Difference between revisions of "Membrane Autopsy Techniques"

From Desal Wiki
 
Line 1: Line 1:
 +
==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 03:14, 4 September 2014

Purpose

  • OPTAGON Model

  • 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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