복합형 신경망을 이용한 화성폐수처리 공정 모델링

홍성훈, 이대성*, 박종문**

포항공과대학교 화학공학과, 캠브리지대학교 화학공학과*, 포항공과대학교 화학공학과/환경공학부**

Hybrid Neural Network Modeling of a Full-scale Coke Wastewater Treatment Process

Sung Hun Hong, Dae Sung Lee*, Jong Moon Park**

Dept. of Chem. Eng., POSTECH, Dept. of Chem. Eng., University of Cambridge*, Dept. of Chem. Eng./School of Environ. Eng., POSTECH**

Introduction

In recent years, hybrid neural network approaches, which combine mechanistic models and neural networks, have received considerable interest[1-3]. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this study, different modeling strategies were applied to a full-scale coke wastewater treatment process in order to develop a reliable process model in minimal time and with minimal cost. The primary motive was to give operators and process engineers a guideline which would allow them to arrive at the optimum operational strategy and minimize damage to the operating unit as shock loadings of toxic compounds in the influent often cause process instability, which can lead to the death of the effective organisms and the leakage of organic carbon. Initially, process data analysis was performed upon actual operational data using Principal Component Analysis. Secondly, a simplified mechanistic model and neural network model were developed based on specific process knowledge and operational data of the coke wastewater treatment process, respectively. Finally, a neural network was incorporated into the mechanistic model in both parallel and serial configurations.

Coke wastewater treatment process

The coke wastewater treatment plant (CWTP) in the Pohang Iron and Steel Co., Korea, is a conventional activated sludge unit as is shown in Fig. 1. The hydraulic retention time of the CWTP was approximately 2.7 days. Five-months (156 days) of operational data was collected by sampling the settled sludge and the influent and effluent streams of the CWTP at eight-hour intervals. This sampling campaign resulted in 466 operational data sets in total. Fig. 2 shows a score plot of the collected data in the first two principal component plane. As can be seen from the figure, most of the normal operating data points were contained within a control contour with a 95% confidence interval. However, when a process instability occurred, the projected process data points progressively moved outside the ellipse, and then returned to their normal positions after the system had recovered from the damage (from process data points 250 to 279). From operation data analysis, we found that the process upset was caused by a shock loading of cyanide in the influent over a period of several days.

Mechanistic model

A mechanistic model for the CWTP was developed on the basis of ASM1[4]. For the reactor system shown in Fig. 1, the mass balances for process variables in the reactor were developed to predict the concentrations of effluent components. For practical reasons minor mechanisms were neglected and only the known major mechanisms were taken into account.

Neural network model

A feed-forward back-propagation neural network (FBNN), which is by far the most frequently applied in chemical/biological processes, was employed in this study. A generalized delta-rule with momentum and adaptive learning rate was used for the training process with the aid of MATLAB off-line (The MathWorks, 1998). A sigmoid function was chosen as the activation function. From the errors of both recall of the training data sets and generalization of the validating data sets, the best network structure was determined (8 nodes in the input layer, 5 nodes in the hidden layer, 4 nodes in the output layer).

Serial hybrid model

A neural network in the serial hybrid model was used to model inaccurately known kinetic values of the mechanistic model. The schematic representation of the serial hybrid neural model is given in Fig. 3. The inputs to the neural network were Q(t); the influent concentrations of COD(t), phenol(t), and cyanide(t); the effluent concentrations of COD(t) and cyanide(t); and MLSS(t) and pH(t) in the last reactor. From the results of sensitivity analysis, the outputs of the neural network were chosen as the following three kinetic parameters: □H, YH and □CN. The parameter values so obtained were considered as constants between sampling points and fed into the mechanistic model. In the serial hybrid model, the main problem was the training of the neural network. Since the target values of the neural network were unknown process parameters, the standard error-back-propagation algorithm could not be applied directly. In this case, the plant could be regarded as an additional layer of neural network parts and the output errors of the plant were simply propagated back through the plant.

Parallel hybrid model

In the parallel hybrid model structure a neural network was used to model the difference between the mechanistic model predictions and the corresponding operational data (i.e. the residuals). The schematic diagram of the parallel hybrid model structure is shown in Fig. 4. The influent wastewater quantities were fed to both the neural network and the mechanistic model. These inputs included Q(t), COD(t), cyanide(t) and phenol(t). pH(t) in the reactor and the residuals, e(t) of MLSS, COD, and cyanide were only fed to the neural network. The network outputs were the residuals e(t+1) of MLSS, COD, SS, and cyanide. By comparing the performance of network configurations in the recall and generalization process, we found that the hidden-layer with five neurons gave the best results.

Results and discussion

Four different strategies were used for the modeling of a working industrial wastewater treatment process: the simplified mechanistic model, the neural network model, the serial hybrid neural model, and the parallel hybrid neural model. The parallel hybrid model, which combined the mechanistic model and the neural network in parallel configuration, gave the best performance (Fig 5). Major process variables were predicted with good accuracy, even in the case of a process upset caused by the shock-loading of toxic compounds. This indicated that the neural network captured sufficient residual information to compensate for the inaccuracy of the mechanistic model. In this case study, the parallel hybrid modeling strategy gave a reasonable model in a comparatively short development time with a limited amount of data, in a situation where a mechanistic model with relatively high structural and parameter uncertainty was the only one available. In practice, this is a feature of most industrial wastewater processes, and typically, no detailed knowledge of the process or of its response to external disturbances exists. Therefore, we believe that the parallel hybrid modeling strategy should be widely applied to complex and time-varying biological systems to develop cost-effective and reliable process models.

References

1. Psichogios, D. C. and Ungar, L. H. AIChE J., 38, 1499 (1992).

2. Thompson, M. and Kramer, M. A. AIChE J. 40, 1328 (1994).

3. Zhao, H., Hao, O. J. and McAvoy, T. J. J. Environ. Eng. 123 (4), 311 (1997).

4. IAWPRC Scientific and Technical Report No.1. IAWPRC. London, England (1987).

Fig. 2. Score plot of operation data.

Fig. 1. Schematic diagram of a coke wastewater treatment plant.

Fig. 3. Schematic diagram of serial hybrid neural model.

Fig. 4 Schematic diagram of parallel hybrid neural model.

Fig. 5. Simulated and measured concentrations in the coke wastewater treatment plant (-: simulated, x: measured).