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Details of Grant 

EPSRC Reference: EP/L505791/1
Title: Bio-renewable Formulation Information and Knowledge Management System
Principal Investigator: Lopez-Sanchez, Professor JA
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Chemistry
Organisation: University of Liverpool
Scheme: Technology Programme
Starts: 01 June 2014 Ends: 31 May 2016 Value (£): 20,167
EPSRC Research Topic Classifications:
Design of Process systems Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
EP/L505808/1 EP/L50578X/1
Panel History:  
Summary on Grant Application Form
The project will build a demonstration information and knowledge management system (IKMS) to facilitate innovation with

new and replacement chemical materials from renewable biomass in formulated products. The IKMS will enable functional

ingredients from simple transformations of feedstocks to be identified more quickly and recommend the best feedstocks for

a particular function. If successful, it will repair a disconnection in the supply chain for exploitation of bio-based and

renewable materials as functional ingredients in formulated products, creating significant business benefit to the

commercial partners and, following dissemination and further development, to the UK bio-based materials sector and

formulated products businesses as a whole. The demonstrator will focus on a search for bio-surfactant innovations, and will

be innovative in itself by both integrating several IT tools for the first time in a radical approach to formulated product design and by being the first of its kind to be applied across a chemical using industry supply chain.

The ambition of the system is that it will collate and manage existing data with new data recovered from the experimental

measurements and use this to update the models applied by the search tools. An automated data-driven modelling tool will

be developed and integrated into the system for this purpose. As data is added and as models are improved, the

performance of the selection algorithms will improve along with the chances that the selected ingredient and formulation

candidates will meet downstream commercialisation criteria. It is important to note that modelling methods used here are

quite different but complementary to those to be developed under application 33587-239245, which are physics-based

rather than data-driven, and will provide powerful capability for fast selection of novel chemistries against a subset of filter

criteria and provide mechanistic insights to sharpen these filters for better precision and better experimental assay design.

To achieve its objectives, the project will extend the 101508 information model and add a repository to store formulation

information (composition and assembly) and property data (experimental and computed) to complement the feedstock and

transformation repositories. The information model and repository will need to be chemically intelligent, use readily

extensible RDF and triple store technologies, and incorporate semantic search capabilities to facilitate integration.

Modelling tools will be adapted and implemented using modern machine learning methods to find the mathematical

relationships between ingredient structure and properties, and between formulation composition and assembly with

application performance. The models will be built on data created during the project and added to the 101508 model

repository. The 101508 tools for enumerating ingredient options (from feedstocks and chemical transformation processes)

will be extended to enumerating formulations (from ingredients and assembly processes). The enumeration tools will be

coupled to a global many-objective search tool using diversity or chemical structure/formulation composition/assembly -

property models for efficient exploration of the combinatorial ingredient/formulation space.

We will also develop tools to help maintain and grow the IKMS with minimal overhead to future projects. These include

semantic search and semi-automated extraction of appropriate data from literature and other available resources, and for

ontological integration and semi-autonomous building of ontologies where these do not exist.

In order to demonstrate how this system will work in practice, novel bio-surfactants identified in 101508 will be made and

their properties measured, a selected sub-set formulated and evaluated and the data and derived models used to drive

another cycle of bio-surfactant selection and formulation optimisation.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Project URL:  
Further Information:  
Organisation Website: http://www.liv.ac.uk