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The focus of this thesis was to design soft-sensors that can be used besides conventional instrumentation to improve the process operation and safety. Due to the availability of the massive amount of process data in most modern WWTPs, datadriven methods have attracted significant attention. Therefore, in this thesis, we developed different data driven soft-sensors for online prediction of a crucial parameter (for instance, VFA) and fault detection (FD) and diagnosis in WWTPs.
Firstly, we propose different data-driven softsensor for estimating total VFA concentration in the anaerobic digester. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained from the International Water Association (IWA) Benchmark Simulation Model No. 2 (BSM2). In addition, the model robustness was assessed to determine the performance of each soft sensor under different process states.
Second, to prevent failures and serious consequences during the running of the anaerobic digestion (AD) plant, the VFA soft-sensors using different advanced techniques such as SVM, ELM and ensemble of neural network (ENN) are tested and compared in terms of accuracy and robustness for detecting process and instrument faults. To compare the proposed approaches with the traditional FD method, a principal component analysis (PCA) model was also developed. By applying soft-sensors, the residual signal, i.e., the difference between estimated and measured VFA values, can be generated. This residual signal was used in combination with univariate statistical control charts to detect the faults.
Third, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. The contribution of variables is also recursively provided using a complete decomposition contribution (CDC). For the imputation of missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework.
Overall, this thesis presents the application of different data-driven soft-sensors for online prediction and FD in WWTP; it is also shown that they have strong potential for providing support to the operation of water treatment facilities.",
Genetic Programming entries for Pezhman Kazemi