PhD Studentship: Biotechnology Design and Optimisation for Waste Valorisation

Food sector growth is accompanied by rising waste streams, which offer considerable potential for resource recovery due to high concentrations of organics and nutrients. Biotechnologies as engineered biological systems offer promising solutions to sustainably utilise the waste streams with complex chemical compositions. However, the microscopic mechanisms e.g. complex molecular interaction in bioreactors are yet to be fully understood, which are hindered by analytical technology advancement. Molecular dynamics (MD) cross-scale modelling represents an emerging research area and offers computational methods to unlock microscopic limitations and bring microscale to meso-scale bioreactor design enabling biotechnology optimisation.

This project aims to bridge microscale complexity (e.g. microbiome structure, molecular interaction in bioreactor) and meso-scale bioprocess design to experimentally and computationally optimise biotechnology for waste valorisation. Selected organic waste valorisation through ‘model’ bioreactors will be explored as a representative study. Objectives are –

Obj-1 Develop a computational platform that can be used to optimise the value of the material retrieved from the waste stream.

A combination of optimisation theory, molecular simulation and machine learning (ML) algorithms will be used to develop new cross-scale modelling methods to enable a computationally-efficient platform to optimise bioreactors and the underlying biological & physical-chemical processes across micro-and meso-scales. Molecular dynamics simulations will be deployed to uncover the microscopic mechanisms, which govern the complex atomic/molecular interactions in bioreactors, including substrate/product diffusion, reaction and flow (physical-chemical processes) and the growth of microorganism species (biological process). In order to link this microscopic detail into the design of a mesoscale bioreactor, the PhD student will explore a new dynamic surrogate optimisation approach based on our recent research on Bayesian and neutral network emulators and  object-oriented optimisation software. This will enable computationally tractable optimisation of bioreactors and identify optimal process conditions with respect to waste-recovery objectives. 

Obj-2 Model validation to design and optimise biotechnology to valorise organic waste.

Based on our chemical analyses and characterisation of wastewater, two ‘model’ bioreactors (i.e. waste-to-energy and waste-to-protein) will be tested. Multi-scale model (Obj-1) will be applied to optimise waste-to-energy bioreactors and then the continuous bioreactor in Guo lab and Anaero lab (industrial partner) will be used to validate the model. Metagenomic and pan genomic analyses will provide insights into the microbial structure and function; the microbial kinetics, substrates and metabolic products will be monitored by sensor and chemical analyses. Experimental data will feedback into the model for calibration and improved validation. In a similar manner, we will develop a model which will be enhanced through experimental testing for the waste-to-protein technology. This will allow us to test whether waste stream could be formulated into growth media for microbial protein fermentation with pre-screened food-safe (or feed-safe) microorganisms. Co-culture systems will be the focus. 

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