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Saving costs and improving effluent by knowing what is happening in your bioreactor - Part I

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AerationWWTPEffluent quality

This is the first episode of a series of articles that describe a unique advanced modelling project in the water industry in a practical way. This article focuses on the wastewater industry but it's definitely interesting if you're in the drinking water business. If you don’t like reading (or you can’t wait), just watch the explanatory action movie on the left. This part focuses on measurements and unprecedented model validation using a construction crane

 

1. The full-scale plant and their challenge

The Eindhoven water resource recovery facility (WRRF) is a 750,000 p.e. conventional activated sludge plant, operated by Waterboard De Dommel (the Netherlands). While the plant operators want to lower effluent nitrate, low levels are hard to get, especially during cold weather. To find the optimal performance of the existing bioreactor, an advanced denitrification step is done before the plant upgrade to evaluate if an upgrade is needed. Both our previous modelling work and practical observations have shown that elevated oxygen levels close to the intake of the anoxic tank may hinder denitrification in the bioreactor, so this is where we pointed our arrows on.

Air image of Dommel WWTP in EindhovenFigure 1: The Eindhoven WWTP. Measurement locations in one of three identical bioreactors. Red circles show the reach of the construction crane. Orange dots: oxygen measurements, red dots: velocity measurements.

2. The modelling objective

  • Find solutions to lower effluent nitrate significantly, whereby operational measures are preferred over design modifications
  • Save costs 
  • Detect other optimisation opportunities 

3. The strategy - what is the value of modelling?

Trial-and-error testing has its limitations: it's risky, laborious, time-consuming and lacks testing freedom. That's why Waterboard De Dommel chose a structural advanced modelling approach. Our proprietary CFD-biokinetic model was ready to use, so we could start fast. First, we wanted to extensively validate the model to remove any remaining doubt about the model. Actually, we wanted to demonstrate the validity of an advanced model like no one did before. And this is what we discuss in this article.

Questions for the validation:

  • Do we observe the same oxygen gradients as predicted by CFD in reality?
  • Do the measured gradients correspond to the simulated gradients?
  • Is the model accurately predicting the flow velocity?

4. Why the heck did we hire a construction crane?

We'll answer that question immediately. And no, you don’t always need a crane for model validation. 

Construction crane sampling with LDO sensors to collect dissolved oxygen data in WWTP

If you use a crane, you can collect a spectacular data set. The crane allowed us to perform extensive oxygen profiling, leading to a 3D image of oxygen levels along the depth, width and length of the large bioreactor. This is an ideal dataset to compare to the three-dimensional CFD results. We measured during a whole day and collected oxygen data at 99 locations in the bioreactor. If we add the sensor of the bioreactor to that, we even reach 100 locations! Three LDO sensors were attached to the chain of the crane, leaving several meters of spacing. A 700 kg concrete block had to keep the chain vertical, and it luckily did without detaching.

Experimental setup of full-scale validation measurement of dissolved oxygen in wastewater

Figure 2: Submerging the oxygen sensors with the crane along one cross-section

Besides oxygen, we collected velocity data at eight locations, along the depth of the reactor. These local velocities of flow were then compared to model outcomes as an extra validation variable that adds a lot of information. To challenge the model and collect extra information, the plant operator doubled the aeration rate halfway through the measurement campaign.

5. Why is the model so accurate?

In many cases, we have seen people applying rather simple CFD models to model activated sludge systems. For example, the density of clean water is used and other dangerous assumptions are made (eg. how the aeration system is modelled). Based on our experience, these simple approaches have very limited accuracy and hence, value. Our activated sludge model was developed and improved throughout the years and contains proprietary equations. Furthermore, biokinetics (e.g. ASM1) are integrated, so the model is also able to calculate sludge, nutrient and carbon conversion rates and concentrations in 3D. Flows and concentrations can be simulated within a couple of hours, making testing of many different scenarios possible. AM-TEAM co-founder Dr. Usman Rehman developed this model during his PhD at BIOMATH, UGent, under the supervision of prof. Ingmar Nopens and in collaboration with Waterboard De Dommel.

6. Results

6.1. Velocity

The next graphs show velocity profiles across the depth of the reactor. These recordings relate to a cross-section: the blue data points were recorded near the inner reactor wall, the brown ones close to the outer wall. Using the simple, and conventional, CFD modelling approach, we typically obtain accuracy as shown on the left graph.

Velocity validation of computational fluid dynamics (CFD) model

Figure 3: Velocity data compared to simulated data using the 'standard' approach (left) and AM-TEAM's in-house approach (right)

After a major improvement, the fit in the middle was obtained. After further improving the model with a second major feature, we ended with the fits shown on the right - and in more detail further in this article. As you can see, the model accurately describes the data trends, which means the main mechanisms were taken into account. Adding more resolution to the CFD model clearly increased model accuracy and value significantly.

Goodness of fit between experimental and simulation data of velocity (CFD)

Figure 4: More detailed comparison of velocity data and simulated data using AM-TEAM's in-house approach

6.2. Dissolved oxygen

The figures below show comparisons between dissolved oxygen data and simulated air distribution at four locations. The biokinetic model was not yet activated at this stage of the project, so there is no simulated oxygen data yet. However, we can assume (as we have seen in the past) that DO levels and air are correlated and that more DO can be found where more bubbles are.

Validation of CFD model based on dissolved oxygen profiles and gradients

Dissolved oxygen profiles in anoxic zone of bioreactor

Figure 5: comparison of air distribution (gas holdup) and measured DO concentrations at 4 locations in the bioreactor (A and B are in the aerated zone)

Measured oxygen gradients looked as we expected based on the CFD outcomes.

Conclusions regarding DO:

  • Large DO gradients exist, even, and especially in the aerated zone, which often is regarded as a completely mixed zone. Differences of 96% along the measured depth were found.
  • Higher DO levels were measured at the expected locations (near the surface and inner wall).

As you probably know, water and air are not the best of friends, which results in typical hydrodynamic segregation of the phases. Bubbles like being together, probably reducing the overall area available for mass transfer.

7. General conclusions

  • Never before, the accuracy of CFD modelling for water applications was assessed and demonstrated like this
  • Producing nice colourful drawings is easy. Producing accurate ones requires advanced expertise
  • The plant was reorganized based on the modelling outcomes
  • Completely mixed reactors, or even zones, do not exist. We have to live with that and more importantly, account for that. By keeping mixing in mind, the water sector could save a lot of time and money in the future.

 

These results were partially presented at Weftec 2017 (Chicago), the IWA Large WWTP conference in Chongqing (China), and in scientific publications.

There's more to discover

Eager to find out more about this project?

Read the second article on model-based plant optimisation

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Special thanks to

  • Waterboard De Dommel: Tony Flameling, Peter van Horne, Han van Happen, Victor Claessen, Stefan Weijers, Peter van Dijk (overall help and support)
  • Ghent University – BIOMATH: Giacomo Bellandi, Tinne De Boeck, Chaïm De Mulder, Ingmar Nopens (scientific discussions and measurement campaign)
  • Kinnear Process Solutions: Dave Kinnear (equipment for velocity measurements)
  • Go-solar sustainable energy: Chris Poppe (spectacular drone images)

 

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