development of a fault detection algorithm for an alternate aerobic/anoxic cycle nitrogen removal

211 Pages·2017·6.44 MB·English

DEVELOPMENT OF A FAULT DETECTION ALGORITHM
FOR AN ALTERNATE AEROBIC/ANOXIC CYCLE
NITROGEN REMOVAL PROCESS

Dissertation

submitted to and approved by the

Department of Architecture, Civil Engineering and Environmental Sciences
University of Braunschweig – Institute of Technology

and the

Department of Civil and Environmental Engineering
University of Florence

in candidacy for the degree of
Doktor-Ingenieur (Dr.-Ing.) / Dottore di Ricerca in
Civil and Environmental Engineering

by

Emanuele El Basri

born 16.03.1986
from Prato (PO), Italy

Submitted on: 27.02.2017
Oral examination on: 08.05.2017
Professorial advisors: Prof. Stefano Marsili-Libelli
Prof. Hermann G. Matthies
Prof. Claudio Lubello
Prof. Norbert Dichtl

2017

Abstract 3

Abstract
An emerging need in the management of wastewater treatment plants (WWTPs) is
the availability of efficient fault detection (FD) methods to maximize the performance
of the process instrumentation.
This thesis presents a critical appraisal of several differing approaches to the design
and testing of FD algorithms to monitor the instrumentation used in the alternate
aerobic/anoxic cycles (AC) process for nitrogen removal. In this scheme, the nitrogen
removal is achieved by intermittently switching the aeration on and off, thus enabling
the ammonia oxidation in the “on” phase and reducing the oxidized nitrogen in the “off”
phase, provided that enough organic carbon is available.
The first part of the thesis is devoted to the definition of faults and their
parametrization based on simple process indicators. Several factors were selected at the
basis of the diagnosis, involving the slope and the timing of the process measurements
of the various nitrogen species. Then the faults were classified according to their impact
on the process. Gross faults, involving total sensor failure, were then separated from
more subtle malfunctions, like drift and process anomalies. Two distinct detection
algorithms were developed, separating the anoxic and the aerobic phases. Though the
two parts require a separate tuning, both share the same principles: first some low-level
checks are performed on the raw signals, in order to discriminate malfunctions like
missing data, spikes, anomalous constant measurements and irregular duration of the
phases. More sophisticated methods have then been developed to investigate the
presence of anomalies that were not detected by the previous screening.
The fault detection problem has thus been treated either in terms of classification
problem, testing different algorithms based on classification techniques such as binary
trees, support vector machines (SVM) and principal component analysis (PCA), or as a
forecasting problem, using an approach based on the Bayesian theory to predict the
faulty or normal state of the process based on the previous records.
In the PCA-based algorithm, the classification method was improved with respect to
the normal statistical indicators, like the Hotelling’s T2, by introducing variable fault
thresholds and a moving window to keep track of the normal variability of WWTP
operation due to seasonality, varying loading conditions, etc.
In the beginning, operational data obtained from a municipal plant were used to train
the algorithm. However, the information which could be extracted from this dataset was
fairly limited, so in order to set-up a more comprehensive data set an AC model was
developed starting with the standard Benchmark simulation model with improved
4 Abstract

nitrogen kinetics and seasonal temperature variations. Detailed sensor models were also
included, so that the occurrence of faults could be totally controlled, both in kind and
timing, reproducing the anomalies observed in the operational data.
The performance of the various methods was then assessed comparing the anomalies
detected by the algorithm with those actually observed, either in case of actual
operational data, or produced by the simulation model. While the great majority of the
gross failures were successfully detected by the preliminary screening, differing
performances of the subsequent algorithm were obtained, depending on both the data
set and the detection method.
A poor detection performance was observed using the operational data, in part due
to an insufficient characterization of the fault events and in part to the limited number
of signals available. It was observed, instead, that the higher availability of
measurements provided by the numerical model enhances the discrimination
capabilities of the tested methods, especially for on the cubic kernel SVM, while the
moving window PCA (MWPCA) approach and especially the Bayesian predictor,
although less effective, results less affected by a change in the combination of diagnostic
parameters used.
Apart from the numerical analysis on both the operational and simulation data, the
MWPCA method, first developed in Matlab environment, was then ported in LabVIEW
and tested on the alternate aerobic/anaerobic nitrogen removal tanks of the municipal
wastewater treatment plant of Mantua, Italy.
Acknowledgments 5

Acknowledgments
This thesis certainly would not have been possible without the help and support of
many people and I will try here to thank them.
First of all, I want to thank my family, Mohamed, Lia and Marco for their love and
support and most of all for being there. These have been rough years and it is good to
still be all together.
I also want to thank my friends, the old ones that accompany me since elementary
school and those met along the way: Ste, Ila, Lollo, Giggi, Leo, Gaia, Beppe, Vero, Mac,
Ire, Andre, Alessandra, Vale, Ale&Ale, Bea, Lore, Massi...
Thank you to the board of teachers that three years ago allowed me to join this
doctorate and gave me the opportunity experience all of this and to meet so many new
people. Thanks to my supervisors, professor Marsili-Libelli who first pushed me to
apply for this adventure and following closer my work, professor Lubello and professor
Dichtl for the suggestions and professor Matthies for the precious advises and for
hosting me in his institute during the months spent in Braunschweig.
Thanks to all the people with whom I shared the desks and the laughs: first of all to
Matteo and Giacomo, with whom I started this journey in the realm of the fault
detection, my “neighbours” of the LabGOL in Florence, Alessandro, Federica,
Francesco, Guido, Luca and the new arrived guys. Thanks to the colleagues of the WiRe,
Cosima, the true boss of the institute (no offense prof. Matthies), Elmar, Bojana, Noemi,
Oleksy, Jaroslav, Sadiq, Thilo, Ehsan and Joachim.
Thank you to my fellow colleagues of the XXIX doctorate cycle Pina, Dario,
Federica, Vito, Andrea, Stefano, etc., who began with me this adventure, and thank to
the sempai of the previous cycle, Chiara, Valentina, Ilaria, Giovanni, Irene, Alessandro,
Tommaso, etc. that helped me in so many ways during these years.
Finally, I am grateful to all the other people that helped me in any way along the
road, forgive me if I forgot to mention you here, it was not intentional.
Emanuele

Table of contents 7

Table of contents
Abstract ...................................................................................................................... 3
Acknowledgments ...................................................................................................... 5
Table of contents ........................................................................................................ 7
Chapter 1 Introduction ......................................................................................... 11
1.1 Problem statement....................................................................................... 11
1.2 State of the art ............................................................................................. 13
1.3 Main results and contributions .................................................................... 15
Chapter 2 The biological wastewater treatment ................................................... 17
2.1 Wastewater characterization ....................................................................... 18
2.2 Operational configuration of a wastewater treatment plant ........................ 20
2.2.1 Pre-treatments ...................................................................................... 21
2.2.1.1 Screening ......................................................................................... 21
2.2.1.2 Grit removal ..................................................................................... 22
2.2.1.3 Fat and grease removal .................................................................... 22
2.2.2 Primary treatments .............................................................................. 22
2.2.2.1 Flow equalization ............................................................................ 22
2.2.2.2 Primary settling ............................................................................... 23
2.2.3 Secondary treatments .......................................................................... 23
2.2.3.1 Oxidation-Nitrification .................................................................... 23
2.2.3.2 Denitrification .................................................................................. 25
2.2.3.3 Secondary settling ........................................................................... 26
2.2.4 Tertiary treatments .............................................................................. 27
2.2.4.1 Filtration .......................................................................................... 27
2.2.4.2 Disinfection ..................................................................................... 27
2.2.5 Sludge treatment .................................................................................. 27
2.2.5.1 Thickening ....................................................................................... 28
2.2.5.2 Drying .............................................................................................. 28
2.2.5.3 Disposal ........................................................................................... 28
Chapter 3 Characterization of the wastewater treatment structure and operation29
3.1 The alternate cycle processes ...................................................................... 29
3.1.1 The process .......................................................................................... 29
3.1.2 The performances ................................................................................ 31
3.1.2.1 Nitrogen removal ............................................................................. 31
3.1.2.2 Costs savings ................................................................................... 32
8 Table of contents

3.1.3 The control .......................................................................................... 33
3.2 Analysis of the operational data ................................................................. 37
3.2.1.1 Blower switching logic ................................................................... 42
3.3 A modified Benchmark simulation model for fault detection .................... 43
3.3.1 Definition and scope of the Benchmark Simulation Models .............. 44
3.3.1.1 Properties and limits of the earlier activated sludge models ........... 48
3.3.1.2 Properties of the activated sludge model n.3 .................................. 49
3.3.2 A modified long- term benchmark simulation model based on the
activated sludge model n.2 with two-steps nitrification ........................................ 51
3.4 Definition and detectability of faults and anomalies .................................. 65
3.4.1 Fault definitions .................................................................................. 65
3.4.1.1 Gross faults ..................................................................................... 66
3.4.1.2 Finer faults ...................................................................................... 67
3.4.2 Preliminary screening of the gross malfunctions ................................ 69
3.4.3 Trend analysis and parameter extraction ............................................ 71
3.4.3.1 Differing parametrization depending on the available data ............ 71
Chapter 4 Review of methods for the fault detection .......................................... 77
4.1 Fault detection based on the principal component analyisis ...................... 80
4.1.1 Identification of the principal components ......................................... 80
4.1.2 Principal component analysis for dimensionality reduction ............... 82
4.1.2.1 Choice of the number of principal components to retain ................ 82
4.1.2.1.1 Explained variance .................................................................... 83
4.1.2.1.2 Eigenvalue scree plot ................................................................ 83
4.1.2.1.3 Scrambling ................................................................................ 84
4.1.3 Fault detection by principal component analysis using statistical control
charts 85
4.1.3.1 Univariate control charts ................................................................. 85
4.1.3.2 Multivariate control charts .............................................................. 85
4.1.3.2.1 Hotelling’s T² statistic............................................................... 86
4.1.3.2.2 Q statistic .................................................................................. 87
4.1.4 Extensions to the Principal Component Analysis ............................... 89
4.1.4.1 Methods for nonlinear processes based on the principal component
analysis 89
4.1.4.2 Methods for batch processes based on the principal component
analysis 90
Table of contents 9

4.1.4.3 Methods for dynamic processes based on the principal component
analysis 92
4.2 Classifiers based on the Bayes decision theory .......................................... 95
4.2.1 Estimation of probability density functions ........................................ 98
4.2.1.1 Parametric estimation of the probability density functions ............. 99
4.2.1.2 Non-parametric estimation of the probability density functions ... 101
4.3 Binary Decision Trees .............................................................................. 105
4.4 Support Vector Machine ........................................................................... 110
4.4.1 Support vector machine in the linear case ......................................... 110
4.4.1.1 Linearly separable classes ............................................................. 111
4.4.1.2 Non-separable classes .................................................................... 114
4.4.2 Support vector machine for the nonlinear case ................................. 115
Chapter 5 Implementation of the fault detection methods and performance
assessment 119
5.1 Implementation of the preliminary screenings ......................................... 121
5.2 Implementation of the finer check methods ............................................. 123
5.2.1 The real-time mowing window principal component analysis method
123
5.2.1.1 Algorithm initialization ................................................................. 124
5.2.1.2 Threshold definition and optimization .......................................... 124
5.2.2 The Bayesian method ........................................................................ 125
5.2.2.1 Initialization step ........................................................................... 126
5.2.2.2 Priors’ estimation ........................................................................... 127
5.2.2.3 Likelihoods estimation .................................................................. 127
5.2.2.4 Prediction ....................................................................................... 128
5.2.3 The binary classification trees ........................................................... 129
5.2.4 The support vector machine classifiers ............................................. 130
5.2.5 The confusion matrix ......................................................................... 131
5.3 Fault detection using the plant operational data ....................................... 132
5.3.1 Preliminary screening ........................................................................ 132
5.3.2 Test of the real-time mowing window principal component analysis
method 135
5.3.2.1 Fault detection performances ........................................................ 136
5.3.3 Testing of the Bayesian approach...................................................... 139
5.3.4 Comparison between the real-time mowing window principal
component analysis and the Bayesian algorithm. ................................................ 141
10 Table of contents

5.4 Fault detection on synthetic measurements .............................................. 143
5.4.1 Characteristics of the synthetic dataset ............................................. 144
5.4.2 Results of the fault detection using the Bayesian approach .............. 151
5.4.3 Results of the fault detection using the real-time mowing window
principal component analysis method .................................................................. 154
5.4.4 Results of the fault detection using the simple binary tree ............... 162
5.4.5 Results of the fault detection using the medium binary tree ............. 172
5.4.6 Results of the fault detection using the linear support vector machine
method 178
5.4.7 Results of the fault detection using the cubic kernel support vector
machine 182
5.5 Comparative assessment of the of the methods........................................ 188
5.5.1 The naïve Bayesian predictor ............................................................ 189
5.5.2 Comparison of the results for the different classifiers ...................... 190
Chapter 6 Conclusions ....................................................................................... 197
6.1 Key aspects of this research ..................................................................... 202
6.2 Outlook ..................................................................................................... 203
Bibliography .......................................................................................................... 205