In groundwater treatment, it is often essential that the iron and manganese removal run well. Even if the techniques involved have been in use for decades, a lot remains unclear about the basic mechanisms of the underlying processes and their speed in practice. Effective measures are implemented only following trial and error, or are even dropped altogether. This entails unnecessary (operational) costs. Better designs, less water loss and fewer particles in the distribution network can all lead to savings.
In this project we (further) developed two models for iron removal: one model for subsurface iron removal, and one for above-ground iron removal. Moreover, by combining model development with large amounts of (existing) knowledge and data, we gained insight into which knowledge is vague or limiting. This allows us to use targeted measurement to better define important, imprecisely known parameters, as well as fill knowledge gaps.
Combination of models and measurements for better understanding and optimisation
In this project we combined measurements from water practice and targeted measurements made in labs, with modelling for both subsurface and above-ground iron and manganese removal. We collected and selected data and other information, and we developed and applied the models in different ways: (a) with or without fitting and (b) through the conduct of sensitivity analyses on model parameters.
Translating insights to water practice
The knowledge resulting from this project provided insights that can help improve groundwater treatment by means of a well-running iron and manganese removal process. Also, during the modelling we encountered a variety of knowledge gaps. For the (above-ground) treatment, additional testing of the combined process of oxidation and adsorption is desirable. The model also needs to be extended for the description of the start-up, particle transport (important for back-washing, for example), and the biological removal. For the subsurface, a more precise quantification of some model parameters, such as the reactivity of soil organic matter, is desirable. The models and experience from the practice tests are used to optimise the operational management at existing locations, and to increase the efficiency of iron and manganese removal.