Application of neural network on the environmental unit operation optimization

• Main Authors: Chen-wen Hsiao, 蕭振文
• Other Authors: Tang-Kai Yin
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/34213947016571989126

碩士 === 嘉南藥理科技大學 === 環境工程與科學系暨研究所 === 93 === This research is to use ability of multi-input and multi-output from artificial neural network for environmental application. It is applied to predict effluent quality in municipal wastewater plant and to simulate the experiment of nitrate removal by zero-valent iron coupled with carbon dioxide bubbling. Neural network contains many kinds of network structures, of which Back-Propagation neural networks was employed in this research. In the case of wastewater treatment plant, it is based on parameters such as the practical operation and influent wastewater quality. The batch operation was used to predict effluent wastewater quality discharging into the river by using neural network. In 2001, water quality data obtained from Montréal wastewater treatment plant was carried out to train the model. According to the raw data of 2002, the model was used to predict effluent wastewater quality by using the input data from the influent quality. Therefore, the optimal model that predicts the operation parameter is useful and easy to operate in the real plant. In the case of chemical treatment process, the data taken from experimental measurement was used to predict the output using the same type of neural network model. The optimal model was conducted to simulate the parameters such as nitrate, ferrous iron and ammonium. The advantage of this study can reduce the cost of material and time for research as well as help design the treatment process. As a result, the normalization could assist to predict performance of model. Moreover, the correlation coefficient can determine the correlation of variables except non-line variables. In addition, the single output model can achieve good prediction. Besides, setting up a single output model can support in comparing with other model structure.