Industry 4.0 based process data analytics platform: A waste-to-energy plant case study
Research output: Contribution to journal › Article
- Outotec GmbH
Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning (ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although, this is an area of increased interest, the information concerning the implementation of data analytics in the context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial grade cloud computing platforms. The industrial applicability of the platform was demonstrated by the development of soft sensors for use in a waste-to-energy (WTE) plant. In the case study, the work studied data-driven soft sensors to predict syngas heating value and hot flue gas temperature. Among the studied data-driven methods, the neural network-based NARX model demonstrated better performance in the prediction of both syngas heating value and flue gas temperature. The modeling results showed that, in cases where process knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive data analytics.
|Journal||International Journal of Electrical Power and Energy Systems|
|Publication status||Published - 1 Feb 2020|
|MoE publication type||A1 Journal article-refereed|
- Data analytics platform, Industrial internet of things platform, Machine learning, Soft sensor, Waste-to-energy