Water and Wastewater Chemical Management


Our MCE analytics platform uses existing chemical tank level data for chemical management and monitoring on water and wastewater sites. By monitoring the rates of change in tank levels, we automatically work out if tanks are emptying or being filled. So, we can calculate chemical consumption and applied dose rates using existing telemetry or SCADA data.

This is up and running on more than 40 water and wastewater treatment sites monitoring a broad array of chemical.


MCE uses 15-minute raw data from the corporate telemetry or SCADA system. This is imported into MCE on a daily basis where a range of algorithms are used to calculate the chemical consumption from changes in the tank levels.

Issues such as data collection and communication problems generate errors such as, negative values, zero consumption or constant small increases/decreases in tank levels caused by sensor resolution errors. These data issues mean that simple rates of change calculations overestimate consumption. Experience has shown that  these simplified rates of change calculations are not suitable in practice to cope with real life data.

Our analytics platform allows the use of more complex algorithms to overcome these issues so that calculated consumption values match actual values. Once accurately calculated consumption data is available then a range of additional metrics can be calculated, such as the actual dose rates. These in turn are used to create High and Low alerts to identify if chemical use if higher or lower than expected.

This application is built using the Meniscus Calculation Engine (MCE). Data from the platform is extracted and displayed using our MCE widgets. This helps provide a proven chemical management and monitoring system.

Benefits of using our analytics platforms

MAP provides the flexibility to apply any calculation to any set of raw data. In this case, this flexibility offers the user the ability to apply more complex algorithms to accurately calculate chemical consumption values. This solution has helped to reduce the workload on the company’s process scientists who previously manually calculated these figures.