Addition of polyaluminiumchloride (PACl) to waste activated sludge to mitigate the negative effects of its sticky phase in dewatering-drying operations.

This paper presents a new application of polyaluminiumchloride (PACl) as a conditioner for waste activated sludge prior its dewatering and drying. It is demonstrated at lab scale with a shear test-based protocol that a dose ranging from 50 to 150 g PACl/kg MLSS (mixed liquor suspended solids) mitigates the stickiness of partially dried sludge with a dry solids content between 25 and 60 %DS (dry solids). E.g., at a solids dryness of 46% DS the shear stress required to have the pre-consolidated sludge slip over a steel surface is reduced with 35%. The salient feature of PACl is further supported by torque data from a full scale decanter centrifuge used to dewater waste sludge. The maximal torque developed by the screw conveyor inside the decanter centrifuge is substantially reduced with 20% in the case the sludge feed is conditioned with PACl. The beneficial effect of waste sludge conditioning with PACl is proposed to be the result of the bound water associated with the aluminium polymers in PACl solutions which act as a type of lubrication for the intrinsically sticky sludge solids during the course of drying. It can be anticipated that PACl addition to waste sludge will become a technically feasible and very effective method to avoid worldwide fouling problems in direct sludge dryers, and to reduce torque issues in indirect sludge dryers as well as in sludge decanter centrifuges.

Assessment of activated sludge stability in lab-scale experiments.

The majority of activated sludge research is conducted in a laboratory environment with, most often, the start-up sludge being taken from a large-scale wastewater treatment plant. Inoculating this sludge in a lab-scale set-up induces a transient period, which, evidently, has a direct impact on the experimental results during this period of acclimatization. In the currently published literature, the acclimatization period is either neglected or fixed to two or three times the sludge age, without any guarantee that stable conditions are indeed reached. To develop a strategy that assesses the stability of activated sludge, three experiments were performed during which the activated sludge was extensively monitored through a series of physical, microscopic and biochemical analyses. It is demonstrated that it is possible to objectively quantify activated sludge stability through the monitoring of the total averaged filament length per image, the sludge volume index and the maximum specific oxygen uptake rate. Hereto, a moving window approach is adopted: within a 7 days interval the mean slope and the gap between the maximum and minimum value has to be smaller than a pre-specified threshold value. Once stability is reached, the true impact of test conditions can be studied without interference of adaptation phenomena.

Efficient tracking of the dominant eigenspace of a normalized kernel matrix.

Various machine learning problems rely on kernel-based methods. The power of these methods resides in the ability to solve highly nonlinear problems by reformulating them in a linear context. The dominant eigenspace of a (normalized) kernel matrix is often required. Unfortunately, the computational requirements of the existing kernel methods are such that the applicability is restricted to relatively small data sets. This letter therefore focuses on a kernel-based method for large data sets. More specifically, a numerically stable tracking algorithm for the dominant eigenspace of a normalized kernel matrix is proposed, which proceeds by an updating (the addition of a new data point) followed by a downdating (the exclusion of an old data point) of the kernel matrix. Testing the algorithm on some representative case studies reveals that a very good approximation of the dominant eigenspace is obtained, while only a minimal amount of operations and memory space per iteration step is required.

Optimal fed batch experiment design for estimation of monod kinetics of Azospirillum brasilense: from theory to practice.

In this paper the problem of reliable and accurate parameter estimation for unstructured models is considered. It is illustrated how a theoretically optimal design can be successfully translated into a practically feasible, robust, and informative experiment. The well-known parameter estimation problem of Monod kinetic parameters is used as a vehicle to illustrate our approach. As known for a long time, noisy batch measurements do not allow for unique and accurate estimation of the kinetic parameters of the Monod model. Techniques of optimal experiment design are, therefore, exploited to design informative experiments and to improve the parameter estimation accuracy. During the design process, practical feasibility has to be kept in mind. The designed experiments are easy to implement in practice and do not require additional monitoring equipment. Both design and experimental validation of informative fed batch experiments are illustrated with a case study, namely, the growth of the nitrogen-fixing bacteria Azospirillum brasilense.

Linearization of the activated sludge model ASM1 for fast and reliable predictions.

In this paper a strategy is proposed to reduce the complexity of the activated sludge model no. 1 (ASM1) which describes the biotransformation processes in a common activated sludge process with N-removal. The key feature of the obtained reduced model is that it combines high predictive value (all state variables keep their biological interpretation) with very low computation time. Therefore, this model is a valuable tool in a risk assessment environment (designed for the evaluation of wastewater treatment plants facing stricter effluent norms) as well as in on-line (MPC) control strategies. The complexity reduction procedure consists of four steps. In the first step representative input/output data sets are generated by simulating the full ASM1 model. In the second step the ASM1 model is rewritten in state space format with linear approximations of the nonlinear (kinetic) terms. In the third step the unknown parameters in the linear terms are identified based on the generated input/output data. To reduce the amount of parameter sets that have to be identified (to cover the full operation range of the plant), a Multi-Model interpolation procedure is introduced as a last step.

Feedback stabilization of fed-batch bioreactors: non-monotonic growth kinetics.

This paper deals with the design of a feedback controller for fed-batch microbial conversion processes that forces the substrate concentration C(S) to a desired setpoint, starting from an arbitrary (initial) substrate concentration when non-monotonic growth kinetics apply. This problem is representative for a lot of industrial fermentation processes, with the baker's yeast fermentation as a well-known example. It is assumed that the specific growth rate mu is function of the substrate concentration only. A first approach exploits the availability of on-line measurements of both the substrate and biomass concentration. A second approach is merely based on on-line measurements of the biomass concentration, which provide an estimate for the specific growth rate. After a reformulation of the substrate concentration setpoint into a specific growth rate setpoint, it is demonstrated that the fed-batch process can still be stabilized around any desired operating point along the non-monotonic kinetics.