Meniscus Systems Ltd

Meniscus Systems Ltd

Combined Sewer Level (CSO) Monitoring and Analytics - Case study

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Courtesy of Meniscus Systems Ltd

Our CSO Monitoring solutions combines MAP Rain and machine learning to compare the historic performance of assets with historic rainfall. We then use this analytis to deliver actionable insights that predict the operation of the CSO, or other similar geometries in MAP, to current and forecast rainfall.

Key features

Three different modes of operation

  • Rainfall. Expected operation is driven by radar rainfall data in a configurable area or polygon around the CSO. Uses the Window of Operation concept detailed below.
  • Hydraulic model. Expected operation is driven by a MAP Sewer simplified hydraulic model. This predicts flow in the sewer network and hence in the CSO.
  • Both. Combines both rainfall and hydraulic model.

Range of alerts

  • Dry weather alert. Identifies if the CSO is spilling when there is no significant associated rainfall event.
  • Post storm alert. Identifies if the CSO is spilling for longer than expected once the rain event has finished.
  • Long duration alert. Warns if the CSO has been spilling for longer than a set time i.e. 24 hours.
  • Partial blockage alert. Looks at the rate of change in level in dry weather flow conditions and also compares level with upstream CSO performance, if applicable.

Machine learning—Window of Operation (WoO)

  • Analyses historic spill and rainfall data in an automated process to identify the rain events that cause the CSO to spill. Calculates a range of metrics about each rain event.
  • Output is a set of rain and post storm lags and rain event triggers that are imported into MAP Rain. These lags and triggers are used to predict when the CSO is expected to operate and for how long.
  • Uses a machine learning approach that continually improves the analysis based on the latest spill data.

Easy configuration

  • Configuring and updating CSO information is easy and fast. Everything is configured from the MAP Rain dashboard. Authorised users can also update key properties using the web based MAP Client application.
Examples of metrics calculated in the CSO Geometry

The MAP Rain CSO geometry includes a number of pre-configured calculations that are updated in real-time as new rainfall and CSO data are processed.

Derived Level
Calculates when the CSO is spilling. Uses either the Level in the CSO or the Status of the CSO i.e. High High.

Inferred High Level
Calculates when we predict the CSO may spill based on the results of the machine learning from the Window of Operation analysis.

Average Dry Weather Flow level
Calculates the average Dry Weather Flow level during periods of dry weather only – i.e. excludes any days when the ground is wet

Flow in, Flow out and Spill flow – only calculated if part of a MAP Sewer model
Uses a simplified MAP Sewer hydraulic model to calculate inlet, outley and spill flows

Inferred Blockage
Calculates when we believe there is a blockage or partial blockage in the sewer. Compares the current level in the CSO with level data in the upstream CSO as well as monitoring rates of change of Dry Weather Flow (DWF) in the CSO over a longer period of time

Combined Sewer Level (CSO) Monitoring and Analytics - Case study

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