Dale E. Seborg

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An adaptive nonlinear control strategy for a benchscale pH neutralization system is developed and experimentally evaluated. The pH process exhibits severe nonlinear and timevarying behavior and therefore cannot be adequately controlled with a conventional PI controller. The nonlinear controller design is based on a modified input-output linearization(More)
This paper applies multivariate statistical process control (MSPC) techniques to pilot plant fermentation data for the purpose of fault detection and diagnosis. Data from ten batches, nine normal operating conditions (NOC) and one failed, were available. A principal component analysis (PCA) model was constructed from eight NOC batches, while the remaining(More)
A novel process monitoring method is proposed that uses predictions from a dynamic model to predict whether process variables will violate an emergency limit in the future. The predictions are based on a Kalman ®lter and disturbance estimation. A critical feature of the proposed method is the evaluation of a T statistic as a ``reality check'' for deciding(More)
In order for an "artificial pancreas" to become a reality for ambulatory use, a practical closed-loop control strategy must be developed and critically evaluated. In this paper, an improved PID control strategy for blood glucose control is proposed and evaluated in silico using a physiologic model of Hovorka et al. The key features of the proposed control(More)
For many engineering and business problems, it would be ®ery useful to ha®e a general strategy for pattern matching in large databases. For example, the analysis of an abnormal plant condition would benefit if pre®ious occurrences of the abnormal condition could be located in the historical data. A new pattern-matching strategy is proposed for multi®ariate(More)
AbstrPct--The design and implementation of a new adaptive nonlinear predictive controller is presented using a general nonlinear model and variable transformations. The resulting controller is similar in form to standard linear model predictive controllers and can be tuned analogously. Alternatively, the controller can be tuned using a single parameter. The(More)
A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is(More)
BACKGROUND A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able(More)