Identification of Biokinetic Models Using the Concept of Extents.

The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes.

Extent-based kinetic identification using spectroscopic measurements and multivariate calibration.

Extent-based kinetic identification is a kinetic modeling technique that uses concentration measurements to compute extents and identify reaction kinetics by the integral method of parameter estimation. This article considers the case where spectroscopic data are used together with a calibration model to predict concentrations. The calibration set is assumed to be constructed from reacting data that include pairs of concentration and spectral data.

Drift correction in multivariate calibration models using on-line reference measurements.

On-line measurements from first-order instruments such as spectrometers may be compromised by instrumental, process and operational drifts that are not seen during off-line calibration. This can render the calibration model unsuitable for prediction of key components such as analyte concentrations. In this work, infrequently available on-line reference measurements of the analytes of interest are used for drift correction.

Data reconciliation of concentration estimates from mid-infrared and dielectric spectral measurements for improved on-line monitoring of bioprocesses.

Real-time data reconciliation of concentration estimates of process analytes and biomass in microbial fermentations is investigated. A Fourier-transform mid-infrared spectrometer predicting the concentrations of process metabolites is used in parallel with a dielectric spectrometer predicting the biomass concentration during a batch fermentation of the yeast Saccharomyces cerevisiae. Calibration models developed off-line for both spectrometers suffer from poor predictive capability due to instrumental and process drifts unseen during calibration.

Run-to-run control of blood glucose concentrations for people with Type 1 diabetes mellitus.

Run-to-run control has been applied to several traditional batch processes in the chemical industry. The 24-h cycle of eating meals, measuring blood glucose concentrations, and delivering the correct insulin bolus, with the goal of achieving the optimal blood glucose profile, can be viewed in the same spirit as traditional batch processes such as emulsion polymerization. In this paper, we aim to exploit the "repetitive" nature of the insulin therapy of people with Type 1 diabetes.

Optimal operation of batch processes via the tracking of active constraints.

This paper presents a new measurement-based optimization framework for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus the optimal input trajectories can be adjusted using measurements without the use of a model of the process.