We present an integrated framework for the online optimal experimental re-design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro-kinetic differential equation model for Escherichia coli fed-batch processes after 6 h of cultivation. The system includes two fully-automated liquid handling robots; one containing eight mini-bioreactors and another used for automated at-line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re-designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re-computation of the optimal experiment are proven by a 50-fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610-619. © 2016 Wiley Periodicals, Inc.

The online redesign of experiments for parameter determination of nonlinear dynamic systems has been studied recently by different research groups. In this article, this technique is assessed in a real case study for the first time. The presented algorithm adopts well-known concepts from model-based control. Compared to previous studies, special attention is given to the efficient treatment of the underlying nonlinear and possibly ill-conditioned parameter estimation and experiment design problems. These problems are solved with single shooting and gradient-based nonlinear programming (NLP) solvers. We use an initial value solver, which generates first- and second-order sensitivities to compute exact derivatives of the problem functions. As a special feature, we propose the integration of a local parameter identifiability analysis and a corresponding algorithm that generates well-conditioned problems. The practical applicability is demonstrated by experimental application to a chromatography column system where A, D, and E optimal experiments are performed

Thermal storages are part of highly integrated energy systems. The development of accurate and reduced models is critical for efficient simulations on a system-level and the analysis of the storage design, control, and integration. We present the experimental analysis and numerical modeling of a lab-scale shell and tube latent heat thermal energy storage (LHTES) unit with a (latent) storage capacity of about 10–15 kWh. The phase change material (PCM) is a high density polyethylene (HD-PE) with phase change temperatures between 120 and 135 °C. An efficient 2D numeric storage model is derived which accounts for design and material parameters of PCM, storage, and heat transfer fluid (HTF). Different probability distribution functions are used to model the PCM apparent specific heat capacity. From these functions the state of charge (SOC) can be predicted, which indicates the extent to which a LHTES is charged relative to storeable latent heat. Model predictions are fitted to experimental data from thermophysical measurements and from LHTES operation with partial and full charging/discharging. The storage model agrees well with experimental results. However, thermosphysical material analysis and storage operation indicated that the temperature range of phase transition is noticeable affected by storage loading operating condition, i.e., heating and cooling rates, which is not considered in the model. With this simplification it turns out that the model is limited by the quality of prediction of internal storage PCM temperatures.