- author = "Pezhman Kazemi",
- title = "Data-driven soft-sensors for monitoring and fault diagnosis in wastewater treatment plants",
- school = "Departament d'Enginyeria Quimica, Universitat Rovira I Virgili",
- year = "2020",
- address = "Av. Paisos Catalans, 26, 43007 Tarragona, Spain",
- month = "1 " # oct,
- keywords = "genetic algorithms, genetic programming, ANN, BSM2, Bootstrapping, Anaerobic digestion, Soft Sensor, Neural network, CUSUM chart, Incremental PCA, BSM2, EBLUP, Fault detection, Fault isolation, Time-varying processes, data driven",
-
URL = "
http://hdl.handle.net//10803/670778",
-
URL = "
https://www.tdx.cat/bitstream/handle/10803/670778/TESI%20Pezhman%20Kazemi.pdf",
- size = "184 pages",
-
abstract = "Failing to reach the specific effluent properties in
wastewater treatment plants can adversely affect human
health and environmental. Due to this, there are
significant pressures on authorities for efficient
design and operation of waste water treatment plants
(WWTPs). Therefore, to achieve regulatory standards for
wastewater effluent in a cost-efficient way, the
development of an advanced information framework for
the control and supervision of the WWTPs is mandatory.
For the implementation of this framework, the real-time
measurements of crucial parameters (e.g.,concentrations
of nitrate and total nitrogen, phosphate and total
phosphorus, suspended solids, biochemical oxygen demand
(BOD) and chemical oxygen demand (COD), total volatile
fatty acids (VFA)) a re necessary. Measurement of such
parameters is often associated with capital and
maintenance costs, as well as the time delay.
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.",
-
notes = "In english
Supervisors: Jaume Giralt i Marc and Jean-Philippe Steyer",
