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Development of sustainable energy is a pivotal step towards solutions for today's global challenges, including mitigating the progression of climate change and reducing dependence on fossil fuels. Biofuels derived from agricultural crops have already been commercialized. However the impacts on environmental sustainability and food supply have raised ethical questions about the current practices. Cyanobacteria have attracted interest as an alternative means for sustainable energy productions. Being aquatic photoautotrophs they can be cultivated in non-arable lands and do not compete for land for food production. Their rich genetic resources offer means to engineer metabolic pathways for synthesis of valuable bio-based products. Currently the major obstacle in industrial-scale exploitation of cyanobacteria as the economically sustainable production hosts is low yields. Much effort has been made to improve the carbon fixation and manipulating the carbon allocation in cyanobacteria and their evolutionary photosynthetic relatives, algae and plants. This review aims at providing an overview of the recent progress in the bioengineering of carbon fixation and allocation in cyanobacteria; wherever relevant, the progress made in plants and algae is also discussed as an inspiration for future application in cyanobacteria.

Predictive Analytics and Machine Learning (ML) are at the heart of some of the most popular Industry 4.0 applications such as condition-based monitoring, predictive maintenance, and quality management. To support the implementation of such use cases, various ML models have been proposed and validated in the research literature. This paper introduces a novel set of machine learning algorithms for Industry4.0 use cases, namely the QARMA algorithms, which are capable of mining of quantitative rules. QARMA models present several advantages when compared to conventional ML and Deep Learning mechanisms, including computational performance, predictive accuracy and "explainability". In the scope of this paper, we discuss these advantages based on practical experiences from the field deployment and validation of QARMA models in two different production lines. The deployment has been supported by a state-of-the-art Industrial Internet of Things platform, which is also presented in the paper.

Abstract The present paper is based on the work carried out in the framework of the European FP7 project MAXSIMA, in which MYRRHA safety studies are performed. MYRRHA is a pool-type 100 MWth system with MOX fuel designed to operate both in ADS and critical modes. It uses lead-bismuth eutectic (LBE) as primary coolant. The MOX fuel has almost the same density as the LBE coolant. In case of pin failure fuel pellets may break into chunks and particles carried by the coolant upwards and redistributed in the reactor pool. The transmutation group at IKET/KIT mainly with the numerical analysis tool is involved for studying severe accidents for MYRRHA reactor design. The highlight of the current work is that 3D simulations with explicit modelling on the gaps between fuel assemblies and 3D macroscopic pin bundle models are performed for the first time using a reactor safety analysis code, SIMMER-IV, with 3D geometry. In this paper, the numerical analyses are conducted for a single fuel assembly blockage and 19 pin-rods on basis of an LBE coolant experiment. The 3D analysis has been applied with both scales namely fuel assembly scale and pin bundle scale. For the fuel assembly scale, the evaluation of a single fuel assembly blockage using non-axisymmetric geometry configuration in the subcritical mode is addressed. For the pin bundle scale, the 3D pin bundle simulations show a good agreement from the experiment conducted at KALLA liquid metal laboratory. Note that this is the only code applied by now for blockage analyses after pin failure in MYRRHA. The current work has formed a solid basis for the safety analysis for MYRRHA in the future.

The goal of this theoretical study is to illustrate the potential of three different MgB2 tapes, developed by Columbus Superconductors, for application in cylindrical coils. First, the distribution of critical currents and electric fields of individual turns is compared when the winding of the model coil is made with tapes having different Ic(B) and anisotropy values. Second, the influence of the winding geometry on basic parameters of cylindrical coils which consist of a set of pancake coils, such as critical current Icmin, central magnetic field B0 and stored energy E, is analysed. The winding geometry of the coils, i.e. the outer winding radius and the coil length, with the same inner winding radius, was changed from a disc shape to a long thin solenoid in such a way that the overall tape length was held constant, and considered as a parameter. Finally, the winding cross-section of the coil is optimized with respect to the constant tape length in order to reach the maximum central field. The results of calculations show that for a given overall tape length and inner winding radius there exists only one winding geometry which generates the maximum central field. The overall tape length, as a parameter, is changed in a broad range from 500 m to 10 km. All calculations were performed using the experimental data measured at 20 K while the effect of the anisotropy in the Ic(B) characteristic of the short samples is taken into account.

Abstract This paper explores two different blisk dynamic models for resonant vibration prediction of a rotating blisk test piece, i.e. the Model-BDTID and GMM. The former represents a mistuned blisk model with blade mistuning pattern experimentally retrieved by a recently proposed blade mistuning identification method based on blade detuning tests (BDTID). It falls into the scope of the frequency-mistuning modeling approach. The latter refers to a geometrically mistuned model constructed upon high-precision blisk geometry data by leveraging the advanced optical geometry measurement technology. A specifically developed ‘Sector Mode Assembling Reduction Technique’ is exploited for efficient dynamic analyses of the large-sized GMM. Forced response tests are performed in a spinning rig under well-controlled laboratory condition. The Blade Tip-Timing technique is employed to give all-blade vibration measurements of the rotating blisk. Correlation results between the forced response predictions to BTT measurements demonstrate that both the Model-BDTID constructed upon the identified blade mistuning of the blisk at rest and the GMM, can predict the resonant vibration of the rotating blisk with satisfactory accuracy.

The recently published quinoxaline/thiophene-based polymer TQ1 has been modified on its acceptor unit, either altering the acceptor strength by incorporating a pyridopyrazine, substitution of the acceptor-hydrogens by fluorine, or substitution of the alkoxy side chain by alkyl. The changes in physical, electronic and device properties are discussed. For the polymers incorporating the stronger acceptors a decreased performance is found, where in both cases the current in the devices is compromised. Incorporation of the pyridopyrazine-based acceptor seems to result in more severe or additional loss mechanisms compared to the polymer that incorporates the fluorine atoms. A similar performing material is obtained when changing the alkoxy side chain in TQ1 to an alkyl, where the solar cell performance is mainly improved on the fill factor. It is demonstrated that the standard TQ1 structure is easily modified in a number of ways, showing the versatility and robustness of the standard TQ1 structure and synthesis.

The accelerated penetration of distributed energy resources (DER), most notably renewable distributed generation (DG) like wind and solar, in distribution networks (DNs) lead mainly to voltage and congestion issues. The unavoidable transition of passive DNs toward smart grids calls for fast-deployment of cost-effective flexible options, both grid controllable assets (e.g. on-load tap changer transformers) and DER (e.g. DG, storage batteries and flexible loads), to enable distribution system operators (DSOs) in maintaining reliable operation. To this end, this paper proposes a novel centralized stochastic multi-period AC optimal power flow (S-MPOPF) model allowing DSOs to procure at minimum cost from DER ancillary services for voltage control and congestion relief in medium voltage DNs. The main novelty lies on the joint optimization of a comprehensive set of flexible options. The full-flexibility options model is formulated as a mixed-integer nonlinear programming (MINLP) problem while some combinations of flexible options lead to simpler NLP problems. Furthermore, a meaningful approximation of MINLP problem as an NLP is explored. The results obtained for the optimal operation of a modified 34-bus benchmark DN on 24 hours demonstrate the relative effectiveness of various flexible options as well as that off-the-shelf MINLP and NLP solvers require generally low computation effort.