Qualitative Modeling and Machine Learning Applied to the Real-Time Estimation of Chemical Process Dynamics

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The control of dynamically changing processes, for example those operated in a batch or semi-batch mode, are complex because the mathematical models used to implement the control strategies do not have analytical solutions. Numerical solutions such as recursive least-squares (RLS) estimation are sometimes used, but these are not easy to implement for chemical processing systems which may be high order, time-varying, non-linear or all three. Modifying RLS incorporating specific system characteristics has been used successfully resulting in solutions that have limited applicability. In this work, the PI plans to generate automatic control algorithms using RLS that are based on classifying disturbances into one of three categories and planning control action accordingly. The resulting method is expected to have general applicability. There are three basic types of disturbances: 1) Slow drift in system state - for such systems there is an acceptable band of operating conditions which will permit the use of linearized models, but the process will ultimately move out of this band (real examples are caused by catalyst decay, fouling and slagging, etc.). 2) Short periods of steady state operation - this causes linear dynamic models to be inoperable. 3) Sudden change in setpoint - immediately following such disturbances linear models will be grossly inaccurate (real examples occur when feed lots are changed, for example). The PI plans to design his algorithm so that it will decompose all disturbances into combinations of one or more of these. The method then involves continuous diagnosis of process behavior to update control action.