Waternet introduces automation across its network 

Waternet works for the Regional Public Water Authority Amstel, Gooi and Vecht and for the municipality of Amsterdam. It delivers 250,000 m³ of clean, safe, drinking water per day to 1.5 million people across Amsterdam and the surrounding area. The water supply system consists of 2700 km of water network, two water treatment works and five pumping stations. 

The challenge

Optimising an ageing infrastructure to enable digital transformation 

The existing infrastructure included a combination of river (Rhine) water which is treated in the sand dunes and then transported to the city, and resurfacing ground water from the Bethunepolder which is treated near the city. Because of hydraulic limitations for the transport from the dunes, there are additional storage reservoirs and booster pumping stations within the cities. Waternet recognised that a holistic view of the reservoirs and its other assets was required to develop a calmer network and improve performance.   

The existing infrastructure required monitoring 24/7, and the team at Waternet was required to work evenings and weekends to ensure the stability of the water supply. This was identified as unsustainable, and that the infrastructure needed updating to provide efficiencies, and help the business move towards carbon net zero. 

The solution

Predictive analytics, combined with AI and machine learning from Aqua Suite to develop a calmer network

Waternet has worked for many years with Royal HaskoningDHV to improve performance and support their ambition towards net zero. Following the initial introduction of Aqua Suite, Royal HaskoningDHV worked with Waternet to further optimise the water distribution. Implemented software (Opir module) helps to determine the correct distribution by predicting consumption for the pump station's distribution. This ratio determination allows Waternet to work unmanned during the weekend and night shifts. Such, company not only looked at actual flow and level measurements, but also optimised the distribution based on demand prediction and the resulting reservoir levels.

The impact 

No more night and weekend shifts, due to source to tap automatic pilot 

By distribution prediction and determination with implemented software, Waternet was able to make optimal use of the reservoirs, level off production and reduce energy consumption. Consumption forecasts contribute to having a calmer network, which leads to the reduced wear and tear of pumps and valves. However, the biggest change was new confidence in the system, leading to the nighttime and weekend shifts no longer being required. The automatic pilot provided insight into the consumers’ water consumption, by predicting what their drinking water consumption will be - in a given area, and up to three days in advance.