首页|Researchers at Department of Civil Engineering Report New Data on Artificial Int elligence (Optimizing Aerobic Granular Sludge Process Performance: Unveiling the Power of Coupling Experimental Factorial Design Methodology With Artificial ... )
Researchers at Department of Civil Engineering Report New Data on Artificial Int elligence (Optimizing Aerobic Granular Sludge Process Performance: Unveiling the Power of Coupling Experimental Factorial Design Methodology With Artificial ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ar tificial Intelligence. According to news reporting out of Toronto, Canada, by Ne wsRx editors, research stated, "This research explored innovative approaches, in tegrating artificial intelligence (AI) and design of experiments, to enhance the performance of the aerobic granular sludge (AGS) process in wastewater treatmen t. A hybrid model coupling artificial neural networks and random forests (ANN-RF ) with response surface methodology (RSM) via central composite design (CCD) and Box-Behnken design (BBD) was developed to improve the optimization process." Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC) Alliance International Grants.
TorontoCanadaNorth and Central Ameri caArtificial IntelligenceEmerging TechnologiesMachine LearningDepartment of Civil Engineering
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