Parameter estimation of multi-substrate biokinetic models of lignocellulosic microbial protein systems

Mason Banks; Mark Taylor; Miao Guo

doi:10.1016/B978-0-443-28824-1.50427-0

Parameter estimation of multi-substrate biokinetic models of lignocellulosic microbial protein systems

Mason Banks, Mark Taylor, Miao Guo^*

^*Corresponding author for this work

Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

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 multisubstrate 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.

Original language	English
Title of host publication	Computer Aided Chemical Engineering
Pages	2557-2562
Number of pages	6
Volume	53
DOIs	https://doi.org/10.1016/B978-0-443-28824-1.50427-0
Publication status	Published - 26 Jun 2024

Publication series

Name	Computer Aided Chemical Engineering
Volume	53
ISSN (Print)	1570-7946

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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abstract = "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 multisubstrate 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.",

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N2 - 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 multisubstrate 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.

AB - 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 multisubstrate 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.

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ER -

Parameter estimation of multi-substrate biokinetic models of lignocellulosic microbial protein systems

Abstract

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UN SDGs

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