Microalgae, microscopic plants ubiqitous in the world's oceans, are nature's very own power cells converting light energy from the sun into chemical compounds. The high biodiversity of microalgae and their adaptation to a wide range of changing environments has resulted in them containing unique suites of compounds. Certain suites of compounds play a key role in protecting the cell against for example the sun's damaging rays. These same compounds have potential to protect humans and could be used in a range of healthcare consumer products. Currently, microalgae are a relatively untapped source of natural products. The heathcare industry are are looking to nature for sustainable alternatives in a range of their personal care products and microalgae have many attributes that make them particularly attractive. Research at Plymouth Marine Laboratory in collaboration with the Boots Company has revealed that certain species of microalgae contain valuable bioactivity including sunscreen protection and antioxidant activity. This project will focus on optimising the yield of these bioactives and on understanding biosynthetic pathways and interconversions. Results from experimental studies will be compared to those derived from mathematical models and will be used to optimise the yield of bioactives in microalgae grown using photobioreactor (PBR) technology, required for commercial scale production of microlagae. We will also investigate the potential of using waste CO2 and NOx emissions to enhance the growth of the microalgae and assess the impact this has on levels of bioactives. Additionally co-product material, remaining after extraction of targeted bioactives, will be investigated as a potential sustainable source of nutrition for farmed fish.

With the fast development of network technology and computing power, a huge amount of data has been generated in almost every aspect of our lives. The International Data Corporation reported that 90 ZB of data will be created each year by 2020, indicating that a big data era is upon us. A typical example is in the energy sector where a large amount of data is generated every day due to smart meter and other digitized changes. These are in turn changing the operation of the energy industry as big data analytics can provide efficient and effective decision support processes. The effect of decentralised generation in the future electricity landscape has and will continue to significantly increase the population of microgrids comprising renewable generation (wind and PV) and battery energy storage supplying local demand, with the excess being exported to the grid. The traditional control design for the energy management system of microgrids is based on a highly simplified model, whose results are highly suboptimal for such a complicated distributed system. Data-driven control could largely improve performance as there is enough data and computing power available today. In addition, energy management systems and market trading optimization packages provided by the big companies are generally designed for large utility and power generation companies and not tailored for smaller prosumers. Given the rapid growth of small prosumers, the PI will develop packages which are tailored to the micro level and meet their individual needs. The PI aims to develop a data-driven intelligent energy management system for a micro grid (connected to a main grid) consisting of wind and solar generation, batteries, and local load in order to provide an integrated, local, smart source of energy. It will use available information (e.g. wind data, weather forecast, energy pricing profile, balancing services pricing etc) to manage the energy generation/utilization and export on site to maximise the financial return to the stakeholder of the microgrid site, and provide balancing services to the System Operator (e.g. my project partner National Grid in the UK). Eventually this will benefit the environment and lead to cheaper energy to the end users due to the improved usage efficiency of renewable energy and the reduced system operation cost.