Applying mpc in non refinery settings

1. Applying MPC in Non-
Refinery Settings

2. Agenda
• Oil refining
– MPC in oil refining
• MPC in other process industries

3. The Origin of Species

4. Petroleum refining

5. Optimization Hierarchy
Short Term Plan
Advanced Control
Regulatory Control
Operating Objectives, Component
Prices, Constraints
Operating Targets
Controller Setpoints
Valve Positions

6. MPC History
• Oil refining started using linear programming
in 1940’s
• First adaption to nonlinear problems (SLP)
published by Shell in 1950’s
• Use of LP to solve control problems Exxon
1970’s
• DMC (Shell) 1980’s

7. Key features
• Definition of “good” control
• Time domain model
• Rules
• Control is online solution of optimization
problem

8. Input 1 Input 2 Input 3 Input 4
Out 1
Out 2
Out 3
Out 4
Knowledge Base
MPC Model Matrix

9. MPC vs PI
• Discrete PI Control Law
Δ𝐶𝑂 = 𝐾(
𝑒 𝑡
𝑇𝑖
+ (𝑒 𝑡 − 𝑒 𝑡 − 1 )
Control expressed solely in terms of
current and past error

10. MPC Methodology
• Plants Tests
• Model Fitting
• Tuning
• Commissioning

11. Process models
• Process tests to identify the relationship
• Model identified for every input/output
relationship
• Required for future prediction
• Models embedded directly in controller
Input
Output
Model: How does output change to a 1 unit change in input

12. Refinery testing
• We know the answer from first principles,
simulation experience
• Stable feed stock, stable plant
• Common to test multiple inputs
simultaneously
2
2
)(
tA
n
gainStdDev noise


13. Testing in other industries
• Heterogeneous feeds
• Transportation delay
• Harsh environments
• Less instrumentation
• There is not a single “best” model that
converges with longer testing
• Recognize when model is “good enough” to
satisfy the objective

14. Chip Digester
Plug flow reactor with four hour transportation time delay

15. External Delay Handling
U(t) Delay U(t-d) Identification
Commercial time series analysis packages developed to meet the needs of oil
refineries do not handle situations where the time delay varies during the
plant tests
External delay stacks are required to preprocess the inputs

16. Equation transformation
Theory: Q=
𝑉
𝐹
𝑘𝑒 𝑎𝑇+𝑏
Highly nonlinear
But: ln(Q) linear in T and ln(F)
Transforms of both controlled and manipulated
variables often required

17. Performance
• Refining
– Rules of thumb for variance reduction
• 25-40% reduction
• Are these realistic elsewhere?

18. Regulatory Performance
• Y(t) = G(s)u(t)+Noise(t)
• Process model identification focuses on G(s)
• Variance reduction is by undoing or correcting
for the Noise(t)

19. Two noise samples with identical
variance

20. Variance decomposed by frequency
0
5
10
15
20
25
30
35
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Cycles/minute
Power Spectrum
Power Spectrum 1 Power Specttrum 2

21. The noise vs control lineup

22. Poor control prospects

23. Catching the Noise

24. Control Performance
• Feedback at least as important as Predictive
component

25. Robustness
• Can’t control what you can’t measure
• Can’t control what you can’t model

26. Ore Crushing

27. Pellet preparation

28. Iron ore pellet induration

29. 225
245
265
285
305
325
345
V.Low Low Medium High V.High
(kg/p)
Firing intensity
Compressive strength

30. Calculations to infer state
• Unusual Controlled/Constraints
– Variance
– Dominant cycle period
– Continuous plant tests to detect gain reversals
– Logistic regression

31. Logistic Regression
• Regression method to predict the likelihood
of a binary outcome as f(x)
𝑃 =
1
1 + 𝑒− 𝑎+𝑏𝑋
• Similar to classical neuron
• Allows including constraints such as keep
probability of upset event to less than z%

32. Bullet Proofing
• Instrument failures
• Trips
• Blockages
– Safe Parking strategies essential
• Pure “MPC” of successful MPC project may be
less than 40% of total engineering
configuration

33. MPC contrast
• Refining
– Stable systems with deep theoretical knowledge
• Other industries
– Dynamic/variable/frustrating environments
– MPC + “training wheels”

34. Conclusions
• The suitability of technology can’t be
considered in isolation from the environment
that created it
• Refining MPC evolved to address specific
situations and challenges
• Its fitness in other industries depends on
careful adaptations