• Membrane Journal
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• v.28 no.1
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• pp.31-36
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• 2018

The purpose of this study is to investigate the changes of sludge characteristics according to the changes of influent sewage flowrate in the advanced wastewater treatment processes including MBR, SBR, and $A_2O$. The ratio of the actual sewage flowrate to the design flowrate is decreased from 100% to 70, 40%, and 10%, and the specific denitrification rate and ammonia oxidation (nitrification) rate was measured. The specific nitrification rate of the sludge collected from the aeration tank of each process was measured at a similar value ($0.10gNH_4/gMLVSS/day$) in all three process under the condition of 100% of sewage flowrate. It has tended to decrease significantly as the sewage flowrate decreased from 70% to 40%. The specific denitrification rate was also decreased by ~50% as the sewage flowrate decreased. However, considering the total nitrogen concentration in the influent and the microbial concentration in the reactor, the changes in kinetic parameter did not affect overall nitrogen removal. Therefore, it can be concluded that stable nitrogen removal will be possible under low influent flowrate condition if the MLVSS concentration is kept high.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1255-1262
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• 2012

Look into the nuclear power plant of Wolsong currently, it is controlled in order to required operating pressure with PI controller. PI controller has a simple structure and satisfy design requirements to gain setting. However, It is difficult to control without changing the gain from produce changes in parameters such as loss of the valves and the pipes. To solve these problems, the dynamic change of the PI controller gain, or to compensate for the PI controller output is desirable to configure the controller. The aim of this research and development in the parameter variations can be controlled to a stable controller design which is reduced an error and a vibration. Proposed PI/NN control techniques is the PI controller and the neural network controller that combines a parallel and the neural network controller part is compensated output of the controller for changes in the parameters were designed to be robust. To directly evaluate the controller performance can be difficult to test in real processes to reflect the characteristics of the process. Therefore, we develope the simulator model using the real process data and simulation results when compared with the simulated process characteristics that showed changes in the parameters. As a result the PI/NN controller error and was confirmed to reduce vibrations.