Biological activated carbon (BAC) filtration is an important treatment step in the production of drinking water especially if drinking water is produced from surface water. The performance and processes within a BAC filter have been of interest for researchers since the 1980's, mainly because of its ability to remove natural organic matter known as disinfection precursors. A malfunction of one of the pre-treatment steps might affect the feed water quality into the BAC filters. The main objective of this study was to determine the immediate response of the BAC filters to a rapid change in feed water quality. It was shown that with the studied setup it was possible to compare the effect of different pre-treatment steps and subsequent different water qualities on the performance of the BAC filters on the long term adaptation. However, especially the immediate response was not studied in detail before. All filters were able to mitigate a sudden change in feed water quality, either through improved adsorption or increased activity of the biomass on the filter. As a result of this resilience against sudden changes, it is therefore concluded that there is no direct need for very stringent on-line monitoring and continuous adjustments of the feed water quality of the BAC filters. The addition of phosphate resulted in the lowest dissolved organic carbon (DOC) concentration in the effluent of the BAC filters. In this study the influence of intact cells in the feed water on the performance of the BAC filters was shown to be limited.

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This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods.

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