• Suardi, M.
• Testa, D.
• Fidanzi, A.
• Guastone, G.
• Millini, R.

Abstract

<jats:title>Abstract</jats:title><jats:p>The energy sector plays a pivotal role in the journey towards achieving net-zero emissions. As one of the largest contributors to greenhouse gas emissions, transforming the energy sector is essential for the climate change goals. The transition involves a shift from fossil fuel-based energy sources to renewable and low-carbon alternatives. Embracing renewable energy technologies such as solar, wind, hydro, and geothermal power is vital for reducing carbon emissions. Carbon capture, utilization, and storage (CCUS) technologies also play a crucial role in mitigating emissions from fossil fuel-based power plants and industrial processes. Collaborative efforts, innovative solutions, and supportive policies are needed to drive this transformation and ensure a sustainable, net-zero emissions future for the energy sector. Artificial Intelligence (AI) is currently playing a primary role in accelerating and de-risking the development of novel technologies for achieving these goals.</jats:p><jats:p>Artificial intelligence can be strategic to accelerate energy industry towards the achievement of net zero emissions with a transversal and integrated approach starting from the introduction of new digital solutions up to their industrialization [1]. In this context, we propose a framework which aims to develop AI solutions that align with the level of awareness and data availability on the basis of the maturity of the development of novel R&amp;D technology. These levels are commonly classified as experimental, prototypal, and demonstrative according to the typically used Technology Readiness Level (TRL) taxonomy [2]. The experimental phase focuses on conducting experiments and gathering data to explore the feasibility and potential of a new technology. The emphasis is on the proof of concept, testing hypotheses, and assessing the fundamental principles underlying the technology. Once the experimental phase has shown to be successful, the technology progresses to the prototypal phase. Here, a functional prototype is developed to validate the design and functionality of the technology. The prototype serves as a tangible representation of the envisioned solution and allows for further testing, refinement, and optimization. Finally, the demonstration phase is characterized by the construction and commissioning of a demonstration plant or system that showcases the technology capabilities on a larger scale. This phase aims to provide evidence of the technology performance, its reliability and potentiality for its commercialization. It involves demonstrating the technology functionality, efficiency, and suitability for real-world applications.</jats:p><jats:p>In this context, this research work proposes a framework to develop data science and AI solutions complying for the TRL of maturity of an R&amp;D technology The proposed approach has been integrated as an established framework accompanying the full lifecycle of the development of novel R&amp;D technologies for achieving the net zero emissions target. The application of this framework has shown to lead to important benefits in R&amp;D initiatives in terms of data enrichment, speed-up, efficiency, and de-risking; the most tangible results were achieved on projects related to the development of new renewable energies, biofuel production and CO2 capture and utilization.</jats:p>

Topics

• impedance spectroscopy
• Carbon
• phase
• experiment
• informatics