Case study

UReason supports Aquafin With Data Driven Maintenance, Making It Possible To Save €400K Annually

2 min. read

MaxGrip & UReason join forces to help reduce maintenance cost at Aquafin by implementing data driven maintenance.

UReason’s APM Studio solution offers advanced predictive maintenance methods which give you accurate insight and predictions on how your assets are performing. Aquafin was able to significantly reduce intervention costs and the number of man-hours needed, thereby making it possible to save up to €400.000 annually.

 

 

Aquafin Logo

The Situation

Aquafin is a Belgian utility company responsible for the infrastructure of sewage water purification in the province of Flanders. Aquafin is not only responsible for the operation, but also the financing and expansion of the wastewater treatment infrastructure in Flanders. The Belgian company is responsible for managing 2,500 pumping stations and 318 water treatment plants. Aquafin wanted to predict pump failure and implement a more proactive approach to maintenance on these failed pumps. UReason and MaxGrip join forces with their respective knowledge in predictive maintenance and consulting to optimise processes at Aquafin.

The Challenge

Before the project came about, no use was made of the data of the pumps to predict pump failure. Due to the dependency of the province on Aquafin for the purification of sewage waters, maintenance had to be directly implemented to avoid unwanted discharge in the water, lowering water purity.

If Aquafin would be able to better predict maintenance around working hours, operations will be more efficient, water purity would be increased and general maintenance cost due to unplanned hours would be decreased.

The Solution

UReason offered APM Studio to support Aquafin. With APM Studio, Aquafin was able to:

  • Run real-time analysis of the root causes of the current flows of events and alarms.
  • Create dashboards & insights that can be used to predict failure and avoid or prevent PLC/ SCADA alarming.
  • Identify critical issues on pumps 3,5 hours earlier than current PLC/SCADA alarms, lowering the risks of consequential damage due to complete failure of assets and showing that PLC/SCADA alarms were missed previously.
  • Create rules to detect a low flow/power ratio and a downward trend on the flow. This results in the use of a probabilistic BowTie model.
APM Studio Dashboard of Advanced alarm management
Within APM Studio, Advanced Alarm Management & Probabilistic Bowtie models help Aquafin predict pump failure prematurely

The Benefits

  • Aquafin transformed from a reactive to a proactive,  real-time data driven maintenance strategy.
  • Able to identify pump issues 3.5 hours earlier than with standard PLC/SCADA alarming, allowing Aquafin to switch pumps before losing pump capacity or even capability and scheduling maintenance accordingly.
  • Reduce 50% in weekend and intervention costs, with further improvements possible in weekend and night hours.
  • Save energy and reduce carbon footprint by further implementing data driven maintenance and optimizing processes, helping them reach CSR targets.
  • Reduce unavoidable discharges, which keeps water purification levels at 100% and helps Aquafin comply with European requirements.

Learn More About This Solution

Want to know more about the solution and how it can support your production?

Related Articles

  • Blog 09/02/2022

    Who Owns Your Data? Data Storage And Predictive Maintenance

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Webinar 08/18/2021

    Digital Twins for Data Driven Maintenance?

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Webinar 02/10/2021

    How to do real-time, data driven, risk assessment on your asset base?

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Webinar 09/06/2021

    Wie berechnen Anlagenbetreiber Business Cases für Data Driven Services?

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Webinar 03/10/2022

    Wie entwickeln Maschinenbauer und OEMs data driven Services?

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Case Study 06/01/2022

    UReason Supports KUKA Towards More Autonomous Production

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Case Study 07/29/2022

    UReason Supports SUEZ To Reduce Costs And Have A Safer Operation

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • Article 06/24/2021

    Ultimo and UReason Close the Loop From Asset Data to Automatic Work Order Generation

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.

  • News 09/04/2020

    BEMAS | Aquafin Hackathon

    This article explains the concept of Digital Twin, what a Digital consists of and how you can implement it in solving maintenance and operations problems.