Membrane Autopsy Techniques: Difference between revisions

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==Purpose==
<div class="align-right">
<gallery widths=225px heights=225px>
File:Optagon model.png|OPTAGON Model
File:Asset resilience.png|Asset Resilience
</gallery>
</div>
*Resilience is a system’s ability to maintain routine function even under unexpected circumstances.It is an essential factor in ensuring continuous process throughput whilst remaining compliant with strict water discharge guidelines.
*Resilience modelling tools have been widely used in the Petrochemical, Oil and Gas, and Aviation industries to model process reliability and safety over the last 15 years.
*No standard resilience modelling method has been developed for a potable reuse scheme.
*Study used a resilience modelling tool from the Oil and Gas industry, GL Noble Denton’s (GLND) OPTAGON Simulation Package
*OPTAGON is GLND’s Monte Carlo-based Reliability, Availability and Maintainability (RAM) simulation tool which is capable of modelling the performance of asset.
*With user-variated real-time data, OPTAGON is able to accurately predict equipment failure and system resilience.
==Objective==
*Develop a mechanical resilience model for dual membrane plants (MF/UF + RO) using data from large scale (>10 MLD) plants with long operating history (7-10 yrs)
*Develop “What-if” scenarios for resilience model’s sensitivity based on confirmed cases of drinking water plant failure resulting in pathogen infection
*Quantify process resilience and predict process equipment failure using resilience model.
==Common Failures in Drinking Water Systems==
*GIDEON database catalogued >2000 confirmed pathogenic outbreaks from 2003 to 2013.
*30% of the outbreaks were associated with protozoan parasites.
*The most common type of failure was an incident in the catchment area in conjunction with an inadequate process design.
*Second most common type of failure occurred in the distribution system followed by an inadequate management framework and operational error.
*Poor asset management and failures highlight the need and importance of resilience modelling in the water industry.
[[File:Gideon map.png|thumb|600px|left]]
<html xmlns="http://www.w3.org/1999/xhtml">
  <head>
    <meta http-equiv="content-type" content="text/html; charset=utf-8"/>
    <script type="text/javascript" src="//www.google.com/jsapi"></script>
    <script type="text/javascript">
      google.load('visualization', '1', {packages: ['corechart']});
    </script>
    <script type="text/javascript">
      function drawVisualization() {
        // Create and populate the data table.
        var data1 = google.visualization.arrayToDataTable([
          ['Pathogen', 'Outbreak (%)'],
          ['Bacterial', 23],
          ['Protozoan', 30],
          ['Viral', 26],
          ['Unknown', 9],
          ['Mixed Aetiology', 12]
        ]);
        var data2 = google.visualization.arrayToDataTable([
          ['Causes', 'Failure (%)'],
          ['Management', 9],
          ['Breakage', 17],
          ['Design', 60],
          ['Monitoring/Maintenance', 6],
          ['Operational/Human Error', 8]
        ]);
     
        // Create and draw the visualization.
        new google.visualization.PieChart(document.getElementById('visualization1')).
            draw(data1, {title:"Pathogen Outbreaks"});
        new google.visualization.PieChart(document.getElementById('visualization2')).
            draw(data2, {title:"Causes of Failure"});
      }
     
      google.setOnLoadCallback(drawVisualization);
    </script>
    <style>
      .chart-container {
        width: 100%;
        margin: 0 auto;
        text-align: center;
      }
      .pie-chart-container {
        width: 372px;
        margin: 0 auto;
        text-align: center;
        display: -moz-inline-stack;
        display: inline-block;
        zoom: 1;
        *display: inline;
      }
    </style>
  </head>
  <body style="font-family: Arial;border: 0 none;">
    <div class="chart-container">
      <div class="pie-chart-container">
        <div id="visualization1" style="width: 372px; height: 224px;"></div>
      </div>
      <div class="pie-chart-container">
        <div id="visualization2" style="width: 372px; height: 224px;"></div>
      </div>
    </div>
  </body>
</html>
<p>
==Supporting Evidence==
==Modelling Process==
===Data Sourcing and Collection===
*Equipment failure and performance data is sourced from 7 water recycling plants worldwide.
*Relevant information is collected from a wide array of data sources.
<!-- Map -> Table -->
===Data Analysis and Mapping===
*Cataloged equipment data is sorted and mapped according to process equipment specified in the model (Reference Plant).
*Equipment arranged with design and operational capacities based on functional location.
*Operation & Maintenance (O&M) Manuals provide vital information on equipment availability.
*MTBF and MTTR are also calculated if not previously provided.
*Equipment criticality is determined based on failure and maintenance data.
<center>
<gallery widths=400px heights=300px>
File:Equipment table spreadsheet.png
File:ReferencePlant-AWRCoE-ProcessDesignDiagram.png
</gallery>
</center>
===Resilience Modelling and Sensitivity Analysis===
*Mapped data becomes input variables for OPTAGON to model asset’s mechanical resilience.
*Monte Carlo simulations of 10,000 realisations ensure confidence of modelling results.
*Results also demonstrate equipment interdependency.
*Modelling results would quantify the asset’s overall reliability and resilience.
*Sensitivity analysis would further highlight which input variable would have the greatest impact on the system.
*“What-if” scenarios would test the robustness of the reference plant and aid with process optimisation. 
<center>
'''Resilience = &fnof; (Availability, Performance)'''<br>
'''Availability = &fnof; (Reliability, Maintainability)'''<br>
'''Risk =  &fnof; (Likelihood, Consequence)'''
</center>
===Outputs===
*OPTAGON can model complex water recycling systems with high level of accuracy and consistency.
*Modelling results would be able to quantify asset resilience, criticality and risk.
*Resilience modelling can predict and improve asset performance throughout asset’s lifespan.
*Sensitivity analysis would support asset management decisions and aid in efficiency and profitability.
*Reference model can also be used to provide insight to specific failure modes and resulting effects.
<center>
<gallery widths=400px heights=300px>
File:Resilience modelling monte carlo.png
File:Sensitivity analysis.png
</gallery>
</center>
[[Category:Training Material for Operators]]
[[Category:Training Material for Operators]]
[[Category:Resources]]
[[Category:Resources]]

Revision as of 20:14, 4 September 2014

Purpose

  • OPTAGON Model

    OPTAGON Model

  • Asset Resilience

    Asset Resilience

  • Resilience is a system’s ability to maintain routine function even under unexpected circumstances.It is an essential factor in ensuring continuous process throughput whilst remaining compliant with strict water discharge guidelines.
  • Resilience modelling tools have been widely used in the Petrochemical, Oil and Gas, and Aviation industries to model process reliability and safety over the last 15 years.
  • No standard resilience modelling method has been developed for a potable reuse scheme.
  • Study used a resilience modelling tool from the Oil and Gas industry, GL Noble Denton’s (GLND) OPTAGON Simulation Package
  • OPTAGON is GLND’s Monte Carlo-based Reliability, Availability and Maintainability (RAM) simulation tool which is capable of modelling the performance of asset.
  • With user-variated real-time data, OPTAGON is able to accurately predict equipment failure and system resilience.

Objective

  • Develop a mechanical resilience model for dual membrane plants (MF/UF + RO) using data from large scale (>10 MLD) plants with long operating history (7-10 yrs)
  • Develop “What-if” scenarios for resilience model’s sensitivity based on confirmed cases of drinking water plant failure resulting in pathogen infection
  • Quantify process resilience and predict process equipment failure using resilience model.

Common Failures in Drinking Water Systems

  • GIDEON database catalogued >2000 confirmed pathogenic outbreaks from 2003 to 2013.
  • 30% of the outbreaks were associated with protozoan parasites.
  • The most common type of failure was an incident in the catchment area in conjunction with an inadequate process design.
  • Second most common type of failure occurred in the distribution system followed by an inadequate management framework and operational error.
  • Poor asset management and failures highlight the need and importance of resilience modelling in the water industry.
Gideon map.png

Supporting Evidence

Modelling Process

Data Sourcing and Collection

  • Equipment failure and performance data is sourced from 7 water recycling plants worldwide.
  • Relevant information is collected from a wide array of data sources.


Data Analysis and Mapping

  • Cataloged equipment data is sorted and mapped according to process equipment specified in the model (Reference Plant).
  • Equipment arranged with design and operational capacities based on functional location.
  • Operation & Maintenance (O&M) Manuals provide vital information on equipment availability.
  • MTBF and MTTR are also calculated if not previously provided.
  • Equipment criticality is determined based on failure and maintenance data.
  • Equipment table spreadsheet.png
  • ReferencePlant-AWRCoE-ProcessDesignDiagram.png


Resilience Modelling and Sensitivity Analysis

  • Mapped data becomes input variables for OPTAGON to model asset’s mechanical resilience.
  • Monte Carlo simulations of 10,000 realisations ensure confidence of modelling results.
  • Results also demonstrate equipment interdependency.
  • Modelling results would quantify the asset’s overall reliability and resilience.
  • Sensitivity analysis would further highlight which input variable would have the greatest impact on the system.
  • “What-if” scenarios would test the robustness of the reference plant and aid with process optimisation.

Resilience = ƒ (Availability, Performance)
Availability = ƒ (Reliability, Maintainability)
Risk = ƒ (Likelihood, Consequence)

Outputs

  • OPTAGON can model complex water recycling systems with high level of accuracy and consistency.
  • Modelling results would be able to quantify asset resilience, criticality and risk.
  • Resilience modelling can predict and improve asset performance throughout asset’s lifespan.
  • Sensitivity analysis would support asset management decisions and aid in efficiency and profitability.
  • Reference model can also be used to provide insight to specific failure modes and resulting effects.
  • Resilience modelling monte carlo.png
  • Sensitivity analysis.png
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