This White Paper sets out a methodology for using the Meniscus Calculation Engine (MCE) as a bottom up process centric Energy Monitoring and Targeting based application for use in the Water Industry.MCE is a generic application and so can be used for the monitoring of any type of numeric based data and for setting up set of calculations required to understand the performance of assets.
Many Energy Monitoring and Targeting applications are top down driven, i.e. from the fiscal electricity meter installed for the Site and then down, possibly one layer, to any sub meters installed on site. This is because these applications require the use of metered electricity data.Unfortunately the information gathered by such monitoring is limited since sub meters are expensive to install and, in the UK, few sites have comprehensive sub metering at process and sub process levels.Using MCE, the customer can adopt a bottom up approach by maximising the value from existing data that is frequently available from site, namely hours run data for the fixed speed pumps and drives and converting this into consumption data using the rated kW power demand for each drive. By using this very granular data (often available at 15 minute periods) it is possible to build up a very good approximation of energy use at sub process and then process level and to be able to set and apply process centric targets. This process level energy data can then be aggregated up to Site level where it can be compared to the actual fiscal electricity use. This gives a good understanding of the accuracy of the overall âmodelâ developed for your site.
There are obvious limitations to this approach and so in practice it is important to understand and then workaround these limitations.
There is a continual discussion concerning the merits of near real time vs historic monitoring. When using MCE this discussion is not that relevant since the system can deliver both modes of operation. Though large volumes of real time energy or hours run data will impact on server performance.The issue is more a question of what will be done with the information once the system is up and running. If your company has the ability to act on the larger volume of alerts coming from a near real time system then fine but experience indicates that this is frequently not the case (many companies do not want energy based alarms included in their main control room responsibilities) in which case the processing of D+1 data may be more appropriate. D+1 means that yesterdayâs data is received and processed early this morning. The raw data is still processed at 15 minute intervals â but the overall calculations are delivered in time for operations to act on them early the next day.
Data cleansing is an essential element of any data monitoring task and must be considered as part of the development.Energy data itself is frequently of good quality but the area where data cleansing is required is in the normalising factor when creating benchmarks and performance KPIâs. This normalising factor is often flow based (i.e. kWh/Ml, kWh/m3 etc). Examples of data cleansing issues to consider include:
The purpose of any Monitoring and Targeting system is to identify the key normalisers that control energy use and set the targets around these.For a range of Industrial/Commercial applications this is relatively simple since the normalisers for monitoring energy use in buildings and in some production environments are quite simple. With Water and Wastewater assets then these normalisers are more complex and in some cases may not be available/suitable to be monitored â i.e. Biological Oxygen Demand as the normaliser for the aeration process in a wastewater treatment plant.As such the setting of targets becomes more complex and may require the use of more than 1 independent variable and the variables themselves may be surrogates for the variable that we really wish to monitor.The purpose of setting this target is to be able to identify when the actual sub process and process performance deviates away from this target.An example of the relationship between the Actual and Target use for a large wastewater treatment plant is shown in Appendix 7.1. This data is derived from a bottom up approach with targets established for each process. There are two main ways that these targets can be set.
Calculations can be as complex as required to deliver the relevant KPIs and metrics. MCE supports a broad range of its own functions or more experienced users can use the full range of the C# functions in the Microsoft .NET Framework libraries. This also means that high level uses of MCE can write their own C# functions for complex problems.This level of calculation complexity can also be applied to the targets, high and low alert limits and to the costs. This latter point means that MCE can be used to model any electricity tariff.MCE also retains a dependency tree of calculation in memory allowing chains of calculations to be created. Re-calculation of one will subsequently lead to recalculation of all the other relevant calculation in the tree.
By aggregating both consumption and target data using a bottom up approach allows the user to create calculations to generate alerts if the sum of the savings (actual use â target use) for the whole Site, or even for particular process, exceeds specified limits which may include a period of time over which the savings should have been occurring and/or a value of savings. This helps to ensure that Opportunities are only identified for meaningful amount of money and are not short duration âblipsâ.
All data feeds can be automatically uploaded into MCE as standard semi colon delimited text files in the format Alias; Date and Time; Value.
4.3. Configuration with the web site
The main MCE web site provides an alternative means to configure the database for simple updates/additions (PC Client is much better for making a number of such changes) but is primarily used for analysis using a range of drill down graphs, dashboards and management reports. The web site is the only way for an authorised administrator to manage access rights and permissions.Designed principally as the management access to the data but is being generally superseded with the range of Silverlight dashboards.
This is a Silverlight dashboard solution aimed at giving the user the ability to select the key data they wish to use to set the targets. Examples are given in Appendix 7.2 and 7.3. This dashboard was developed as a tool to help automatically identify the best pump combination to use for multiple large pump installations.