The feasibility of using anaerobic baffled reactor （ABR） as onsite wastewater treatment system was discussed. The ABR consisted of one sedimentation chamber and three up-flow chambers in series was experimented unde...The feasibility of using anaerobic baffled reactor （ABR） as onsite wastewater treatment system was discussed. The ABR consisted of one sedimentation chamber and three up-flow chambers in series was experimented under different peak flow factors （PFF of 1 to 6）, superficial gas velocities （between 0.6 and 3.1 cm/hr） and hydraulic retention times （HRT） （24, 36 and 48 hr）. Residence time distribution （RTD） analyses were carded out to investigate the hydraulic characteristics of the ABR. It was found that the PFF resulted in hydraulic dead space. The dead space did not exceed 13% at PFF of 1, 2 and 4 while there was 2-fold increase （26%） at PFF of 6. Superficial gas velocities did not result in more （biological） dead space. The mixing pattern of ABR tended to be a completely- mixed reactor when PFF increased. Superficial gas velocities did not affect mixing pattern. The effects of PFF on mixing pattern could be minimized by higher HRT （48 hr）. The tank-in-series （TIS） model （N = 4） was suitable to describe the hydraulic behaviour of the studied system. The HRT of 48 hr was able to maintain the mixing pattern under different flow patterns, introducing satisfactory hydraulic efficiency. Chemical oxygen demand （COD） and total suspended solids （TSS） removals under all flow patterns were achieved more than 85% and 90%, respectively. The standard deviation of effluent COD and TSS concentration did not exceed 15 mg/L.展开更多
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
基金supported by the Swiss National Centre of Competence in Research (NCCR) North-South:Research Partnerships for Mitigating Syndromes of Global Change, and the Swiss National Science Foundation and the Swiss Agency for Development and Cooperation
文摘The feasibility of using anaerobic baffled reactor （ABR） as onsite wastewater treatment system was discussed. The ABR consisted of one sedimentation chamber and three up-flow chambers in series was experimented under different peak flow factors （PFF of 1 to 6）, superficial gas velocities （between 0.6 and 3.1 cm/hr） and hydraulic retention times （HRT） （24, 36 and 48 hr）. Residence time distribution （RTD） analyses were carded out to investigate the hydraulic characteristics of the ABR. It was found that the PFF resulted in hydraulic dead space. The dead space did not exceed 13% at PFF of 1, 2 and 4 while there was 2-fold increase （26%） at PFF of 6. Superficial gas velocities did not result in more （biological） dead space. The mixing pattern of ABR tended to be a completely- mixed reactor when PFF increased. Superficial gas velocities did not affect mixing pattern. The effects of PFF on mixing pattern could be minimized by higher HRT （48 hr）. The tank-in-series （TIS） model （N = 4） was suitable to describe the hydraulic behaviour of the studied system. The HRT of 48 hr was able to maintain the mixing pattern under different flow patterns, introducing satisfactory hydraulic efficiency. Chemical oxygen demand （COD） and total suspended solids （TSS） removals under all flow patterns were achieved more than 85% and 90%, respectively. The standard deviation of effluent COD and TSS concentration did not exceed 15 mg/L.
基金the Thailand Graduate Institute of Science and Technology(No. TGIST 01-47-038)the National Science and Technology Development Agency(NSTDA) for Ph.D.Scholarship to Mr. Chaiwat Waewsakthe National Research Council of Thailand for research grant under Fiscal Year 2008 Budget to King Mongkut’s University of Technology Thonburi
文摘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.
