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주대성(Dae Sung Joo),최동진(Dong Jin Choi),박희경(Hee Kyung Park) 한국물환경학회 1999 한국물환경학회지 Vol.15 No.1
Currently determining the coagulant dosing rates depends on Jar-test results and the operators experience in many cases. The nature of these practices makes it difficult to timely cope with the rapid fluctuation of raw water quality mainly since it takes relatively long time to obtain Jar-test results. For promptly predicting required coagulant doses in response to water quality change, a number of researchers have attempted to use multi-variable regression (MVR) approach. However, it has been known that the prediction capability of the MVR. approach is not satisfactory. Artificial neural network (ANN) is known as an excellent estimator of nonlinear relationship between the accumulated input and output numerical data. Using this nature of the ANN, this study has attempted to predict the optimal coagulant dosing rate with accuracy and in time. The ANN used in this study consists of an input, a hidden and an output layer. The input and output relationship of a neural network can be nonlinear and linear, and its characteristics are determined by the weights assigned to the connections between nodes in two adjacent layers. Changing these weights will change the input/output behavior of the network. Systematic ways of determining the weights of the network to achieve a desired input/output relationship are referred to as a training or learning algorithm. In this study, quasi-Newton algorithm is used as a training method which is known most efficient for the unconstrained optimization. To train the ANN and deduce the MVR equation, a set of 142 data chosen from the 2-year operation of a water treatment plant was used. Another set of 72 data not used in training was also used to check the prediction capability of the trained ANN and MVR equation. Mean square root error(MSRE) has been used as a quantitative indicator of prediction capability. For the training data set and the raw data set, MSREs of the MVR equation are, respectively, 0.0143 and 0.0193 while those of the ANN 0.0058 and 0.0092, respectively. These results indicate that the ANN reduced the prediction error for the training data by about 59% and for the raw data by about 52%. Therefore, it can be said that the prediction capability of ANN for raw data is enhanced about twice as much as that of MVR. As the advancement of on-line monitoring techniques enables ANN to continuously update the weights periodically, its prediction capability can be also continuously enhanced. However, it is noted that since ANN can not reveal the direct mechanistic relationship between water quality parameters and required coagulant doses, the training data must be prepared correctly in advance to reliably use the ANNs prediction. Further research is necessary for improving correctness of the training data.
최은주(Eun Ju Choi),주대성(Dae Sung Joo),박희경(Hee Kyung Park) 한국물환경학회 2000 한국물환경학회지 Vol.16 No.5
Development of a dynamic SBR simulation model for biological nitrogen removal. based on the Activated Sludge Model No.l, is presented. An experimental study for the calibration and validation of the model was carried out using a pilot scale SBR which treated the wastewater of KAIST. Thirteen water quality components and twenty model parameters are incorporated into this model. In this study, the model components and parameters are evaluated using the simple respirometer. The fractions of organic matter are used for mathematical modelling as the model components. The fractions include readily biodegradable(S_S), inert soluble(S_I), slowly biodegradable(X_S). inert particulate(X_I) substrates and active biomass(X_H) in the wastewater. In this study, the model components and parameters are evaluated using the simple respirometer which measures the biological oxygen consumption rate under well-defined experimental conditions. The fractions of S_S, S_I. X_S. X_I and X_H were 0.1. 0.04. 0.417, 0.11 and 0.28 of TCOD respectively. Also the sensitive model parameters. Y_H(0.53gCOD/gCOD), Y_A(0.208gCOD/gN),_(μH)(2.36/d)._(㎂)(0.936/d), K_(NH)(2.185㎎N/ ℓ), η_g(0.88) and η_n(0.7857) were evaluated using the simple respirometer. Using the SBR model simulation, two operating strategies are suggested. First, the optimization of the total cycle time and the phase distribution is developed in order to minimize the effluent nitrogen concentration. The existing operating cycle for SBR is 8hr/cycle(1.5hr agitation - 3.5hr aeration - 1.5hr agitation - lhr settle - 0.5hr idle). It is thought that extended aeration caused shortage of the carbon source in the anoxic period and high DO concentration at the end of the aeration period. As the result, these are inhibition factors on denitrification in the anoxic period. The simulation result shows that the new cycle phase distribution with shortened cycle time (6hr/cycle, lhr agitation - 2hr aeration - 1.5hr agitation-lhr settle - 0.5hr idle) is more effective than the old one. Secondly, in order to maintain constant DO concentration in the aeration period, step-aeration was suggested. As a result of model simulation, it is thought that the step-aeration accelerates the nitrification and denitrification and reduces the effluent nitrogen compounds concentration.
박희경,오정우,박노석,주대성 대한상하수도학회 1998 상하수도학회지 Vol.12 No.2
It is necessary to calculate the accurate velocity from the hydraulic model for the reliable prediction of water quality changes in water supply system. To verify the hydraulic analysis of the water supply system, fluoride was used as a tracer to calculate the travel time from the injection point to the sampling points. Results from this field experiment indicate that fluoride can be a good conservative tracer while it showed a little longitudinal dispersion along the pipe lines. And the velocity from the model was verified by these travel times and calibrated by changing the ration of the unaccountable water. When the ratio of the unaccountable water was 20%, the error between the estimation of hydraulic model and the real travel time was minimum.
박희경,오정우,박노석,주대성 대한상하수도학회 1998 상하수도학회지 Vol.12 No.3
To use chlorine residual as an surrogate parameter of the water quality change during the transportation in the water distribution system(WDS), the correct prediction model of chlorine residual must be established in advance. This paper shows the procedure and the result of applying the water quality model to the field WDS. To begin with, hydraulic model was calibrated and verified using fluoride as an tracer. And chlorine residual was predicted through simulation of water quality model. This predicted value was compared with the observed value. With adjusting the bulk decay coefficient(kb) and the wall decay coefficient(kw) according to the pipewall environment, the predicted chlorine residual can represent the observed value relatively well.