If you decide to add advanced process control after optimizing loops and control strategies, the next step is to identify process models.
A common mistake is to start an advanced control project without doing any housekeeping. As mentioned, in most APC projects, more than 50% of benefits stem from optimizing basic control loops.
Multivariable models are identified using modern tools by bump testing setpoints or making small changes to setpoints, for example, with pseudo-random binary signals (PRBS) or by predetermined sequences.
A matrix of models is then obtained. If the models are of good quality, then an MPC controller can be designed.
If models cannot be identified using identification techniques, the next step is to check whether or not historical data can be used to model the process using a neural network (handling non-linearities). Usually, neural networks can replace measurements and become a soft sensor.
If models cannot be identified, the next step is to verify whether or not an experienced operator can control this process; if so, fuzzy logic control can be used to mimic this experienced operator. If the identified models are too complex, fuzzy logic control could be the appropriate strategy because the operator, not the process, is modelled .
In conclusion, you have a goldmine of opportunities to optimize your plant. Putting your equipment to work, tuning control loops and optimizing control strategies will improve your operations. It is essential to complete these steps before thinking about adding advanced control.
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