Increasing concerns regarding the environmental pollution of plastic waste are driving the shift towards more sustainable solutions, such as plastics derived from biomass. Economic sustainability, environmental impacts and “green credentials” are key issues for environmentally conscious chemical companies and bioplastics manufacturers seeking to differentiate their products in the market.

This project aims to develop a machine learning model that can predict the costs and environmental impacts of manufacturing chemicals used in bioplastics. Traditional cost and sustainability assessment tools often require expensive commercial software and specialist knowledge to operate. Our flexible evaluation model will help researchers and manufacturers test hundreds of different combinations of raw materials and processing capacities quickly and accurately eliminating the need for time-consuming experiments and expensive conventional modelling tools. Our case study will be a process producing 2,5-furandicarboxylic acid (FDCA), a key component for manufacturing sustainable plastics.

By using this innovative model this project hopes to identify the most promising bioplastic production methods, attracting investment and accelerating market entry. The project involves collaboration with 17Cicada, which has developed a novel FDCA production process where microorganisms can convert a wide range of green materials, such as algae, into FDCA. The ultimate goal is to support the UK’s efforts to reduce carbon emissions and promote sustainable industrial growth.