{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260100027"},"data":{"type":"grant-details","id":"DE260100027","attributes":{"code":"DE260100027","administering-organisation":"Curtin University","announcement-administering-organisation":"Curtin University","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":" Nonlinear scheduling optimisation for green hydrogen production. This project aims to develop cutting-edge mathematical algorithms to optimise operation scheduling for green hydrogen plants, to enhance overall productivity and reduce green hydrogen production costs. Optimisation problems in this domain are highly nonlinear and of massive scale. The project will leverage recent breakthroughs in integer programming and nonlinear optimisation to create efficient computational algorithms for overcoming this complexity. These algorithms will provide critical insights into optimal operations strategies for potential Australian hydrogen scenarios. The new theoretical developments will contribute to bridging the gap between discrete and continuous optimisation, two fields that are normally studied disparately.","funding-current":523483.00,"funding-at-announcement":519436,"investigators-current":[{"title":"Dr","firstName":"Hoa","familyName":"Bui","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-1698-6383 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Hoa","familyName":"Bui","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-1698-6383 "}],"organisations-current":[{"organisationName":"Curtin University","roleName":"Administering Organisation","state":"WA"}],"organisations-at-announcement":[{"organisationName":"Curtin University","roleName":"Administering Organisation","state":"WA"}],"field-of-research":[{"isPrimary":false,"code":"490108","name":"Operations Research","type":"FOR20"},{"isPrimary":true,"code":"4903","name":"Numerical and Computational Mathematics","type":"FOR20"},{"isPrimary":false,"code":"490304","name":"Optimisation","type":"FOR20"}],"socio-economic-objective":[{"code":"170102","name":"Industrial Energy Efficiency","type":"SEO20"},{"code":"170704","name":"Hydrogen Production From Renewable Energy","type":"SEO20"},{"code":"280118","name":"Expanding Knowledge In the Mathematical Sciences","type":"SEO20"}],"international-collaboration":["Austria","France","Germany","Spain","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"The Australian Government is investing billions of dollars to position the nation as a major global producer of green hydrogen. The success of this new industry relies on large-scale infrastructure and reliable operational systems, with efficient operation scheduling being crucial for performance and reliability. However, operation scheduling is a significant challenge. Even in mature industries like mining, it is already laborious and highly complex; and today's most advanced computer algorithms cannot scale to the dimensions required for operations scheduling in industry. There is a critical need for a novel mathematical optimisation framework and fast, scalable algorithms to tackle these complex scheduling problems. This project will address this gap by developing effective scheduling algorithms for optimising production activities through new advances in mathematical optimisation. The outcome will be innovative scheduling technology that provides optimal planning and scheduling strategies, minimising costs and safety risks while enhancing overall productivity and reliability. These new scheduling algorithms will be applied to proposed Australian hydrogen projects, accelerating the industry's viability and contributing to decarbonisation efforts."}}}