本研究利用類神經網路（artificial neural networks）模擬焚化系統中戴奧辛（dioxins）排放與焚化操作參數、進料組成間之關係，其終極目的是求得最佳操作區，以確定最低之戴奧辛排放。 研究方法針對蒐集之實驗資料以類神經網路分三階段進行模擬：（一）架構最佳之類神經網路，（二）完成最佳類神經網路之學習與測試，（三）探討最佳焚化操作區。 研究進行以一商業運轉之流體化床式焚化系統及水牆式焚化系統為對象，利用Neuralworks Professional Ⅱ/Plus軟體分別進行模擬。 模擬結果顯示：流體化床式焚化系統最佳操作條件為焚化爐頂部及鍋爐溫度分別控制在909℃和253℃，其戴奧辛排放值為6.9 ng-TEQ/Nm3。水牆式焚化系統最佳操作則為上部輻射室溫度及煙道氣溫度控制在800℃及210℃，戴奧辛排放量為133 ng/Nm3。 This study, utilizing artificial neural networks, correlates the emission of dioxins to the feed compositions and operating conditions of some incinerators. The ultimate concern is to search for the optimum operating conditions so that minimum dioxin emission values are obtained. Three successive stages were carried out to the designed purposes, namely, (1) finding the best neural networks for incinerators under study, (2) completing the learning and testing of the proposed network structure and (3) locating the optimum operating conditions. Both a commercial scale fluidized-bed and a water cooled incinerators are subjected to the simulation using Neuralworks Professional II/Plus developed by Neural Networks. Results indicate that the lowest dioxin emissions(at a value of 6.9 ng-TEQ/Nm3) occur if the temperatures are controlled at 909℃ and 253℃ for the furnace top and boiler out, respectively, for fluidized-bed incinerator; while the minimum dioxin emissions(at the value of 133 ng/Nm3) occur provided that the flue gas temperature is controlled at 210℃ and the furnace top(radiation zone) operated at 800℃ in the case of water cooled incinerator.
