title: A data-driven model for maximization of methane production in a wastewater treatment plant.
abstract: A data-driven approach for maximization of methane production in a wastewater treatment plant is presented. Industrial data collected on a daily basis was used to build the model. Temperature, total solids, volatile solids, detention time and pH value were selected as parameters for the model construction. First, a prediction model of methane production was built by a multi-layer perceptron neural network. Then a particle swarm optimization algorithm was used to maximize methane production based on the model developed in this research. The model resulted in a 5.5% increase in methane production.
authors.affiliation: Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242, USA. andrew-kusiak@uiowa.edu
authors.forename: Andrew
authors.initials: A
authors.lastname: Kusiak
authors.forename: Xiupeng
authors.initials: X
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D000693:Anaerobiosis; D008697:Methane / Q000737:chemistry / Q000378:metabolism*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D014865:Waste Disposal, Fluid / Q000379:methods*
chemical_list: D008697:Methane