Modeling Nutrient Dynamics in Wastewater Treatment Processes Using Hybrid Kinetic Model and Artifical Neural Network

点击翻译按钮获取中文摘要

Hao 9625183 This is an award to provide support for research aimed at developing an approach to modeling nutrient removal from wastewater by use of suspended growth bioreactors based on coupling the use of artificial neural networks with knowledge of the underlying biological processes that take place within the reactors. The investigators plan on using a hybrid modeling approach that combines a simplified process model and a neural network, the purpose of which is to correct any errors encountered by use of the simplified model. Two hybrid modeling methods involving a series structure and parallel structure will be used to integrate a suspended growth reactor model and a neural network. The approach will be explored for potential use in both sequential batch reactors and in a system for alternating aerobic and anoxic conditions. Results of this research are expected to enhance the procedures by which complicated biological processes can be designed, controlled and optimized. The proposed concepts to be investigated in this project are expected to enhance process control in general and lead to engineering designs that save energy in meeting stringent requirements for removal of nutrients from wastewater prior to their discharge to the environment.