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Outline. Terminology Typical Expert System Typical Decision Support System Techniques Taken From Management Science and Artificial Intelligence Overall Project Project Evolution -- Synthesis of Techniques System Diagram Ultimate Goal for System Outputs. Outline (continued).

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Outline
Outline

  • Terminology

    • Typical Expert System

    • Typical Decision Support System

    • Techniques Taken From Management Science and Artificial Intelligence

  • Overall Project

    • Project Evolution -- Synthesis of Techniques

    • System Diagram

    • Ultimate Goal for System Outputs


Outline continued
Outline (continued)

  • Plan for First Prototype of Automated System

    • Immediate Objective for Prototype Automated System

    • System Flow Diagram

    • A Possible Starting Point

      • Basis for Storage Injection/Withdrawal Model Computation

      • Monthly Plan for Supply Selection

      • Rules for Monthly Injection Computation

      • Rules for Monthly Supply Selection

      • Rules for Monthly Withdrawal Computation

    • Discussion Agenda -- Input from Team of Experts

  • Next Steps


Typical expert system
Typical Expert System

  • Accumulates knowledge, including tricks

  • Codifies expert knowledge, often in the form of rules

  • Makes the expertise available, even when the expert is not, by emulating the decision-making ability of the human expert

  • Performs (at best) as well as the human expert that it emulates, but cannot go beyond the knowledge that was gathered


Typical decision support system
Typical Decision Support System

  • Assists managers in their decision processes about semi-structured tasks

  • Supports managerial judgment by providing a smorgasbord of analytical tools and models

  • Seeks to improve the effectiveness of decision-making and to generate better solutions than are currently in use

  • Helps managers respond to novel or unanticipated situations


Techniques taken from management science and artificial intelligence
Techniques Taken from Management Science and Artificial Intelligence

  • Linear Programming (LP)

  • Heuristic Search

  • Pattern Recognition

  • Machine Learning


As our project evolves we find that it needs a synthesis of techniques
As our project evolves, we find that it needs a synthesis of techniques

  • Gathers knowledge from multiple experts

  • Uses rules to simulate the decisions made in managing gas sources for the pipeline

  • Tries more possible solutions than are possible to evaluate by hand

    • Not an exhaustive search

    • Guided by heuristics from human experts

  • Uses machine learning techniques to try to improve the rules


Knowledge based application development project
Knowledge-Based Application Development Project techniques

UNCONTROLLED EVENTS

SystemFailures

IndustrialDemand

Weather

- Supply- Pipeline- Burnertip- Interconnect

GAS SOURCES

OBJECTIVE

Storage

Well & Pipeline Supply

Transportation Imbalance

Demand Curtailment

High Reliability of Service

Lower Gas Cost

Contracts - Take-or-Pay - Recoup - Tests for Deliverability

Regulations - Ratable

Physical System Capacity - Maximum Limits (e.g., MAOP, Well Deliverability, Injection, Withdrawal) - Minimum Required - Transients

CONSTRAINTS


Ultimate goal for system outputs
Ultimate Goal for System Outputs techniques

  • Create monthly plans for selection of gas supply that will minimize WACOG, while maintaining a high reliability of service and meeting contractual and regulatory requirements

    • Consider the yearly cycle when developing the monthly plans

  • Support replanning on a real-time basis in response to changing circumstances during the month


Immediate objective for prototype automated system
Immediate Objective for Prototype Automated System techniques

  • Create monthly* plans for selection of gas supply that will result in a lower WACOG* Generate plans for winter heatingseason only

  • Use rules that assure meeting contractual and regulatory requirements

  • Present information that allows managers to appraise the level of risk associated with each plan


First prototype of automated system system flow diagram
First Prototype of Automated System - techniquesSystem Flow Diagram

WACOGModel

Multiple

Budgeted Demand

Monthly Plans

Scenarios

Rule-BasedExpertSystem

WACOG/RiskProfile

Weather-Driven

WeatherProfiles

Demand


Basis for storage injection withdrawal model computation a starting point
Basis for Storage Injection/Withdrawal Model Computation - A Starting Point

  • Weather profile for each calendar month

  • Need to add electric generation usage later

  • Sample table from academic paper

    • Shows only December

    • Based only on estimates, not analysis

    • Consider this a starting point


Basis for storage injection withdrawal model computation a starting point1
Basis for Storage Injection/Withdrawal Model Computation - A Starting Point

* Sample Taken From Academic Paper


Rules for monthly supply selection a starting point
Rules for Monthly Supply Selection - Starting PointA Starting Point


Agenda
Agenda Starting Point

  • Consider the architecture of the proposed model

    • Granularity of models

  • Discuss temperature thresholds

  • Discuss translations of weather profiles to Bcf of gas

    • Incremental demand by residential and commercial customers

    • Storage injection and withdrawal


Next steps
Next Steps Starting Point

  • Further analysis of weather data

  • Research historical transportation imbalances and use of storages

  • Implement a very simple version of this system in CLIPS

  • Compare the Possible Starting Point method to the current Operating Guidelines


Decision support model for gas expert system project
Decision Support Model for Gas Expert System Project Starting Point

Deliverability Max. & Contractual Min.

Actual GasSupply

PredictedResponse ofSystem

PreliminaryCalculation

Model

Actual Weather

ManualComparisonto Actual

Rules&Contestants

Validation


Decision Support Model for Gas Expert System Project Starting Point

PredictedResponse ofSystem

Deliverability Max. & Contractual Min.

Gas SupplyForecast

EvaluationFunctionor “Critic”

PreliminaryCalculation

Model

Weather

Scenarios

WeatherHistory

Risk Factors &Distribution ofProbable WACOG

GenerateScenarios(Monte CarloMethod)

Rules&Contestants

Current Implementation


Decision Support Model for Gas Expert System Project Starting Point

PredictedResponse ofSystem

Deliverability Max. & Contractual Min.

Gas SupplyForecast

EvaluationFunctionor “Critic”

PreliminaryCalculation

Model

Weather

Rules &Contestants

Scenarios

WACOG& RiskFactors

WeatherHistory

GenerateScenarios

ModifyRules

Partially Automate the Search for BetterRules by Using A.I. Techniques


Gasxpert system design overview
GasXpert System Design Overview Starting Point

Genetic Programming

  • Selection

  • Crossover

  • Mutation

  • Create NewGasXpertPlans asCLIPS Rules

  • Fitness

  • Evaluate Perfor-mance ofAll Plansin Popu-lationAcrossAllScenarios

Create Weather and Demand Scenarios

Expert System Control, Constraint,and Input/Output Rules

GasXpert Plan

(Supply contracts and storage capacitiesare considered fixed in this model)

EvaluatePerformance ofGiven Plan Across AllScenarios

EvaluatePerformance ofPlan on GivenScenario


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