No abstract

Comprehensive transient models (CTMs) are not readily available for complex industrial processes. In contrast, fundamentals-based process models (FbPMs) often are readily available and data-driven models (DDMs) can be readily developed. Generally, FbPMs have enough accuracy and safety margin to size equipment for steady-state operations but in contrast to CTMs, are not accurate enough to predict the unique operational responses required for applications, such as the definition of system functional failures in predictive maintenance (PdM). However, in the absence of more accurate models, FbPMs may be valid to indicate response trends or determine operational windows, with respect to safety and functionality. The case study is a Raw Material Preparation Plant, used to screen, grind and dry coal for an iron-making process. Following DDM construction through supervised machine learning from operational data, the validity of an available FbPM against operations is investigated through: (1) comparison of FbPM and DDM regression responses (2) consideration of physical phenomena and (3) comparison of sensitivity analysis results. Following validation, the definition and detection of functional failures in the plant as obtained from the FbPM will be used as the first step towards system PdM.