Study on the use of NADH fluorescence measurements for monitoring wastewater treatment systems.

Fluorescence measurement of intracellular nicotinamide adenine dinucleotide (NADH) provides information about the physiological response of microbes towards changing conditions in their environment and has been suggested to be useful for the control of wastewater treatment plants. In this study, the practical usefulness of such measurements was evaluated from batch experiments with a commercially available NADH sensor in a bench scale reactor. The sensor was linear in the NADH concentration, robust, almost maintenance free, and hardly sensitive to floc size distribution.

Full-scale application of the SHARON process for treatment of rejection water of digested sludge dewatering.

At the Rotterdam Dokhaven WWTP the first full-scale application of the SHARON process has been constructed. In the SHARON process, rejection water from dewatering of digested sludge is treated for N-removal. It concerns a highly active process operating without sludge retention.

An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances: Part II.

There is a need for efficient modeling strategies which quickly lead to reliable mathematical models that can be applied for design and optimization of (bio)-chemical processes. The serial gray box modeling strategy is potentially very efficient because no detailed knowledge is needed to construct the white box part of the model and because covenient black box modeling techniques like neural networks can be used for the black box part of the model. This paper shows for a typical biochemical conversion how the serial gray box modeling strategy can be applied efficiently to obtain a model with good frequency extrapolation properties.

An efficient model development strategy for bioprocesses based on neural networks in macroscopic balances.

In the serial gray box modeling strategy, generally available knowledge, represented in the macroscopic balance, is combined naturally with neural networks, which are powerful and convenient tools to model the inaccurately known terms in the macroscopic balance. This article shows, for a typical biochemical conversion, that in the serial gray box modeling strategy the identification data only have to cover the input-output space of the inaccurately known term in the macroscopic balances and that the accurately known terms can be used to achieve reliable extrapolation. The strategy is demonstrated successfully on the modeling of the enzymatic (repeated) batch conversion of penicillin G, for which real-time results are presented.

Linear constrain relations in biochemical reaction systems III. Sequential application of data reconciliation for sensitive detection of systematic errors.

This article presents a method to test the presence of relatively small systematic measurement errors; e.g., those caused by inaccurate calibration or sensor drift.