Modelling in practice is a new series at DHI that explores how modelling helps us interpret complex water environments and turn scientific insights into practical decisions.
In this edition, we are joined by Trine Dalkvist, Senior Wastewater Engineer at DHI, to discuss how wastewater process modelling is applied in real-world engineering and operational contexts.
Q: If you were explaining wastewater process modelling to someone outside the industry, how would you describe it in plain language?
Wastewater process modelling is essentially a way of using computers to better understand what happens inside a wastewater treatment plant before we make changes in the real world. I often describe it as a virtual version of the treatment plant that lets us test ideas safely.
In a treatment plant, water, microorganisms, air and chemicals interact in complex ways. Instead of relying only on trial and error, we translate this knowledge into mathematical models that describe how the system behaves. The computer then simulates different situations – such as increased population, heavy rain, stricter discharge limits or changes in operation – and shows us the likely consequences.
A simple analogy is a flight simulator for pilots. You would never train a pilot only by trial and error in a real aircraft. In the same way, process modelling lets engineers and operators ‘fly’ the treatment plant under different conditions without risking compliance, costs or environmental impact.
In practice, wastewater process modelling helps us answer very practical questions: Will the plant meet future requirements? Can energy use be reduced? What happens if we change how we operate the tanks? By testing scenarios digitally first, we can make more informed, robust and cost effective decisions in the real world.
Q: People often talk about digital twins in wastewater treatment. How are they linked to traditional process models in day-to-day use?
In practice, digital twins are a natural extension of traditional wastewater process models rather than something completely new. At their core, they are built on the same well established process models that engineers have used for decades to describe biological, chemical and physical processes in treatment plants.
The key difference is how these models are connected to real life. A traditional process model is typically used offline, for example, to test design options or study ‘what if’ scenarios using assumed inputs. A digital twin uses the same modelling principles, but it is linked to real operational data from the plant, such as flows, loads, sensor measurements and control settings. This means the model reflects how the plant is actually behaving right now, not just how it might behave in theory.
In day to day use, this allows engineers and operators to move seamlessly between analysis and operation. The digital twin can be used to test operational changes, forecast upcoming challenges, or support decisions on energy use, emissions and compliance, often before issues become visible in the real plant. In that sense, digital twins turn traditional process models into living tools that support continuous, informed decision making rather than one off studies.
Q: How does wastewater process modelling support both engineers and clients in making better decisions, and can you share an example where it influenced a design or operational outcome?
Wastewater process modelling provides a shared decision support framework for engineers and clients. For engineers, it links process knowledge, data and engineering judgement; for clients, it translates complex system behaviour into clear, comparable scenarios. In practice, models are used to test design and operational alternatives before committing to costly or risky changes, making discussions fact based rather than opinion based.
At Bjergmarken WWTP, a calibrated process model with integrated N₂O dynamics was used to reduce carbon footprint and energy consumption while maintaining strict effluent limits. By testing scenarios such as adjusted aeration setpoints, carbon dosing, influent buffering and sludge return strategies, the model showed that CO₂‑equivalent emissions could be reduced by up to 30% and costs by up to 25% without compromising effluent quality. Rather than pointing to a single ‘optimal’ solution, the modelling clarified trade‑offs and interactions, allowing the utility to prioritise measures that reduce energy use and emissions without compromising compliance.
Overall, modelling reduces uncertainty and supports robust, cost‑effective decisions aligned with long‑term regulatory and sustainability goals.
Q: In practice, how do modern modelling tools make it easier to handle the complexity and variability of biological wastewater processes?
Modern modelling tools make it easier to handle the complexity and variability of biological wastewater processes by combining detailed process models with data and practical decision‑support workflows. Biological systems are highly dynamic, with influent loads, temperature, weather and operational conditions changing continuously, and modern tools are built to reflect this variability rather than average it away.
Tools such as WEST allow plant‑wide biological, chemical and physical processes to be represented in a structured and modular way, including control strategies and energy balances. When these models are combined with real operational data through digital twins like DHI CityFlow Live Treatment Plant, they can capture short‑term fluctuations, peak loads and unusual events in near real time.
In practice, this means engineers and operators can test alternative operating strategies, control settings and process changes in a virtual environment before implementing them at the real plant. By translating complex interactions into clear scenarios and key performance indicators, modern modelling tools help turn biological complexity into manageable, actionable insights for both day‑to‑day operation and longer‑term planning.
Q: Can you remember what first drew you to wastewater process modelling, and what continues to make it an interesting field for you today?
What first drew me to wastewater process modelling was the combination of biology and problem‑solving. I started out with a background in biology, and modelling gave me a way to translate complex biological processes into something that could actively support real engineering decisions. I was fascinated by the idea that you could take what happens at the microbial level and use it to understand, predict and improve how a full‑scale treatment plant performs.
What continues to make the field interesting today is how relevant and impactful it has become. Wastewater treatment plants are under increasing pressure from tighter regulations, climate targets and rising energy costs. Modelling has evolved from being mainly a design tool into something that supports day‑to‑day operation, optimisation and long‑term planning. The ability to combine process understanding with real data – through digital twins and advanced scenario analysis – means the work is never static.
Every plant is different, and the systems are always changing with weather, loads and operational choices. That variability keeps modelling both challenging and rewarding, because there is always more to learn, and real potential to make a measurable difference for utilities, the environment and society.
