Abstract: A new start-up operation of a continuous caster is monitored by comparing itself with the normal start-up operation, which is benchmarked by a multivariate statistical model using selected historical operation data. If the new operation is statistically different from the benchmark, then alarms are generated to indicate an impending start cast breakout and at the same time, the process variables that lead to process excursions from the normal operation are identified as the most likely root causes of the predicted breakout. The model is built using Mult-way Principal Component Analysis technology to characterize the operation-to-operation variance in a reduced dimensional space (also known as latent variable space) based on a large number of process trajectories from past normal start-up operations. The process trajectories over the entire start cast duration are predicted based on the current observations.

Abstract: A real-time system and method for online monitoring a transient operation in a continuous casting process. The transient operation refers to, but is not limited to, submerged entry nozzle changes, flying tundish changes, product grade changes, etc. This invention treats the aforementioned transient operations as batch processes and utilizes multiway principal component analysis to develop a multivariate statistical model to characterize normal process transitions based on carefully selected historical process data. Such a model is used by an online monitoring system to determine if a continuous caster transient operation is normal. This monitoring system can further be used to predict an impending breakout, one type of catastrophic process failures which may occur in a continuous casting process, during the transient operation.

Abstract: A new start-up operation of a continuous caster is monitored by comparing itself with the normal start-up operation, which is benchmarked by a multivariate statistical model using selected historical operation data. If the new operation is statistically different from the benchmark, then alarms are generated to indicate an impending start cast breakout and at the same time, the process variables that lead to process excursions from the normal operation are identified as the most likely root causes of the predicted breakout. The model is built using Mult-way Principal Component Analysis technology to characterize the operation-to-operation variance in a reduced dimensional space (also known as latent variable space) based on a large number of process trajectories from past normal start-up operations. The process trajectories over the entire start cast duration are predicted based on the current observations.

Abstract: A method and computer program for determining the amounts of desulphurizing reagents required to reduce the sulphur content in hot metal to meet a specified aim concentration. The determination of the amounts of reagents is based on a multivariate statistical model of the process. This model is initially based on a set of representative data from the process including all process parameters for which data are available. These parameters include chemistry-type variables and variables representing the state of operation of the desulphurization process. The use of a plurality of process and chemistry variables provides a more advantageous determination of the reagent quantities. Also, the method includes an adaptation scheme whereby new data are used to automatically update the predictive model so that the optimality of the model is maintained. Other features of the system include optimal handling of missing data, and data and model validation schemes.

Abstract: A method and computer program for determining the amounts of desulphurizing reagents required to reduce the sulphur content in hot metal to meet a specified aim concentration. The determination of the amounts of reagents is based on a multivariate statistical model of the process. This model is initially based on a set of representative data from the process including all process parameters for which data are available. These parameters include chemistry-type variables and variables representing the state of operation of the desulphurization process. The use of a plurality of process and chemistry variables provides a more advantageous determination of the reagent quantities. Also, the method includes an adaptation scheme whereby new data are used to automatically update the predictive model so that the optimality of the model is maintained. Other features of the system include optimal handling of missing data, and data and model validation schemes.