Iterative Learning Control of DO controller for external carbon addition finding
The purpose of this paper is to propose a reduced state-space model driven by the activated sludge model No. 1(ASM1) for a control of sequencing batch reactor(SBR) and two control algorithms for each aerobic and anoxic phase of SBR using the proposed state-space model and iterative learning control (ILC). In this paper, the reduced state-space model from ASM1 is obtained for aerobic and anoxic phase by lumping several components of ASM1 to remove large complexity and strong nonlinearity of ASM1. Dissolved oxygen (DO) control and the amount of external carbon addition are important variable for aerobic phase and anoxic phase of SBR operation, respectively, because DO and amount of external carbon have an important effect on the nutrient removal efficiency and also operational cost. We focus on a development of a control-driven model and two control algorithms that suggest the DO controller of ILC for aerobic phase and find the amount of external carbon addition for anoxic phase. The result in a pilot scale SBR shows that it can systematically control the DO concentration at aerobic phase and can reduce the amount of external carbon addition at anoxic phase.
Iterative Learning Control (ILC) Activated Sludge Model No.1 (ASM1) Sequencing Batch Reactor (SBR) Dissolved oxygen (DO) External carbon
Yeonghwang Kima ChangKyoo Yoo In-Beum Lee
Department of Chemical Engineering,POSTECH,San 31 Hyoja Dong,Pohang,790-784,Korea College of Environmental and Applied Chemistry/Center for Environmental Study,Kyung Hee University,G
国际会议
西安
英文
2007-08-15（万方平台首次上网日期，不代表论文的发表时间）