WHITE PAPER
Advanced Analytics for the Water & Wastewater Industry
Leverage Operational Data for Immediate Insights into Improving Plant Performance
While many industries have adopted digitalization and Industry 4.0 to monitor and improve their processes, those that use water and wastewater as a process or in their processes have been slow to adapt. Today, process engineers are realizing the benefits of digitalization and growing their maturity models. They are learning that advanced data analytics can optimize processes, improve performance, reduce operational costs, and eliminate regulatory fines.
Learn how the water and wastewater industry can use advanced analytics to enhance sustainability, improve operational performance, and increase profitability.
The purpose of this white paper is to demonstrate how TrendMiner can help accomplish these goals. By the end of the paper, water and wastewater experts will see how the advanced self-service data analytics platform empowers engineers and manufacturing process experts to resolve challenges without the help of a data scientist.
Industrial water treatment opportunities include:
- Monitoring a plant’s pump infrastructure
- Detecting and reacting to pipe network abnormalities
- Correlating water quality with live process data
Water & Wastewater treatment opportunities include:
- Reducing energy consumption with improved maintenance scheduling
- Monitoring pump performance
- Overcoming high-input variances to create a condition-based maintenance schedule
Download the White Paper
Read about using data-driven operational insights to create a more efficient water treatment plant.
Major companies already use some analytics for their larger on-site energy issues. Traditionally, this has required significant, time-consuming resources. Engineers often turn to data scientists to gain insight on their processes, but not all data scientists share the same knowledge. Furthermore, many water and wastewater treatment plants have fewer employees, including engineers, than other industrial plants. This is where TrendMiner can help. The self-service advanced analytics solution enables process engineers to use their knowledge and skill sets to advance plants along their digitalization journey.
About the Authors
Daniel Münchrath started his career at TrendMiner after earning degrees in biochemical and chemical engineering. Daniel works as a Customer Success Manager in the DACH division, where he brings the two worlds of industry and data analytics together. Since joining TrendMiner in 2017, he has been supporting the DACH-division to grow and succeed in its efforts.
Charlotte Fischer is a Customer Success Manager at TrendMiner where she helps companies and software users become successful with their self-service analytics programs. She previously was a project engineer at Blücher GmbH, where she specialized in water treatment with activated carbon. Charlotte earned a master’s degree in Biochemical Engineering at TU Dortmund and an MBA with a focus on Process, Quality, and Digital Change Management at FOM Cologne.
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Blog Posts
Low-code and No-code AI & Analytics Solutions Yield High-Value Use Industrial Use Cases13 September 2024 - 15:19- Building a Foundation for AI & Analytics with an Industrial Data Fabric30 August 2024 - 23:12
- TrendMiner’s 2024.R2 Release; A Complete Platform to Facilitate Event Analytics and Operationalize Industrial Data8 August 2024 - 16:38
