Imaging Process Control for Mining and Cominution of Construction Materials
Extraterrestrial mining activities will at first be concerned with the production of construction materials. Rock and earth materials can provide the strongest and most cost effective solutions. Scarce resources (energy, human intervention) in extraterrestrial mining operations, will limit many types of activities. At the same time, construction materials need to be processed, including blasting, crushing, screening, and milling, to exact specifications. Automation is the key to minimizing resource utilization, and optical sizing is crucial for these tasks, enabling automated process control without requiring excessive human quality control strategies.
Toward the improvement of primary grinding productivity and energy consumption efficiency. Part 1: Investigation of the feed ore size effects
Abstract This paper summarizes part of the research into the study of the full and semi-autogenous grinding mills toward the improvement of productivity and energy consumption. While the feed ore size effect on SAG/FAG mill performance is the main subject of this paper, the 2D imaging vision system for feed ore size analyses is described. The ability of the system to produce on-line reliable results is illustrated. Particular attention is paid to the development and extraction of valuable size indicators with regards to the grinding mill mechanisms and energy consumption aspect. Finally, the investigation of the feed size effects at three grinding mills is discussed while extensive data are intensively processed in order to extract significant correlation between the primary grinding performance and some developed size indicators.
State estimation for integrated vehicle dynamics control
This paper discusses a vehicle controller and a state estimator that was implemented and tested in a vehicle equipped with a combined braking and chassis control system to improve handling. The vehicle dynamics controller consists of a feed forward body roll compensation and a feedback stability controller. The estimator is based on a non-linear vehicle model combined with an extended Kalman filter. The Kalman filter is also connected to an algorithm, which estimates the tyre-road friction coefficient. The developed estimator was tested in a vehicle. Good results were achieved for normal and extreme driving manoeuvers.