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Microbiological Characteristics of Anaerobic Granular Sludge in Hybrid Anaerobic Baffled Reactor
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作者 刘荣荣 石光辉 +3 位作者 田晴 杨波 管荣辉 陈季华 《Journal of Donghua University(English Edition)》 EI CAS 2010年第5期700-704,共5页
Anaerobic granular sludge is of key importance for highly effective operation of hybrid anaerobic baffled reactor(HABR).An observation and analysis on the composition of anaerobic granular sludge in each separation co... Anaerobic granular sludge is of key importance for highly effective operation of hybrid anaerobic baffled reactor(HABR).An observation and analysis on the composition of anaerobic granular sludge in each separation compartment of HABR was conducted by using scanning electron microscope(SEM)and molecular biotechnology,and specific methanogenic activity(SMA)and coenzyme F420 content were determined.It was indicated that the disparity of microbial composition was significant among these separation compartments of HABR,and the HABR encouraged phase separation.The results show the understanding of microbiological characteristics of anaerobic granular sludge in HABR is helpful for cultivating granular sludge,which ensures the effective operation of the reactor. 展开更多
关键词 hybrid anaerobic baffled reactor(HABR) anaerobic granular sludge microbiological characteristic specific methanogenic activity(SMA) coenzyme F420
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Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production 被引量:8
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作者 Chaiwat Waewsak Annop Nopharatana Pawinee Chaiprasert 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2010年第12期1883-1890,共8页
Based on the developed neural-fuzzy control system for anaerobic hybrid reactor (AHR) in wastewater treatment and biogas production, the neural network with backpropagation algorithm for prediction of the variables ... Based on the developed neural-fuzzy control system for anaerobic hybrid reactor (AHR) in wastewater treatment and biogas production, the neural network with backpropagation algorithm for prediction of the variables pH, alkalinity (Alk) and total volatile acids (TVA) at present day time t was used as input data for the fuzzy logic to calculate the influent feed flow rate that was applied to control and monitor the process response at different operations in the initial, overload influent feeding and the recovery phases. In all three phases, this neural-fuzzy control system showed great potential to control AHR in high stability and performance and quick response. Although in the overloading operation phase II with two fold calculating influent flow rate together with a two fold organic loading rate (OLR), this control system had rapid response and was sensitive to the intended overload. When the influent feeding rate was followed by the calculation of control system in the initial operation phase I and the recovery operation phase III, it was found that the neural-fuzzy control system application was capable of controlling the AHR in a good manner with the pH close to 7, TVA/Alk 〈 0.4 and COD removal 〉 80% with biogas and methane yields at 0.45 and 0.30 m^3/kg COD removed. 展开更多
关键词 anaerobic hybrid reactor influent feed flow rate neural-fuzzy control system process response
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