Impact of key factors on ANNAMOX and SHARON processes in nitrogenous effluent treatment

Due to the environmental hazards, nitrogenous wastewater treatment is essential and important. In recent decades, novel biological processes such as Annamox and SHARON have developed which are cheaper and more effective compared to conventional processes. In the present study, several significant biological parameters such as temperature, pH, dissolved oxygen, organic carbon, salt concentration, nitrite concentration and sludge retention time (SRT) were investigated. The results showed that SHARON process lowered the need for carbon source while Annamox process without carbon source requirement, was implemented in anaerobic condition. The optimum pH for Annamox process was reported 6.7-8.3. Nitrite and salt concentrations were important control parameters to prevent Annamox bacterial activity. Desired temperature for the bacterial growth was 30-40℃  for Annamox and higher than 25℃ for SHARON, and process efficiencies were not directly related to SRT. Overall, the new biological processes of nitrogen removal were described promising due to the decrease in need for aeration and carbon source and are suitable alternatives for conventional processes

Unit Energy Consumption, Production, and Cost of Innovative Treatment Systems of Different Wastewater Streams

Innovative technologies such as micro-sieving, Anammox, and up-flow anaerobic sludge blanket (UASB) hold the key in the sustainable design of Water Resource Recovery Facility (WRRF). In the past, assessment metrics on the effectiveness and economic feasibility of these technologies have not been systematically investigated. According to the twelve design principles of Sustainable Environmental Engineering, Unit energy and cost metrics could provide universal benchmarks in the design of WRRF. Therefore, the objectives of this study are to design innovative WRRF systems to achieve energy positive. These WRRFs were modeled by developing an Excel model to estimate the unit energy metrics. Database of different wastewater quality was developed according to literature data. An Excel model was also developed to estimate the cost due to the energy saving of innovative systems. In treating young, medium, and old leachate, systems with the innovative technologies could save the unit energy consumption by 2.24-4.07 kWh/kg Nremoved and the unit cost by $0.86-2.09/kg CODremoved than conventional technologies. Treatment of young leachate costs less than other leachate in terms of per kg COD removed. Although micro-sieving decreases CH4 production, it reduces the size of the UASB. As a result, micro-sieving could reduce the unit cost by 27$/kg CODremoved, respectively. Therefore, meat processing wastewater can be the most efficiently treated by using innovative technologies due to its high biodegradability. For WWTPs in China, anaerobic-oxic plus anaerobic-anoxic-oxic, oxidation ditch, and sequencing batch reactor were the main technologies. Due to lower energy consumption, SBR is the best technology in small and medium WWTPs in China

Entwicklung eines Fehlerermittlungsalgorithmus für einen alternierenden aerobischen/anoxischen Prozesszyklus zur Entfernung von Stickstoff

This thesis presents a critical appraisal of several differing approaches to the design and testing of fault detection (FD) algorithms monitoring the instrumentation used in the alternated aerobic/anoxic cycles (AC) process for nitrogen removal. Several features are selected as the basis of the FD, involving the slope and the timing of the process measurements of the various nitrogen compounds. Two separate FD algorithms have been developed for the anoxic and the aerobic phases, requiring a separate tuning but sharing the same principles: first some low-level checks are performed on the raw signals, discriminating gross malfunctions like missing data and spikes, then more sophisticated methods are used to investigate the presence of more subtle anomalies that were not detected by the previous screening. The FD problem is treated either in terms of classification problem, testing different algorithms such as binary trees, support vector machines (SVM) and principal component analysis (PCA), or as a forecasting one, using the Bayesian theory to predict the faulty or normal state of the process based on the previous records. An operational data set obtained from a municipal plant was used to first train the algorithm. However, due to the fairly limited information which could be extracted, a more comprehensive data set was created building an AC model based on the standard Benchmark simulation model with improved 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. The performances of the various methods on either the operational and the synthetic datasets have been assessed comparing the anomalies detected by the methods with those actually observed. While the great majority of the gross faults is successfully detected by the preliminary screening, differing performances of the subsequent finer detection are obtained, depending on both the quality of data set and the detection method used: poorer results are observed using the plant data, in part due to an insufficient characterization of the fault events and in part due to the limited number of signals monitored. The higher availability of measurements provided by the numerical model, instead, enhances the discrimination capabilities of the tested methods, especially the nonlinear SVM, while the PCA-based approach and the Bayesian predictor results less affected by a change in the combination of diagnostic parameters used.Diese Dissertation präsentiert eine kritische Würdigung unterschiedlicher Ansätze für den Entwurf und das Testen von Fehlerermittlungsalgorithmen, die die Instrumente für alternierende aerobe/anoxische (AC) Prozeßzyklen zur Stickstoffentfernung überwachen. Als Basis für die Fehlerermittlungsalgorithmen werden verschiedene Merkmale ausgewählt, darunter die Ableitung und das zeitliche Auftauchen von Ereignissen der Prozeßmessungen für unterschiedliche Stickstoffverbindungen. Es wurden zwei separate Fehlerermittlungsalgorithmen für die aeroben und sauerstofffreien Phasen entwickelt, die unterschiedliche Anpassungen erfordern aber die gleichen Prinzipien teilen: zunächst werden einige Überprüfungen der unbearbeiteten Signale auf niedrigem Niveau durchgeführt, die grobe Fehler wie fehlende Daten und Ausreißer anzeigen. Dann werden höher entwickelte Methoden angewandt, um versteckte Anomalien, die vorher nicht erkannt worden waren, zu erkennen. Fehlererkennung wird entweder als Klassifikationsproblem betrachtet, indem verschiedene Algorithmen getestet werden wie binäre Bäume, Support-Vektor-Maschinen (SVM) und Hauptkomponentenanalyse (PCA), oder als ein Vorhersageproblem, für das die Bayes´sche Theorie zur Vorhersage der normal- oder Fehlerzustände der Prozesse auf früheren Aufzeichnungen basieren. Um den Algorithmus zunächst zu trainieren, wurden operationale Daten eines städtischen Betriebes genutzt. Da jedoch nur begrenzte Informationen daraus gewonnen werden konnten, wurde ein umfangreicherer Datensatz geschaffen, indem ein aerobes/anoxisches Modell entwickelt wurde, das auf einem Standard-Benchmarksimulationsmodell mit verbesserter Stickstoffkinetik und saisonalen Temperaturunterschieden beruht. Detaillierte Sensormodelle wurden auch berücksichtigt, so dass das Fehleraufkommen sowohl in Art und Zeit vollständig überwacht werden konnte. Die Leistungen der verschiedenen Methoden wurden bezüglich der tatsächlichen oder synthetischen Datensätze bewertet, indem die Anomalien, die durch die Methoden erkannt wurden, mit den tatsächlich beobachteten verglichen wurden. Während die große Mehrheit grober Fehler durch ein vorläufiges Screening erfolgreich erkannt werden kann, kann man unterschiedliche Leistungen der folgenden genaueren Fehleranalyse beobachten, abhängig sowohl von der Qualität des Datensatzes sowie der benutzten Fehlerermittlungsmethode: werden die Betriebsdaten benutzt, erhält man schlechtere Resultate, was zum Teil an der ungenügenden Charakterisierung der Fehlerereignisse liegt, zum Teil an der begrenzten Anzahl an beobachteten Signalen. Erhält man jedoch mehr Messungen durch das numerische Modell, erhöht dies die Möglichkeiten zur Diskriminierung der getesteten Methoden, dies gilt besonders für die nicht-lineare Support-Vektor-Maschine, während der auf der Hauptkomponentenalyse basierende Ansatz und die Bayes´schen Vorhersageresultate weniger von einer Veränderung der Kombination der benutzen diagnostischen Parameter beeinflußt werden