Advanced Control for a Chemical Process
GMC coupled with Dynamic Data Reconciliation
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Process measurements are taken in chemical plants for the purpose of evaluating process control or process performance. However, not all variables needed are generally measured due to technical infeasibility or cost. Furthermore, the measurements are often contaminated in the sense that random noise may be present due to result of miscalibration or failure of the measuring instruments. In this book, the benefits of combining dynamic data reconciliation (DDR) with Generic Model Control (GMC) are demonstrated on two example chemical processes. DDR is used preliminary to reconcile and estimate me...
Process measurements are taken in chemical plants for the purpose of evaluating process control or process performance. However, not all variables needed are generally measured due to technical infeasibility or cost. Furthermore, the measurements are often contaminated in the sense that random noise may be present due to result of miscalibration or failure of the measuring instruments. In this book, the benefits of combining dynamic data reconciliation (DDR) with Generic Model Control (GMC) are demonstrated on two example chemical processes. DDR is used preliminary to reconcile and estimate measured and unmeasured data required for evaluating process control or process performance. In the case studies, the robustness of the proposed control strategy is investigated with respect to changes in process condition, modeling error and disturbance variable for both set point regulation and set point tracking. The results show that a rudimentary treatment of measurement errors and an estimation of unknown quantities have proved extremely effective. Therefore, DDR approach is an important adjunct to advanced control.
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