title: Wastewater treatment aeration process optimization: A data mining approach.
abstract: Being water quality oriented, large-scale industries such as wastewater treatment plants tend to overlook potential savings in energy consumption. Wastewater treatment process includes energy intensive equipment such as pumps and blowers to move and treat wastewater. Presently, a data-driven approach has been applied for aeration process modeling and optimization of one large scale wastewater in Midwest. More specifically, aeration process optimization is carried out with an aim to minimize energy usage without sacrificing water quality. Models developed by data mining algorithms are useful in developing a clear and concise relationship among input and output variables. Results indicate that a great deal of saving in energy can be made while keeping the water quality within limit. Limitation of the work is also discussed.
authors.affiliation: Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States.
authors.forename: Ali
authors.initials: A
authors.lastname: Asadi
authors.affiliation: Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States. Electronic address: anoop.verma@wayne.edu.
authors.forename: Anoop
authors.initials: A
authors.lastname: Verma
authors.affiliation: Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States.
authors.forename: Kai
authors.initials: K
authors.lastname: Yang
authors.affiliation: Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States.
authors.forename: Ben
authors.initials: B
authors.lastname: Mejabi
mesh_terms: D000465:Algorithms; D057225:Data Mining; D014865:Waste Disposal, Fluid; D062065:Wastewater
chemical_list: D062065:Wastewater
keywords: Aeration process; Data-driven modeling; Data-mining; Effluents; Energy optimization