The study of neural network-based controller for controlling dissolved oxygen concentration in a sequencing batch reactor
Document Type
Article
Publication Date
1-1-2006
Abstract
The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) level of an activated sludge system by manipulating the air flow rate. The NN inverse model-based controller with the model-based scheme represents the controller, which relies solely upon the simple NN inverse model. In the IMC, both the forward and inverse models are used directly as elements within the feedback loop. The hybrid NN control consists of a basic NN controller in parallel with a proportional integral (PI) controller. Various simulation tests involving multiple set-point changes, disturbances rejection and noise effects were performed to review the performances of these various controllers. From the results it can be seen that hybrid controller gives the best results in tracking set-point changes under disturbances and noise effects.
Keywords
sequencing batch reactor, dissolved oxygen, direct inverse neural network control, internal model control, hybrid neural network control, activated-sludge process, water treatment-plant, waste-water, removal, identification, model.
Divisions
fac_eng
Publication Title
Bioprocess and Biosystems Engineering
Volume
28
Issue
4
Publisher
Springer
Additional Information
018HB Times Cited:0 Cited References Count:24