Ruel based decision support for the process flow Embedding SIMONE optimisation modules in a Knowledge and rule based process

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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 / Introduction Transport optimisation is a highly combinatorial Problem

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Introduction - Compressor Plant - First level: Compressor plant second level: Compressor station third level: Compressor unit M M fourth level: Compressor Driver (Cooler)

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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)

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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 / Process flow of the optimisation - Overview - SIMONE external data Configuration optimisation Permutation 1. pre-processingLoads 1. post-processing Set-point optimisation of variants 2. post-processing Results 2. pre-processing

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Inputs and off takes Valid for all runs Data sources: SCADA System various planning files Process flow of the optimisation - Loads -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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 Process flow of the optimisation - 1. Pre-processing -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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 Process flow of the optimisation - 2. Pre-processing -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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  ~ 5 10 different scenarios (N = )  runtime O(15N)  4,64 years (N = 750  3h7m30s)  runtime O(1N)  113 days (N = 750  12m30s) Process flow of the optimisation - Permutation -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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) Process flow of the optimisation - configuration set point optimisation -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Read 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 Process flow of the optimisation - 1. Post processing-

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Send data via API to SIMONE Set point optimisation with all variants SPO – Module is used Process flow of the optimisation - set point optimisation -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Read data via API from SIMONE Show best results of the scenarios (variants): Configuration of the compressor plants Set points Process flow of the optimisation - 2. Pre-Processing -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / 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 / 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 / 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 / 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

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Rule based system - Condition for flow pattern (1. conditions) -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Rule based system - Condition for flow pattern (2. flowpattern) -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / Rule based system - Pressure rules -

Simone-Optimierung / WTKG Dirk Lieser, Mike Störmer/ GTD / END Thank‘s for your attention