Water Analytics – Meniscus

New makeover for the original MCE Analytics Engine!

meniscus — Tue, 15 Feb 2022 21:37:27 +0000

New AngularJS webclient revitalises our original MCE Analytics Engine – low-code analytics for developing solutions easily and quickly

We are very pleased to announce that our original MCE analytics engine has had a bit of a makeover! A new AngularJS webclient and updated Javascript widgets have revitalised the analytics engine. It is just as fast, just as flexible and just as easy to use. However, the new AngularJS dashboard makes it much easier to set up, configure and modify Items. The new Javascript widgets also make it easier to create your own dashboards and to display the metrics you need.

Request a demo of MCE

The principles behind MCE are becoming more accepted as more companies appreciate the benefits that self-service access to data and associated analytics within the organisation. MCE has always focused on enabling anyone in to quickly develop a simple application, and then add more complex analytics as they understand both the capabilities of MCE and as the application itself develops. The new New AngularJS webclient now makes it a whole lot easier to use!

Traditionally, most users of MCE are based in the energy or water sectors, using MCE to calculate complex energy tariffs; energy management; pumping efficiency metrics; chemical consumption and dosing; spill monitoring and prediction for water assets; real-time home-energy monitoring to name a few. However, we believe that MCE is a powerful and flexible tool for any organisation that wants to enable its workforce to deliver real-time analyitcs solutions without having to rely on an army of data scientists to create the background analytics.

Some case studies can be found here.

MCE analytics engine infographic

Meniscus Calculation Engine – MCE (low-code analytics engine)

MCE Javascript Widgets – built using MCE AngularJS webclient

More information on Operational Analytics

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MAP Rain model infographic

meniscus — Tue, 30 Nov 2021 13:51:56 +0000

MAP Rain model infographic shows some of the analytics solutions built using the MAP Rain model

MAP Rain is a generic real-time model built using MAP for processing, primarily, radar and forecast rainfall data. The model delivers all the underlying process of fusing the historic, current, Hyperlocal (our own 1-hour forecast) and forecast rainfall data together for any area, resolution, or size of cutout (the area over which we process the rainfall data). Built into MAP Rain are lots of methods to re-sample, aggregate, extract and re-process the rainfall data. These ensure we have the flexibility to apply rainfall data to any point or polygon in the overall cutout area. All these methods are available as API calls.

We use MAP Rain to build a broad range of applications that have rainfall data at their heart. The number of such applications continues to rise and we can deliver all these applications to multiple customers on one instance of MAP.

More information on MAP Rain

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MAP Rain selected as finalist for Wessex Water EDM monitoring Proof of Concept

meniscus — Wed, 22 Apr 2020 07:52:06 +0000

MAP Rain uses Machine learning for EDM monitoring to identify blockages and reduce the number of alerts generated from CSOs during wet weather

Meniscus are really pleased to be selected for the Wessex Water Proof of Concept looking at making smarter use of EDM monitoring data. MAP Rain has been chosen as one of three solutions to be trialed during the 3 month Proof of Concept. This follows a rigorous review of competition entries from 30 companies.

The trials will run for the next three months and will involve Wessex Water feeding these companies near real-time EDM data to see if they can correctly mute alarms and identify blockages, some of which will be simulated in a controlled environment.

Wessex Water Marketplace and objectives for the challenge

Strategic technology planning manager Jody Knight hopes to see a multitude of benefits from this data driven approach. These include reductions in blockages and CSO spills to alleviate impact on the environment, and lower volumes of alarms during rainfall periods, which will allow staff to work more efficiently and improve their reporting efficiency.

“The Marketplace approach has challenged the normal procurement channels for these emerging technical and data-related problems we encounter with our sewer network,” explained Jody.
“We have managed to communicate the problem to a wider supplier base and received proposals from companies that we may not have normally reached.

“The number of potentially viable and different solutions proposed by the companies in this challenge is encouraging to see as we move toward becoming a more data-centric business and we are grateful for all who have taken part in this challenge.”

Wessex Water launched its Marketplace in 2019 as a first-of-its-kind model for the water industry to increase collaboration with companies both in and out of its usual supply chain.

The online platform shares real data for each challenge that is posted and was designed to uncover alternative ways of managing water and waste, outside of the traditional asset-focused approach, leading to a better service and better value for customers.

More information on MAP Rain click here

For more information on the Wessex Water Marketplace click here

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MAP Sewer – creation of simplified sewer network models

meniscus — Fri, 01 Feb 2019 15:25:19 +0000

New MAP Sewer capability speeds up the creation of the simplified sewer network models. This makes is quicker and easier to set up our near real time predictive modelling of the sewer network.

We have been working to speed up the creation of the simplified sewer network models in MAP Sewer so that we can rapidly create new models for new catchments. We have now automated the process of creating the main simplified model, and all the relevant geometries, from the detailed GIS layers that make up the ‘standard’ detailed models used by most water companies.

The objective of this work is:

Generate the MAP Sewer model inputs from the detailed model

To do this in an automated way using a combination of QGIS and PYTHON scripts

The methodology includes:

Derive location of Pumping Stations, Combined Sewer Overflows, Detention tanks, Weirs and Sluices

For each Pumping Station, use QGIS flow trace to identify the upstream conduits

Identify the sub-catchments associated to these upstream conduits

Dissolve the sub-catchments into one large sub-catchment

Aggregate the key sub-catchments properties

Calculate the main trunk sewer path and aggregate sewer length, gradient and diameter

Create the MAP Sewer nodes

The process takes several hours to run and the outputs are:

MAP Sewer configuration files. These are CSV files for each geometry. I.e. Pumping Stations, Combined Sewer Overflows, Detention tanks, Weirs and Sluices

One sub-catchment file containing all the dissolved sub-catchments. this is a KML file

Once this is done then we can add some of the pumping attributes to the Pumping Station and Detention Tank geometry files and then load all the files into MAP Sewer from the dashboard. MAP Sewer then creates the geometries in a few minutes and the whole catchment is calculated in 20 minutes – this includes over 2 years of historic data all at 5 minute periodicity. We can now start to validate the model and to feed it with real time and forecast rainfall data.

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Yorkshire Water leakage hackathon – use of MAP IOT

meniscus — Wed, 06 Jun 2018 14:16:30 +0000

Leakage hackathon and MAP IoT

For their leakage hackathon in May 2018, Yorkshire Water released a year of 15 minute water flow data from some 2,170 District Metering Areas (DMA’s) as well as some information on property types and counts in each DMA. All these DMAs were attributed to one of 20 Operating Areas.

The event was managed by the folks at the ODI Leeds and run over the course of two days. Teams were formed from a range of companies with the task of finding new ways to investigate and analyse the data using a range of Big Data analytics.

Meniscus teamed up with colleagues from the RPS Group, Jumping Rivers and the Ordnance Survey to deliver a working leakage analysis prototype using the Internet of Things (IoT) capability of the Meniscus Analytics Platform(MAP).

Additional information:
Actual leakage dashboard – delivered in 2 days….this is a prototype and required a lot more work but demonstrates the principles
MAP RAIN
MAP SEWER
MAP IoT

How we developed the solution using MAP

Creating a team, coming up with an idea, developing and then delivering this idea within a two day period was always going to be a difficult feat – but with the excellent team that we had by the end of day two we had a working prototype which clearly requires more work but delivered the following key features:

A working dashboard displaying the actual baseline against an estimated baseline consumption for each DMA
Dashboard ranked the DMA’s according to the greatest difference between the calculated and the estimated
The actual baseline was derived between a start and end time set for the parent Operating Area so that the calculated baseline could be recalculated within minutes for one – or for all the DMAs
The estimated baseline was derived for each DMA based upon a derived set of lookups

Total consumption
Baseline consumption
Night time and Day time consumption
Day/Night ratio
Estimate of the leakage based on the difference between the calculated and the estimated baseline consumption

Average consumption
Total consumption
Baseline consumption
Day and Night consumption
Day/Night ratio
Total household and Total Non household demand

Identified DMAs with little to no commercial/industrial properties and DMAs having few residential properties
Used these DMA to define the average profile for a residential and a commercial customer – customer Profiles
Set these Profiles up in MAP so that each DMA could use them as a centralised source of data
Created randomised Voronoi polygon shapes as representations of the DMA areas (the location of the DMA’s was not provided to the teams).
Create a template in MAP for the Operating Area polygons. This includes all variables we want to use and the associated calculations. Variables included the baseline start and end times and the night time start and end times.
Create a template in MAP for the DMA data including the property counts, some key variables and the relevant calculations

Overview page showing leakage. Page should (but didn’t have enough time) rank by estimated leakage

Voronoi polygons used to represent DMAs. Click on the DMA to drill down to data

Estimated (household and non-household) vs Actual baseline demand

MAP Templates

MAP templates allow users to create their own calculations and variables in MAP. Once you have set up your initial template and checked the calculations etc then you can replicate the template to thousands….hundreds of thousands of Things or Entities. Everything is created through the MAP browser based web client.

To replicate the template you create a CSV file including all the variables, the names of your Entities and any properties that you want. Include the full path name of the template you wish to use and then import the CSV using the MAP web client.

MAP imports the Entities and applies them to the template which creates all the data structure and associated calculations. Once the Entities are created its just a matter of adding the raw data, historic or real time, and MAP starts calculating everything in the background.

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Predicting Flows in Sewers to Reduce Energy Costs

meniscus — Tue, 06 Jun 2017 13:14:07 +0000

PREDICTING FLOWS AND ENERGY SAVINGS IN SEWER NETWORKS – HUNDREDS OF CATCHMENTS IN REAL TIME

MAP Rain and MAP Sewer are two real time data analytics solutions that help Water Companies improve quality of service whilst reducing operating costs. MAP Rain lets you integrate historic, current and predicted rainfall analytics into your solutions. MAP Sewer is an add on to MAP Rain to predict flows into pumping stations and sewers.

Our solution helps to:

Improving operation of the Wastewater Network by predicting hydraulic overload
Reducing Future Flood Risk by predicting sewer flooding
Reducing Category 1, 2 and 3 Pollution Incidents by predicting flooding in sewers, pumping stations and CSOs
Providing historic radar rainfall and Environment Agency rain gauge data for any location or catchment
On Demand calculation of rainfall Return Periods (using FEH99 method) for any location
Reducing Energy Costs with real time Demand Management to reduce Distribution Time of Use (DUoS) charges

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Helping Water Companies to improve Outcome Delivery Incentives (ODIs) and reduce energy costs using advanced real time analytics

Meniscus has a comprehensive understanding of the Water Industry and in delivering process based energy, chemical and a range of real time analytics solutions based around integrating real time rainfall data with operational asset information. These case studies demonstrate how MAP is used to predict Bathing Water quality, identifying where CSOs are discharging and sewer levels are high in dry weather conditions, predicting pumping station flows in the sewer network and delivering a range of complex process based energy and chemical monitoring solutions.

Meniscus provides analytics solutions for over 2,000 water and wastewater treatment assets sites.

These solutions process a range of different operational datasets from hours run data, flow, chemical tank level, energy and many more. We are delivering these solutions using our core MCE and MAP analytics platforms. These solutions include a range of visualisations in the form of customised dashboards, widgets and scheduled management reports. Both MCE and MAP have comprehensive RESTful APIs so that developers can readily integrate both platforms directly into their own analytic solutions.

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