The purpose of this study is to develop a reliable and effective real-time control strategy by integrating artificial neural network (ANN) process models to perform automatic operation of a dynamic continuous-flow SBR system. The ANN process models, including ORP/pH simulation models and water quality ([NH4+-N] and [NOx--N]) prediction models, can assist in real-time searching the ORP and pH control points and evaluating the operation performances of aerobic nitrification and anoxic denitrification operation phases. Since the major biological nitrogen removal mechanisms were controlled at nitritification (NH4+-N→NO2--N) and denitritification (NO2--N→N2) stages, as well as the phosphorus uptake and release could be completely controlled during aerobic and anoxic operation phases, the system operation performances under this ANN real-time control system revealed that both the aeration time and overall hydraulic retention time could be shortened to about 1.9-2.5 and 4.8-6.2 hrs/cycle respectively. The removal efficiencies of COD, ammonia nitrogen, total nitrogen, and phosphate were 98%, 98%, 97%, and 84% respectively, which were more effective and efficient than under conventional fixed-time control approach.
Development of a real-time control strategy with artificial neural network for automatic control of a continuous-flow sequencing batch reactor
B. C. Cho, S.-L. Liaw, C.-N. Chang, R.-F. Yu, S.-J. Yang, B.-R. Chiou; Development of a real-time control strategy with artificial neural network for automatic control of a continuous-flow sequencing batch reactor. Water Sci Technol 1 July 2001; 44 (1): 95–104. doi: https://doi.org/10.2166/wst.2001.0023
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