Based on its almost twenty years of experience in supplying online solutions to the power industry, ENEXSA developed the Data Processing Service (DPS) platform as a comprehensive tool for configuring and managing the data flow to and from process simulation models or other online applications. Its modular design and user-friendly graphical user-interface enable the user to configure complex workflows including various pre-and post-processing steps, and to test and debug the entire workflow without any coding or in-depth IT knowledge.
In this webinar, ENEXSA describes the typical problems that you will have to face when dealing with real-life online signals in process plants and demonstrates how our Data Processing Service enables you to effectively mitigate the garbage in garbage out syndrome.
Key Learning Objectives
- Understanding the different types of data errors in online applications
- Learn about the basic measures to effectively identify invalid data
- How to ensure that real-life data quality doesn’t stop your systems
- Understanding the benefits of the ENEXSA Data Processing Service platform
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