Details of Grant 

EPSRC Reference:	GR/R42061/01
Title:	Using generic algorithms for high throughput screening of ionic liquids as reaction solvents in micro-channel reactors
Principal Investigator:	Rooney, Professor D
Other Investigators:	Hardacre, Professor C Seddon, Professor K
Researcher Co-Investigators:
Project Partners:
Department:	Chemical Engineering
Organisation:	Queen's University of Belfast
Scheme:	Standard Research (Pre-FEC)
Starts:	01 May 2002	Ends:	30 September 2005	Value (£):	189,884
EPSRC Research Topic Classifications:	Catalysis & Applied Catalysis Combinatorial Chemistry Reactor Engineering
EPSRC Industrial Sector Classifications:	Chemicals No relevance to Underpinning Sectors
Related Grants:	GR/R42078/01
Panel History:
Summary on Grant Application Form
This project unites two institutions together for the common goal of developing a completely novel way of rapidly and accurately determining the optimal process conditions for industrially important reactions. This is to be achieved by combining the chemistry and engineering requirements to produce an optimised reaction system (including variables such as solvent, temperature, residence time and reagent concentration). Combinatorial techniques fail to provide engineers with appropriate kinetic data but by using background work derived from a preceding iAc project, we will develop a high throughput screening approach based on micro-channel reactors. Using such systems it is possible to quickly determine reactor kinetic data for numerous ratios of reagent to solvent at a set temperature. The choice of the optimum solvent is however difficult. Environmental legislation has forced industry to use high t liquids, the ultimate of which is an ionic liquid. These solvents have proved themselves to be both highly effective and environmentally friendly media for tl reactions proposed, including Friedel-Craft reactions, Diels-Alder reactions, and palladium-catalysed allylations (Heck). However choosing the right solvE system, with the most effective catalyst, at the optimised process conditions involves a parameter space of many thousands of items. Searching for optin becomes impossibly time consuming particularly when more than one exists. Even though standard statistical techniques can alleviate the problem some the use of genetic algorithms for this function is preferred.
Key Findings
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Further Information:
Organisation Website:	http://www.qub.ac.uk