KPI prediction, which is categorized under time series data modeling, serves as a crucial area of investigation within the realm of complex industrial processes. This field focuses on forecasting key performance indicators that are pivotal for assessing the operational efficiency and productivity of industries. By leveraging historical data trends, KPI prediction aids in optimizing process controls and decision-making strategies, thus enhancing overall performance and competitive edge.
This dataset is used to model the zinc concentrate grade monitoring, which includes features of froth video in the last cleaning cell.
There are three datasets, which include the labeled grade by the XRF analyzer and its related visual features and feed grades.
It can be used for training and validation the performance monitoring model in the froth flotation.
There are two datasets in the file.
The dataset 1 contains 228 image pairs which are labelled by experienced people with the true burst bubble.
The dataset 2 contains 335 image pairs which are processed by the local motion correction. They are used for measuring the similarity.