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AbstractA common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data‐driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO2 in the off‐gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.

AbstractA common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data‐driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO2 in the off‐gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.

AbstractSmart Grids are electrical grids that require a decentralised way of controlling electric power conditioning and thereby control the production and distribution of energy. Yet, the integration of Distributed Renewable Energy Sources (DRESs) in the Smart Grid introduces new challenges with regards to electrical grid balancing and storing of electrical energy, as well as additional monetary costs. Furthermore, the future smart grid also has to take over the provision of Ancillary Services (ASs). In this paper, a distributed ICT infrastructure to solve such challenges, specifically related to ASs in future Smart Grids, is described. The proposed infrastructure is developed on the basis of the Smart Grid Architecture Model (SGAM) framework, which is defined by the European Commission in Smart Grid Mandate M/490. A testbed that provides a flexible, secure, and low-cost version of this architecture, illustrating the separation of systems and responsibilities, and supporting both emulated DRESs and real hardware has been developed. The resulting system supports the integration of a variety of DRESs with a secure two-way communication channel between the monitoring and controlling components. It assists in the analysis of various inter-operabilities and in the verification of eventual system designs. To validate the system design, the mapping of the proposed architecture to the testbed is presented. Further work will help improve the architecture in two directions; first, by investigating specific-purpose use cases, instantiated using this more generic framework; and second, by investigating the effects a realistic number and variety of connected devices within different grid configurations has on the testbed infrastructure.

AbstractSmart Grids are electrical grids that require a decentralised way of controlling electric power conditioning and thereby control the production and distribution of energy. Yet, the integration of Distributed Renewable Energy Sources (DRESs) in the Smart Grid introduces new challenges with regards to electrical grid balancing and storing of electrical energy, as well as additional monetary costs. Furthermore, the future smart grid also has to take over the provision of Ancillary Services (ASs). In this paper, a distributed ICT infrastructure to solve such challenges, specifically related to ASs in future Smart Grids, is described. The proposed infrastructure is developed on the basis of the Smart Grid Architecture Model (SGAM) framework, which is defined by the European Commission in Smart Grid Mandate M/490. A testbed that provides a flexible, secure, and low-cost version of this architecture, illustrating the separation of systems and responsibilities, and supporting both emulated DRESs and real hardware has been developed. The resulting system supports the integration of a variety of DRESs with a secure two-way communication channel between the monitoring and controlling components. It assists in the analysis of various inter-operabilities and in the verification of eventual system designs. To validate the system design, the mapping of the proposed architecture to the testbed is presented. Further work will help improve the architecture in two directions; first, by investigating specific-purpose use cases, instantiated using this more generic framework; and second, by investigating the effects a realistic number and variety of connected devices within different grid configurations has on the testbed infrastructure.

Computational fluid dynamics (CFD) studies are carried out on a two-bladed vertical axis wind turbine operating at a wind speed of 8 m/s for tip speed ratios (λ) of 0.50–3.0. The blade is the NACA0021 airfoil with chord length 0.265 m and rotor radius 1 m. Basic sensitivity studies for various time step sizes are carried out. The results are validated against available measurement data from the literature. Excellent agreement is obtained for small λ up to optimum condition. For the higher tip speed ratios, the two-dimensional CFD computations predict higher results than the wind tunnel experiment, but they are very similar to the field measurement data. Wake characteristics are presented, showing that the wake becomes Gaussian at 5 times radius downstream of the rotor. It is shown that complex flow phenomena occur owing to dynamic stall onset, especially for the smaller tip speed ratio.

Computational fluid dynamics (CFD) studies are carried out on a two-bladed vertical axis wind turbine operating at a wind speed of 8 m/s for tip speed ratios (λ) of 0.50–3.0. The blade is the NACA0021 airfoil with chord length 0.265 m and rotor radius 1 m. Basic sensitivity studies for various time step sizes are carried out. The results are validated against available measurement data from the literature. Excellent agreement is obtained for small λ up to optimum condition. For the higher tip speed ratios, the two-dimensional CFD computations predict higher results than the wind tunnel experiment, but they are very similar to the field measurement data. Wake characteristics are presented, showing that the wake becomes Gaussian at 5 times radius downstream of the rotor. It is shown that complex flow phenomena occur owing to dynamic stall onset, especially for the smaller tip speed ratio.

A reduction of the inactive components can increase the energy density and reduce production cost of Li‐ion batteries. But an effective reduction of the binder amount also negatively affects the adhesion of the electrode. Herein, slot‐die coating of a primer layer for Li‐ion anodes is investigated. It is shown that the use of a primer layer with only 0.3 g m−2 can increase the adhesive force by the factor of 5 as well as the cell performance for anodes with low binder content. The process limits for a stable, defect‐free primer coating are investigated at coating speeds of up to 550 m min−1. The limits coincide both for a setup without vacuum box and with vacuum box with theory‐based equations. By using a vacuum box, the minimum wet film thickness can be reduced by half.