The project aims to develop advanced metrics that can be used to develop and test control algorithms for real-time monitoring of anaerobic digestion (an initial step for artificial intelligence control of biorefinery).

Anaerobic digestion (AD) is a nature inspired technology converting organic waste to biogas and nutrition rich digestate. The implementation of AD can lead to generation of renewable energy and circular economy solutions to organic waste. However, uptake of industrial-scale AD in the developing world, and high standards of process control have been limited by monitoring equipment and the limited local research work done on locally available feedstock. For the same reason, existing AD plants offer no rapid or detailed diagnosis of the biochemical balance of digesters, such as stress, build-up of inhibitors, scope for feedstock to be fed or reduced etc. As a result, most AD plants are not optimally operated, are oversized, and still run blind regarding the onset of inhibition or toxicity. No real-time metrics have been developed for use by industry to proactively optimise performance through new feedstock, nutrient supplementation, changes in organic loading rate, or temperature.

This project, supported by Anaero Technology, aims to develop advanced metrics that can be used to develop and test control algorithms for real-time monitoring (an initial step for artificial intelligence control of biorefinery), and to acquire or develop dedicated Nano sensors for industrial control probes.

The research will involve a range of methods including statistically-robust experimental work, analytical chemistry protocols, and big data analytical methods etc. The student will be required to work closely with Anaero Technology, have access to their state of art facilities and spend a minimum of three months in Anaero Technology based in Cambridge.