Ruel based decision support for the process flow
Embedding SIMONE optimisation modules in a Knowledge and rule
based process
Rule based decision support for the process flow
- Contens  Introduction
Process flow of an optimisation 
Knowledge based system 
Rule based system 
Rules for compressor plant configuration
Pressure rules
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Introduction
Transport optimisation is a highly combinatorial Problem
B=N
2
4
6
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
(1)
16
256
4096
(2)
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50
602
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Introduction
- Compressor Plant First level:
Compressor plant
second level:
Compressor station
third level:
Compressor unit
fourth level:
Compressor
Driver
(Cooler)
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
M
M
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Introduction
- Network description Compressor plants without crossings and circles (inline).
Compressor plants with crossings and without circles (tree)
Compressor plant with crossings and circles (mesh)
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Rule based decision support for the process flow
- Process flow of an optimisation  Introduction
 Process flow of an optimisation 
Knowledge based system 
Rule based system 
Rules for compressor plant configuration
Pressure rules
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Process flow of the optimisation
- Overview external data
Loads
1. pre-processing
2. pre-processing
Permutation
SIMONE
Configuration
optimisation
Set-point
optimisation
of variants
1. post-processing
2. post-processing
Results
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Process flow of the optimisation
- Loads Inputs and off takes
Valid for all runs
Data sources:
SCADA System
various planning files
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Process flow of the optimisation
- 1. Pre-processing Read process data from SCADA system
Create a balanced load scenario
Calculate flows at the Compressor plants
Set pressure boundaries
Set storage pressure
Set flow dependant pressure boudaris
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Process flow of the optimisation
- 2. Pre-processing The results of the 1 pre-processing are used as input for the rule
system
The user can further reduce the resulting flow patterns for the
compressor plants
Maximum of 5 flow patterns per compressor palant
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Process flow of the optimisation
- Permutation Permutation of the flow patterns for the compressor plants
derived by the 2. Pre-processing
All derived flow patterns of the compressor plants are
independently combinable with each other
It is not neglectable to reduce the number of flow patterns as
much as possible:
~ 10 plants
~ 5 flow patterns per station
 ~ 510 different scenarios (N = 9.765.625)
 runtime O(15N)  4,64 years (N = 750  3h7m30s)
 runtime O(1N)  113 days (N = 750  12m30s)
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Process flow of the optimisation
- configuration set point optimisation Send data via API to Simone
Run configuration set point optimisation with all Scenarios of the
permutation
Standard machine type has to be configured
Number of available machines has to be configured
Mixed integer and discrete optimisation with SIMONE (CSO)
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Process flow of the optimisation
- 1. Post processingRead data via API from SIMONE
Collect result data of the best results:
Resulting configuration of the compressor stations
Set point
Decision criteria for the selected runs:
Fuel gas consumption
Necessary line pack shifting
Create new variants by manual configuration
Pre-selection of machine combinations with the estimated Power
Select feasible combinations of aggregates
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Process flow of the optimisation
- set point optimisation Send data via API to SIMONE
Set point optimisation with all variants
SPO – Module is used
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Process flow of the optimisation
- 2. Pre-Processing Read data via API from SIMONE
Show best results of the scenarios (variants):
Configuration of the compressor plants
Set points
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Rule based decision support for the process flow
- Rule based System  Introduction
 Process flow of an optimisation 
 Knowledge based system 
 Rule based system 
 Rules for compressor plant configuration
 Pressure rules
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Knowledge based system
- handled data The knowledge based system contains the database
Grid export from Simone
Grid topology
Static data
Scenario parameters and configuration
Simulation results
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Rule based decision support for the process flow
- Rule based System  Introduction
 Process flow of an optimisation 
 Knowledge based system 
 Rule based system 
 Rules for compressor plant configuration
 Pressure rules
Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 21.02.2008
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Rule based system
- overview Rule configuration to reduce the maximum number of possible
flow patterns per Plant
Set of rules for each compressor plant
Dependency on the flow in the Branches of the compressor plants
Declaration of pathes and direct connections
Configuration of rules for pressure bounderies
Dependency of flow on nodes
Normal stations
Bidirectional stations
Storage pressure
Formula for pressure boundary
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Rule based system
- Condition for flow pattern (1. conditions) -
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Rule based system
- Condition for flow pattern (2. flowpattern) -
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Rule based system
- Pressure rules -
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END
Thank‘s for your attention
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