Adaptive optimal operation of a parallel robotic liquid handling station

Results are presented from the optimal operation of a fully automated robotic liquid handling station where parallel experiments are performed for calibrating a kinetic fermentation model. To increase the robustness against uncertainties and/or wrong assumptions about the parameter values, an iterative calibration and experiment design approach is adopted. Its implementation yields a stepwise reduction of parameter uncertainties together with an adaptive redesign of reactor feeding strategies whenever new measurement information is available. The case study considers the adaptive optimal design of 4 parallel fed-batch strategies implemented in 8 mini-bioreactors. Details are given on the size and complexity of the problem and the challenges related to calibration of over-parameterized models and scarce and non-informative measurement data. It is shown how methods for parameter identifiability analysis and numerical regularization can be used for monitoring the progress of the experimental campaigns in terms of generated information regarding parameters and selection of the best fitting parameter subset.BMBF, 02PJ1150, Verbundprojekt: Plattformtechnologien für automatisierte Bioprozessentwicklung (AutoBio); Teilprojekt: Automatisierte Bioprozessentwicklung am Beispiel von neuen Nukleosidphosphorylase

Techno-economic analysis of a heat pump cycle including a three-media refrigerant/phase change material/water heat exchanger in the hot superheated section for effi cient domestic hot water generation

Integration of a three-media refrigerant/phase change material (PCM)/water heat exchanger (RPW-HEX) in the hot superheated section of a heat pump (HP) system is a promising approach to save energy for domestic hot water (DHW) generation in multi-family houses. The RPW-HEX works as a desuperheater and as a latent thermal energy storage in the system. The latent thermal energy storage is charged during heating and cooling operation and discharged for DHW production. For this purpose, the water side of the RPW-HEX is connected to decentralized DHW storage devices. DHWconsumption, building standards and climate, energy prices, material costs, and production costs are the constraints for the selection of the optimal storage size and RPW-HEX design. This contribution presents the techno-economic analysis of the RPW-HEX integrated into an R32 air source HP. With the aid of experimentally validated dynamic computer models, the optimal sizing of the RPW-HEX storage is discussed to maximize energy savings and to minimize the investment costs. The results are discussed in the context of a return of investment analysis, practical implementation aspects and energetic potential of the novel technology.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768824 (HYBUILD). This work is partially supported by ICREA under the ICREA Academia programme. The authors thank C. Köfinger, M. Lauermann, and A. Zottl for critical discussion. Furthermore, we thank A. Bras and S. Hauer for their assistance in generating the data of the different buildings. The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia

Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design

Especially in biomanufacturing, methods to design optimal experiments are a valuable technique to fully exploit the potential of the emerging technical possibilities that are driving experimental miniaturization and parallelization. The general objective is to reduce the experimental effort while maximizing the information content of an experiment, speeding up knowledge gain in R&D. The approach of model-based design of experiments (known as MBDoE) utilizes the information of an underlying mathematical model describing the system of interest. A common method to predict the accuracy of the parameter estimates uses the Fisher information matrix to approximate the 90% confidence intervals of the estimates. However, for highly non-linear models, this method might lead to wrong conclusions. In such cases, Monte Carlo sampling gives a more accurate insight into the parameter's estimate probability distribution and should be exploited to assess the reliability of the approximations made through the Fisher information matrix. We first introduce the model-based optimal experimental design for parameter estimation including parameter identification and validation by means of a simple non-linear Michaelis-Menten kinetic and show why Monte Carlo simulations give a more accurate depiction of the parameter uncertainty. Secondly, we propose a very robust and simple method to find optimal experimental designs using Monte Carlo simulations. Although computational expensive, the method is easy to implement and parallelize. This article focuses on practical examples of bioprocess engineering but is generally applicable in other fields