Study of nutrient availability in ash for future effective use in fertilizers

The researchers will work on developing deep learning/machine learning methods on tomography data from synchrotron experiments. The technique provides a 3D image of the sample and advanced image processing can be used to obtain characteristics of the sample. Artificial intelligence through deep machine learning can play a major role in this project as data analysis will be improved and made more efficient.

Large amounts of phosphorus and other important nutrients are present in many of society's residual streams. By capturing the ash after incineration, there may be an opportunity to recycle nutrients from it. In order to use ash for nutrient recycling in an efficient and sustainable way, it is important to gain a better understanding of how the chemical and physical properties of ash affect the availability of nutrients when used as a fertiliser. The research aims to evaluate parameters such as microstructure, porosity and surface area, as well as the chemical form of phosphorus compounds present in the ash.