Anna Katrine Vangsgaard

Sensitivity analysis of autotrophic N removal by a granule based bioreactor: Influence of mass transfer versus microbial kinetics

A comprehensive and global sensitivity analysis was conducted under a range of operating conditions. The relative importance of mass transfer resistance versus kinetic parameters was studied and found to depend on the operating regime as follows: Operating under the optimal loading ratio of 1.90(gO(2)/m(3)/d)/(gN/m(3)/d), the system was influenced by mass transfer (10% impact on nitrogen removal) and performance was limited by AOB activity (75% impact on nitrogen removal), while operating above, AnAOB activity was limiting (68% impact on nitrogen removal). The negative effect of oxygen mass transfer had an impact of 15% on nitrogen removal. Summarizing such quantitative analyses led to formulation of an optimal operation window, which serves a valuable tool for diagnosis of performance problems and identification of optimal solutions in nitritation/anammox applications.

An operational protocol for facilitating start-up of single-stage autotrophic nitrogen-removing reactors based on process stoichiometry

Start-up and operation of single-stage nitritation-anammox sequencing batch reactors (SBRs) for completely autotrophic nitrogen removal can be challenging and far from trivial. In this study, a step-wise procedure is developed based on stoichiometric analysis of the process performance from nitrogen species measurements to systematically guide start-up and normal operation efforts (instead of trial and error). The procedure is successfully applied to laboratory-scale SBRs for start-up and maintained operation over an 8-month period. This analysis can serve as a strong decision-making tool to take appropriate actions with respect to reactor operation to accelerate start-up or ensure high-rate N removal via the nitritation-anammox pathway.

Development of novel control strategies for single-stage autotrophic nitrogen removal: A process oriented approach

Abstract The autotrophic nitrogen removing granular sludge process is a novel and intensified process. However, its stable operation and control remain a challenging issue. In this contribution, a process oriented approach was used to develop, evaluate and benchmark novel control strategies to ensure stable operation and rejection of disturbances. Three novel control strategies were developed, evaluated, and benchmarked against each other: a feedforward control (control structure 1 – CS#1), a rule-based feedback control (CS#2), and a feedforward–feedback controller, in which the feedback loop updates the set point of the feedforward loop (CS#3). The CS#1 gave the best performance against disturbances in the ammonium concentration, whereas the CS#2 provided the best performance against disturbances in the organic carbon concentration and dynamic influent conditions. The CS#3 rejected both disturbances satisfactorily. Thus, the appropriate design will depend on the specific disturbances in the influent generated in the upstream units of the wastewater treatment plant.

pH variation and influence in an autotrophic nitrogen removing biofilm system using an efficient numerical solution strategy

A pH simulator consisting of an efficient numerical solver of a system of nine nonlinear equations was constructed and implemented in the modeling software MATLAB. The pH simulator was integrated in a granular biofilm model and used to simulate the pH profiles within granules performing the nitritation-anammox process for a range of operating points. The simulation results showed that pH profiles were consistently increasing with increasing depth into the granule, since the proton-producing aerobic ammonium-oxidizing bacteria (AOB) were located close to the granule surface. Despite this pH profile, more NH3 was available for AOB than for anaerobic ammonium oxidizers, located in the center of the granules. However, operating at a higher oxygen loading resulted in steeper changes in pH over the depth of the granule and caused the NH3 concentration profile to increase from the granule surface towards the center. The initial value of the background charge and influent bicarbonate concentration were found to greatly influence the simulation result and should be accurately measured. Since the change in pH over the depth of the biofilm was relatively small, the activity potential of the microbial groups affected by the pH did not change more than 5% over the depth of the granules.

A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

Abstract This contribution explores the use of diagnosis and control modules based on fuzzy set theory and logic for bioreactor monitoring and control. With this aim, two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control system was designed for a complete autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation. Additionally, the diagnosis tool was demonstrated by analysis 100 days of experimental data.

Control of a Biological Nitrogen Removal Process in an Intensified Single Reactor Configuration

Abstract The nitrogen removing granular sludge process is a novel and intensified process. However, its stable operation and control remains a challenging problem. In this contribution, a new process oriented approach is used to develop, evaluate and benchmark control strategies to ensure stable operation and rejection of disturbances. Three control strategies were developed: a feedforward control (case 1), a rule-based feedback control (case 2), and a feedforward-feedback controller, in which the feedback loop updates the set point of the feedforward loop (case 3). The case 1 controller, based on influent measurements, was giving the best performance against disturbances in the ammonium concentration, whereas case 2 was providing the best performance against disturbances in the organic carbon concentration. The case 3 controller rejected both disturbances satisfactorily. Thus, this controller provided versatility towards disturbance rejection, however through a less tight control, which meant a bigger offset from the removal efficiency.