1. Neural Network Lab
Develop a Neural Network to simulate the temperature exiting a heat exchanger:
We will use a simulated heat exchanger in DeltaV, EX2_SIM

2. Heat Exchanger
We will configure the network to determine the tempered water exit temperature based in the inlet temperatures and flow rates

3. Inputs
Output, what we want to see from
The Neural Network

4. Heat Exchanger

5. Heat Exchanger Calculations
The amount of heat transferred is based on the heat transfer coefficient, U and h

6. Heat Exchange Calculations
Heat transfer coefficient non linear function depending on viscosity, thermo conductivity mass flow rate

7. Inputs Select TT2-1, TT2-2, FC2-1 and FC2-2
Use a 1 second Historical Sampling Rate for this example

8. Under Advanced Control
Lab Entry Block
NNet Block under Advanced
Control Tab

9. Right click on NN block
Select Extendable Parameters to change
Number of inputs

10. Select 4 inputs

11. Select the sampling rate,
1 sec in our case

12. Browse for input parameter, Any floating point
variable can be selected

13. Select the CV or current value

14. Enter the identifier to articulate the point
In the Neural Network toolkit

15. Wire TT2-4 to the
NN sample input

16. NN block OUT_SCALE should match the
Range of the output

17. Wire to NN_TEMP
So we can trend the point

18. Save the case and
Download the network

19. NN block reference inputs are available through the
Historian, but the Historian must be Enabled,
Assigned to the Area and Node

20. NN block reference inputs are available through the
Historian, but the Historian must be enabled,
Assigned to the Area and Node, then Downloaded!

21. DeltaV Neural
Now that we have built the DeltaV Neural Network, we need to launch ( start up ) the application, either from the Control Studio or from the Explorer

22. Right Click the block and select
Advanced Control > Neural

23. Start
> DeltaV
>Advanced Control
>Neural

24. DeltaV Neural Block
Reference Inputs and
Sample

25. Right click to set scale

26. Green shaded area contains the
Training set, right click to select
Red area for excluded area

27. DeltaV Neural We need to configure the sample multiplier.
This entry should allow the
Time to Steady State to be that which you estimate the process to get to steady state after a disturbance

28. (1/50) * Time to Steady State
DeltaV Neural
In DeltaV, each input is actually 50 sampled inputs delayed by
(1/50) * Time to Steady State
This way the model will sense not only the final value but the approach to steady state

29. In our case, we estimate 5 minutes
to get to steady state, multiplier set to 6

30. Note: You cannot have
Control Studio running
when training!
Press Autogenerate to start the training process …

32. Note the sensitivities and if
the network uses the input!
We have to download the network to get it to work!

33. You can delete other training results, but you cannot
delete the current running result.

34. DeltaV Neural – Display Editing
We need to add a faceplate to the operators display. Open the display and do a quick edit. Locate the NN pre configured block and drag it on the display
Browse to select the Neural Network

37. Faceplate

38. Training and test errors are shown

39. Expert mode allows
User to select data screening range

40. Show inputs
And delays,
Click here
Sensitivity and input selection
Shown. Summ of all inputs = 1.0

41. Delays are shown

42. Detail,
Can shift in expert mode

43. Verification Selectors

46. Bad points here

47. Excluded Area