Do you Need Advanced Control?

5 August, 2019 | Blog

MICHEL RUEL, P.Eng.

Expert, Process Control

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Advanced process control (APC) improves performance, stabilizes production, handles constraints, protects equipment and manages grade changes. At first glance, this solution might seem interesting to businesses. However, before adding an APC to an existing system, it would be wise to take a step back and assess the situation.

For example, studies show that you can increase production by 1.5% by optimizing equipment use and basic control loops; however, advanced process control can increase production by 1.4%, for a total of around 3%. Of course, the first 1.5% is much easier to obtain and requires little investment.

The simplest solutions are always the best: why make things more complicated if performance (production, quality, etc.) does not justify it? Profitability analyses should be conducted to validate project viability. Needs—not trends—should dictate choices!

We have prepared a few questions that will help you start your planning, especially if you are thinking about implementing optimization solutions in the near future.

Steps

The first step before deciding to incorporate an APC is determining whether you need more than just PID control.

Ask the following questions:

  • Is performance adequate?
    • Discuss and prioritize performance expectations.
  • Have the loops been optimized?
    • Optimize all loops.
    • Verify all equipment.
    • Review all control strategies (cascade, constraints, anticipation, etc.).
  • Do the control systems handle disturbances?
  • Do the control loops interact?
  • Does an operator perform better than the control system?

APC can be divided into two categories: model-based and rule-based.

Model-based control is essentially advanced regulatory control and model predictive control, while rule-based control is mainly fuzzy logic control. There are also other techniques, such as genetic algorithms and neural networks.

Model-based approaches are usually the first choice. Rule-based systems are considered when models cannot be identified, or if models vary widely. In rule-based approaches, the best operator’s knowledge is used to manipulate setpoints on control loops. In other words, the fuzzy logic controller models the best operator.

Table 1 compares characteristics for each approach. Development and optimization for model-based approaches are well known, but rule-based control is more complex and includes many methods. On the other hand, fuzzy logic control is easy to maintain and process changes are quickly implemented.

Table 1  Comparison of Advanced Control System Approaches

If you decide to add advanced process control after optimizing loops and control strategies, the next step is to identify process models.

Modelling

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 .

Conclusion

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.

This content is for general information purposes only. All rights reserved ©BBA

References

Brisk, M. L. (2004). Process control: Potential benefits and wasted opportunities. 5th Asian Control Conference, Melbourne, Australia, Vol. 1, pp. 20–23.

Brittain, H., Dewey, D., & Ruel, M. (2009, October). Closed loop tuning: Methodology, tools, benefits and common mistakes. ISA, 2009 Conference, Houston, TX.

Brittain, H. & Ruel, M. (2008, October). Optimize your process using normal operation data. NPRA Conference, Houston, TX

Liptak, B.G. (Editor-in-chief) (2006). Instrument engineers’ handbook, Process control and optimization, (4th Edition, Boca Raton, FL: CRC Press,), Ruel, M., et al. Chapters 2.20 Optimizing control; 2.35 Tuning PID controllers; 2.38 Tuning by computer; Ruel, M., Chapter 2.37 Tuning interacting loops, Synchronizing loops

McNabb, C. & Ruel, M. (2009, February). Best practices for managing control loop performances – Roadmap to Success. Paptac Conferences, Montréal, QC.

Patwardhan, R.S. & Ruel, M. (2008, November). Best practices for monitoring your PID loops – The Key to Optimizing Control Assets. ISA EXPO, Houston, TX.

Ruel, M. & Tremblay, E. (2008, November). Managing assets with modern tools at White Birch Paper. ISA EXPO, Houston, TX.

Ruel, M. (1999, March, April, May). Loop optimization: Before you tune – Troubleshooting – How to tune a loop, Control Magazine

Ruel, M. (2010, November). Control System Performance Assessment – Best Practices, Automining, Santiago, Chile.

Ruel, M. (1995, 2006). Fundamentals of process control, (English, Spanish, Portuguese, French) second Edition, 269 p.

Ruel, M. (2000, October). Control valve performance, Chemical Engineering, pp. 64–67.

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