For　the　existing　wastewater　treatment　process,　the　carbon　emission　mechanism　is　unclear　and　difficult　to　assess,　which　hinders　the　implementation　of　effective　control　strategies　to　reduce　overall　carbon　emissions.　To　solve　this　problem,　a　data-driven　low-carbon　optimization　control　method　for　the　aeration　process　of　wastewater　treatment　was　designed.　First,　the　influence　factors　of　carbon　emission　and　their　relationship　with　water　quality　parameters　were　deeply　analyzed,　and　the　relationship　between　each　water　quality　parameter　and　carbon　emission　in　the　aeration　process　was　obtained.　Second,　a　data-driven　optimization　model　of　energy　consumption　and　carbon　emission　in　the　aeration　process　was　designed　to　obtain　the　optimal　control　strategy　of　aeration　process.　Finally,　the　obtained　low-carbon　optimization　control　strategy　was　applied　to　the　benchmark　simulation　model.　Results　demonstrate　that　the　strategy　can　effectively　track　and　control　the　aeration　process　and　reduce　the　total　energy　consumption　and　carbon　emissions.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.