Abstract
We present a real-time knowledge based [8] supervision support system in the coal washing domain. The Ash Control Model (AshMod) assists operators in maximising clean coal yield while keeping ash (impurity) content within acceptable limits. AshMod assists the operator in plant situation assessment, fault diagnosis, and performance optimisation.In this paper, we describe the optimisation task, which employs a hybrid artificial intelligence and operations research approach. The process is modeled through a set of extended states associated with the entire process and with individual components (circuits) within the plant. The process is continuously monitored to assess the process state, which dynamically influences the planning and scheduling of a sequence of optimisation steps.The supervision support system captures domain knowledge through multiview knowledge models [7] that capture purpose, function, structure, behaviour and heuristics. The supervision support system is currently undergoing online validation at the B&C Coal Washing Plants operated by the Broken Hill Proprietary Limited (BHP) at Port Kembla, Australia [1].KeywordsFault DiagnosisGoal StateOptimisation PlanStatistical Process ControlFeed QualityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
