Process Control & Automation
This paper is the recipient of the 2021 AIST James Farrington Award. For more information about AIST Awards, visit AIST.org.
While digital substations have seen an increase in popularity in the past few years, their adoption has been restricted only to the utilities. This paper explores the finer details of digital substations and provides a comprehensive evaluation of process bus technology for steel mills. A cost-benefit analysis is presented along with lessons learned and future applications. A centralized protection design is introduced and modern protection designs are explored. Safety, cost, scalability and integration into Industry 4.0 systems are evaluated for small-, medium- and large-scale substations.
In the direct reduced iron (DRI) process, reducing gases are generated in a reformer and lose heat to the environment as they are transported to a shaft furnace. To maintain temperature, oxygen and natural gas are injected. In ideal operation, the natural gas combusts with the oxygen, yielding increased thermal energy. However, in existing operating scenarios, carbon formation around the natural gas ports has become a serious issue. In order to better understand this problem, a three-dimensional computational fluid dynamics model was developed to investigate flow phenomena, combustion characteristics and carbon deposition in this region of the DRI process.
The revival of artificial intelligence (AI) promises to offer solutions in particular for complex systems that are difficult to model with classical methods. An overview of AI solutions in ironmaking is provided and their strengths and weaknesses are discussed. Topics such as the applicability for typical problem groups, pre-conditions regarding required data quality and completeness of data sets, reliability, and combination with classical approaches are covered. Further, the deployment and integration of black-box models into control systems and the related stability are discussed.
Q3-Premium was developed on a proprietary Industrial Internet of Things platform to support digital transformation in quality management for high-end metals production. A real-time, plantwide quality control engine ensures automatic product grading and implementation of corrective actions with embedded predictive models for early detection of defects and estimation of mechanical properties. An integrated data analytics platform provides a repository for insight into product life cycle and tools to support root-cause analysis in a collaborative environment. The features of the solution are described together with the results of its implementation in a reference plant for special bar quality steel production.