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Process auditing using historical data can identify causes for poor performance and reveal opportunities to improve process operation. To date, the data used has been limited to process measurements; however other sources hold complementary information about the process behavior. This paper proposes a new approach to root-cause diagnosis, which also takes advantage of the information in utility, mechanical and electrical data, alarms and diagrams. Its benefit is demonstrated in an industrial case study, by tackling an important challenge in root-cause analysis: large-scale systems. This paper also defines specifications for a semi-automated tool to implement the proposed approach.

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In this paper we suggest a new method for controlling the balling drums used in the iron ore industry. We suggest that a cluster of drums are controlled collectively rather than individually. Further, we investigate the possibility of using an extended Kalman filter for estimating the amplitude and frequency of the oscillations in such drums. The filters thresholding point is identified, and the area for which the filter is usable is given. Finally a simulation of the proposed control scheme, based on a simple nonlinear model, is presented

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