The incessant growth in energy demand has resulted in the deployment of renewable energy generators to reduce the impact of fossil fuel dependence. However, these generators often suffer from intermittency and require energy storage when there is over-generation and the subsequent release of this stored energy at high demand. One such energy storage technology that could provide a solution to improving energy management, as well as offering spinning reserve and grid stability, is the redox flow battery (RFB). One such system is the 200 kW/400 kWh vanadium RFB installed in the energy station at Martigny, Switzerland. This RFB utilises the excess energy from renewable generation to support the energy security of the local community, charge electric vehicle batteries, or to provide the power required to an alkaline electrolyser to produce hydrogen as a fuel for use in fuel cell vehicles. In this article, this vanadium RFB is fully characterised in terms of the system and electrochemical energy efficiency, with the focus being placed on areas of internal energy consumption from the regulatory systems and energy losses from self-discharge/side reactions.

Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth.

Social, economic, and political pressures are causing a shift in the global energy mix, with a preference toward renewable energy sources. In order to realize widespread implementation of these resources, large-scale storage of renewable energy is needed. Among the proposed energy storage technologies, redox flow batteries offer many unique advantages. The primary limitation of these systems, however, is their limited energy density which necessitates very large installations. In order to enhance the energy storage capacity of these systems, we have developed a unique dual-circuit architecture which enables two levels of energy storage; first in the conventional electrolyte, and then through the formation of hydrogen. Moreover, we have begun a pilot-scale demonstration project to investigate the scalability and technical readiness of this approach. This combination of conventional energy storage and hydrogen production is well aligned with the current trajectory of modern energy and mobility infrastructure. The combination of these two means of energy storage enables the possibility of an energy economy dominated by renewable resources.