Parameter estimation of multi-substrate biokinetic models for rapid scale-up and optimisation of industrial fermentation process for the production of

The current global food system faces significant challenges related to waste production, carbon emissions, and resource inefficiency. This work aims to address these issues by focusing on the application of microbial protein technology for sustainable protein production from organic waste, thereby promoting a circular economy. The study focuses on a critical bottleneck in bioprocess development, specifically in waste carbon utilisation, emphasising the need for precise biokinetic models. Unstructured models are to be employed for their simplicity and widespread applicability, but challenges in parameter estimation persist, especially for multi-substrate systems. The research introduces an experimental-computational methodology for high-throughput screening, utilising absorbance spectroscopy and HPLC analysis from batch 96 well plate fermentations. The study expands parameter estimation techniques towards multi-substrate biokinetic models for the conversion of lignocellulosic hydrolysates to mycoprotein (Fusarium venenatum A3/5). Various experimental designs explore the influence of sugar composition, pre-culture environment, and substrate-to-biomass ratio on model performance. The ultimate goal is to inform decision-making for the viable scale-up of industrial waste-to-mycoprotein processes, considering sustainability and technoeconomic constraints.