Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products

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Robotics and statistical machine learning have revolutionised manufacturing of mechanical devices and our ability to deal with large amounts of data in many areas of human activities. Chemical manufacturing remains one area where both robotics and statistics have seen very limited uptake. However, both are the likely solutions to many challenges facing the chemicals manufacturing industries. The challenge of sustainable manufacturing requires a rapid switch to locally available renewable feedstocks, new sources of energy and use of rapidly re-configurable intensive reactor technologies. However, conventional methods of process development are slow: this stems from the inherent complexity of chemical processes, including multiple interactions of many components over a very broad range of length and timescales, from behaviour of single molecules to the behaviour of cubic-meter-scale manufacturing reactors. This is where machine learning algorithms could provide the solution, with the ability to rapidly identify the underlying interactions and to design the most useful experiments to perform. To use such algorithms effectively we require a new type of a chemical experiment - highly automated and 'intelligent', equipped with sensors and the ability to link with mathematics, the data handling and the machine learning. The main advance on knowledge that this proposal will realise, is the translation of discovery of new chemical products to their manufacture. For this in this project the chemical robot will learn to identify key process parameters that will impact on scaling the process and will help to develop a scale-up model of a process. This ability will have a tremendous impact in all sectors of chemical products manufacturing, with the main societal impact of better process safety, guaranteed product quality and reduced impact of manufacturing on climate change through reduced emissions and feedstocks waste.