Doctoral Training Alliance (DTA) program MSCA COFUND supported doctoral programme – DTA^3 funded through the MSCA COFUND scheme to provide an enhanced coordinated training provision across all three programmes and enable us to extend our recruitment wider across our international networks and communities (DTA^3 MSCA COFUND).
Industry is facing higher energy-costs and needs to reduce financial and environmental impacts of using energy. Government recognised needs to reduce climate change effects and introduced targets to achieve by 2020 / 2050. Air compressors account for >10% of UK industrial energy use. Ambient-sensing and knowledge gathered within manufacturing environments represent untapped resources to optimise energy use. This research project will investigate ambient-sensing with artificial intelligence (AI) for manufacturing units that interact with people to produce detailed awareness. AI will interpret sensors, make intelligent judgements and take automated decisions in real-time. It will evaluate compressed air systems by asking questions such as: "Are hoses leaking?", "Is air needed?", "Does loading need all compressors?", “Can couplings be removed?", "Are compressor sizes correct?".A knowledge management system will answer questions and automatically provide energy efficiency suggestions. Answers will include: "Use smaller compressor.", "No action.", "Replace filters.", "Investigate.", "Dry system.", "Replace compressors".The research will go beyond current practices (e.g. condition monitoring) by introducing intelligence and holistic awareness. Data will concern equipment, how manufacturing units are performing, environmental effects, human interactions, and energy consumption. That data will be brought together and used with machine learning techniques to provide intelligent approaches to energy efficiency.