title: Modeling the dioxin emission of a municipal solid waste incinerator using neural networks.
abstract: Incineration is considered as an efficient approach in dealing with the increasing demand for municipal and industrial solid waste treatment, especially in areas without sufficient land resources. Facing the concern of health risk, the toxic pollutants emitted from incinerators have attracted much attention from environmentalists, even though this technology is capable of reducing solid waste volume and demand for landfill areas, together with plenty of energy generation. To reduce the negative impacts of toxic chemicals emitted from incinerators, various monitoring and control plans are made not only for use in facilities performance evaluation but also better control of operation for stable effluent quality. How to screen out the key variables from massive observed and control variables for modeling the dioxin emission has become an important issue in incinerator operation and pollution prevention. For these reasons, this study used 4-year monitoring data of an incinerator in Taiwan as a case study, and developed a prediction model based on an artificial neural network (ANN) to forecast the dioxin emission. By doing this, a simplified monitoring strategy for incinerators with regarding to dioxin emission control can be achieved. The result indicated that the prediction model based on a back-propagation neural network is a promising method to deal with complex and non-linear data with the help of statistics in screening out the useful variables for modeling. The suitable architecture of an ANN for using in the dioxin prediction consists of 5 input factors, 3 basic layers with 8 hidden nodes. The R(2) was found to equal 0.99 in both the training and testing steps. In addition, sensitivity analysis can identify the most significant variables for the dioxin emission. From the obtained results, the frequency of activated carbon injection showed as the factor of highest relative importance for the dioxin emission.
authors.affiliation: International Postgraduate Programs in Environmental Management, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand.
authors.forename: Sond
authors.initials: S
authors.lastname: Bunsan
authors.forename: Wei-Yea
authors.initials: WY
authors.lastname: Chen
authors.forename: Ho-Wen
authors.initials: HW
authors.lastname: Chen
authors.forename: Yen Hsun
authors.initials: YH
authors.lastname: Chuang
authors.forename: Nurak
authors.initials: N
authors.lastname: Grisdanurak
mesh_terms: D002947:Cities; D004147:Dioxins / Q000032:analysis* / Q000737:chemistry*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012037:Refuse Disposal; D015203:Reproducibility of Results
chemical_list: D004147:Dioxins