Research On Modeling Wastewater Treatment Processes And Related Key Technologies

It is important to improve wastewater treatment efficiencies and optimize controling strategies. Because wastewater treatment process is often characterized by time-varability, non-linearity and complexity, it is difficult to operate and control wastewater treatment systems normally. Now water environment is seriously threatened in China. Thus it is both theoretically critical and practically essential to study wastewater treatment process models.Mathematical models must have validity and practicability to ensure their popularization. Following a systematic review for the development of wastewater treatment process models over the last fifty years, this thesis highlighted the need for mathematical models in wastewaetr treatment management and decision making. However, there are disputes between profound model theories and limited ability of model designer. Using wastewater treatment process models are now restricted by two points. One is the absence of short-cut for mechanism model calibration, and the other is the lack of black-box models which work well with both stability and accuracy. Therefore, establishing credible and effective modeling approaches and key technologes will be potential for improvinig the validity and practicability of wastewater treatment process models.The thesis proposed a parameter selection approach for model calibration, the inverse selection approach. The proposed approach selected parameters to be estimated by decreasing the mean square errors (MSE) of model output. The MSE consists of two part, a variance item and a bias item. The variance item is proportional to the variance of measurement random errors and the number of selected parameters, and the bias item is related to the values and sensitivities of model parameters. In the first step, the selection procedure made an assumption that all parameters should be estimated. In following steps, each step removed one parameter which contributed the least to improving model simulating accuracy. Follwing a removed parameter, the variance item was reduced and the bias item was increased. Once the decreased value of the variance item was less than the increased value of the bias item, the selection procedure would be stoped, and the remained parameters would be estimated. Note that the inverse selection calculated the sensitivity with parameter uncertainty, which made the procedure more objective. The inverse selection approach was evaluated with experimental data of a lab-scale SBR equipment. The results showed that the inverse selection approach is valid during calibrating mechanism models.Three hybrid model approaches were proposed to represent the dynamic behavior of wastewater treatment processes. Each hybrid model consists of a mechanism model and an artificial neural network (ANN) model. Those models are the additive parallel model (ASM2d+ANN), the multiplying parallel model (ASM2d*ANN), and the cascade model. In the hybrid models, the ANN submodels are used to simulate the error part of the uncalibrated mechanism model. Using experimental data of a nutrient removal process, the proposed models were evaluated to predict the effluent. The results showed that all three models presented with good prediction as well as fitting. Such good performance was caused by the mechanism model which contributed good extrapolation and the black-box model which shared good adatability. The performance of ASM2d-ANN model was better than the others, which was caused by its cascade structure. The cascade hybrid structure ensure the model accurately described the nonlinear relationship between the output of mechanism model and the measurements. Therefore, the hybrid models is suitable for modeling wastewater treatment.Pure black-box model approaches could also be alternatives for modeling wastewater treatment process. Adaptive-network-based fuzzy inference system (ANFIS), the wavelet network, and the wavelet transform-fuzzy Markov chains approach were discussed here. Firstly, ANFIS models were evaluated by experimental data of wastewter anaerobic treatment process, and an potential optimal input selection procedure was proposed for ANFIS models. ANFIS models based on the potential optimal input selection procedure were found with good performance and generalization. Secondly, A wavelet network model was used to simulate the permeate flux of membrane filtration. The wavelet network is a special neural network with only single hidden layer whose neuron is a wavelet function. The results showed that the wavelet network could be constructed definitely and have an good initialization which leads to a fast convergence. Thirdly, the wavelet transform-fuzzy Markov chains approach was evaluated by predicting influent BOD time series. The time series model showed good predicting precision, and could be improved by increasing fuzzy partition and changing the wavelet type. All the above black-box models showed that the black-box model approach could sufficiently make use of the avaiable data and successfully depict the nonlinear irregularity, so it could be used to model wastewater treatment process.