AIChE Symposium Series, Volume 93, Issue 316American Institute of Chemical Engineers, 1997 - Chemical engineering |
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Page 282
... step - ahead prediction for all three networks compared to Set 1 , which is a portion of the training data set , and Table 1 1 summarizes some information about each model . All the networks provided approximately the same accuracy over ...
... step - ahead prediction for all three networks compared to Set 1 , which is a portion of the training data set , and Table 1 1 summarizes some information about each model . All the networks provided approximately the same accuracy over ...
Page 348
... Step 2. Tune the control system for setpoint step changes . The tuning algorithm follows later in this section and yields a vector of filter time constants [ ε ,, 2 , ... , ε „ " ] for which the performance criteria on all setpoint ...
... Step 2. Tune the control system for setpoint step changes . The tuning algorithm follows later in this section and yields a vector of filter time constants [ ε ,, 2 , ... , ε „ " ] for which the performance criteria on all setpoint ...
Page 349
... Step 4. Tuning 1. Perform a SISO IMC tuning procedure for the i - th diagonal transfer function to satisfy its specified Mp by adjusting & This step yields new lower bound for & , and other filter time constants as steps 3 and 4 are ...
... Step 4. Tuning 1. Perform a SISO IMC tuning procedure for the i - th diagonal transfer function to satisfy its specified Mp by adjusting & This step yields new lower bound for & , and other filter time constants as steps 3 and 4 are ...
Contents
Industry Assessment | 1 |
Predictive Control | 5 |
Adaptive and Nonlinear Control Fact or Fantasy? | 9 |
Copyright | |
22 other sections not shown
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adaptive control AIChE algorithm analysis analytical applications approach auto-correlation batch behavior changes Chem Chemical Engineering chemical process chip closed-loop constraints continuous control problems control system controller design defined developed discrete disturbance dynamic equations estimation example feedback control Figure finite Grafcet horizon hybrid systems identification IEEE implementation input linear control loop manufacturing matrix measured methods model predictive control monitoring Morari multivariable neural networks nonlinear control nonlinear model nonlinear systems on-line operating optimal control output error paper machine parameters Petri net PID controllers plant Proc process control process model programming Pulp and Paper quadratic quadratic program reactor robust robust control scheduling sensors sequence set point significant simulation snack food solution specifications stability step structure synthesis TAPPI Press techniques temperature theory tion trajectory transfer function trend trol uncertainty valve variables variance vector wafer