In a Zippe-type 3-pole gas centrifuge, feed gas is introduced through a sonic nozzle into the rarefied region in the rotor. Introduction of the nonrotating feed gas will slow the whirl flow and introduce a secondary r...In a Zippe-type 3-pole gas centrifuge, feed gas is introduced through a sonic nozzle into the rarefied region in the rotor. Introduction of the nonrotating feed gas will slow the whirl flow and introduce a secondary recirculating flow in the meridian plane. The effects of feed gas on the output of a gas centrifuge are investigated. The non-linear. axisymmetric N-S equations are used to calculate the secondary flow induced by the feed gas. Three types of numerical schemes. an implicit scheme similar to the Beam-Warming scheme. an implicit unfactorized scheme and an improved Newton-Raphson scheme are used. The Cohen separation theory with axial variation is used forcalculating the isotope concentration. Optimization of the output is achieved by automatic variation of the weighting factors for a number of linear flow solutions which can be superimposed. A Rome type centrifuge is analyzed as an example. Results show the recirculating flow caused by the feed gas. especially the acceleration loss. has an important effect on the output of a gas centrifuge.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
文摘In a Zippe-type 3-pole gas centrifuge, feed gas is introduced through a sonic nozzle into the rarefied region in the rotor. Introduction of the nonrotating feed gas will slow the whirl flow and introduce a secondary recirculating flow in the meridian plane. The effects of feed gas on the output of a gas centrifuge are investigated. The non-linear. axisymmetric N-S equations are used to calculate the secondary flow induced by the feed gas. Three types of numerical schemes. an implicit scheme similar to the Beam-Warming scheme. an implicit unfactorized scheme and an improved Newton-Raphson scheme are used. The Cohen separation theory with axial variation is used forcalculating the isotope concentration. Optimization of the output is achieved by automatic variation of the weighting factors for a number of linear flow solutions which can be superimposed. A Rome type centrifuge is analyzed as an example. Results show the recirculating flow caused by the feed gas. especially the acceleration loss. has an important effect on the output of a gas centrifuge.
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.
