How Vallei en Veluwe optimised their wastewater treatment process

Whitepaper: Find out how to improve the performance of your wastewater treatment process and reduce your energy consumption up to 15%.

Vallei en Veluwe wastewater treatment plant

Project facts

  • Client
    Vallei en Veluwe
  • Location
    The Netherlands
  • Challenge
    Improving wastewater treatment process performance so it’s more efficient and sustainable – across transport, nutrient removal, and energy and chemical usage.
  • Solution
    Vallei en Veluwe implemented Twinn Aqua Suite flow prediction and process optimisation solutions to improve wastewater discharge and nutrient removal while minimising energy and chemical use.
  • Impact
    Twinn Aqua Suite delivered calmer networks, reduced peak flows, improved treatment processes and better effluent quality – resulting in a 50% reduction in bypass of tertiary treatment and up to 15% reduction in energy consumption.
Responsible for 16 wastewater treatment plants, Dutch water authority Vallei en Veluwe processes 340 million litres – equivalent to 2.2 million baths – of wastewater every day.

The organisation’s goal is to draw all its energy from renewable sources by 2050. As such, it’s constantly searching for innovative treatment solutions, including strategies to improve surface water quality and extract more energy and raw materials from sludge.

The challenge

Optimising wastewater transport and nutrient removal while reducing energy and chemical usage

Vallei en Veluwe was searching for ways to optimise the entire wastewater treatment process with the overarching aim of meeting its sustainability objectives.

“The wastewater treatment process is heavily influenced by the steadiness and predictability of the influent flow,” explained Frank van de Grootevheen, Vallei en Veluwe’s Process Engineer. “Peak flows and loads, especially during periods of heavy rainfall, were creating disturbances to the process and the effluent quality. We needed calmer catchment control during rain-weather flow conditions.”

Vallei en Veluwe also needed to reduce energy and chemical usage, as well as improve nutrient removal processes. In addition, local control systems and operations differed for each plant and were reactive rather than predictive. As a result, manual intervention was often required, which was an inefficient use of staff time.

With increasingly stringent effluent quality regulations, efficient systems are becoming more and more important. Twinn Aqua Suite provides great insights and smart control, which means better management of load peaks and usage of our installed treatment steps.

Frank van de GrootevheenProcess Engineer at Vallei en Veluwe

The solution

Leveraging real-time data and machine learning to predict flows and optimise processes

Vallei en Veluwe implemented two Twinn Aqua Suite solutions.

Our flow prediction solution uses real-time data and rainfall forecasts to predict flows in the sewage network and optimise discharge to the treatment plant with predictive control. This auto-pilot reduces peak flows and bypass of post-treatment, resulting in calmer networks and better effluent quality.

The organisation also deployed our process optimisation solution, which learns wastewater inflow patterns and predicts daily inflow – including detection of rain-weather flow – to anticipate changing loads. It applies machine learning for feedforward control, finetuned with feedback control. Then, it adjusts aerators, pumps and valves to optimise nutrient removal and reduce energy and chemical use as far as possible.

We have introduced our Twinn Aqua Suite process optimisation solution at 8 of Vallei en Veluwe’s wastewater treatment plants – with 4 of those also implementing our flow prediction solution to optimise discharge to the treatment plant.

Melchior SchenkSales Leader, Twinn Aqua Suite

The impact

Calmer networks, reduced energy consumption, increased efficiency and improved performance

Aqua Suite flow prediction and process optimisation solutions have delivered strong results across the Vallei en Veluwe network. “With increasingly stringent effluent quality regulations, efficient systems are becoming more and more important,” said Frank. “The software provides great insights and smart control, which means better management of load peaks and usage of our installed treatment steps.”

At the 4 treatment plants where Twinn flow prediction capabilities are used to control wastewater discharge from the sewage network, there has been an average of 50% bypass reduction of post-treatment filters. This has led to a huge improvement in effluent quality as well as a significant reduction in time spent on operational issues. Process operators have found the robust auto-pilot controller easy to use, and wastewater treatment system performance has been enhanced as a result.

“Aqua Suite has brought about great improvements in the utilisation of our post-treatment assets. We are undoubtedly in better control of plant operations, regardless of flow conditions,” said Frank. “Looking to the future, where removal of micro-pollutants is set to become increasingly important, this level of smart control will be enormously helpful in optimising our treatment systems while keeping costs down."

Across the 8 locations where our process optimisation capabilities have been introduced, Vallei en Veluwe has experienced energy consumption reductions of up to 15%. Staff have more stable, predictive control, as well as an instant overview of the actual situation at the treatment plants.

“The software has met our need for stable effluent quality and reduced energy consumption. It has increased the sense of ‘being in control’ of treatment processes, especially in combination with centralised operations from one main control room,” said Frank. “It’s a reliable controller that provides a snapshot overview of the actual situation at a glance on a clear dashboard, with accurate predictions of dry-weather flows and delivering good results for nitrogen removal at the same time.”

0bypass reduction of the post-treatment filters
0reduction in energy consumption

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