Applying Academic Excellence to Industrial Efficiency

Can you briefly introduce your role at Veil Energy and your academic background at the University of Padua? How do these two worlds intersect in your work?

I hold a degree in Mechanical Engineering and a PhD in Energy Engineering. I currently teach the courses Machines with Laboratory, Energy Systems, and Design and Optimization of Sustainable Energy Systems at the University of Padua. My collaboration with Veil Energy began about seven years ago and focused on the development of an algorithm to optimize complex energy systems—an algorithm that still forms the basis of the optimizations in E-BOOST. In the following years, I led the development team, contributing to the evolution and technological iterations of the platform. Today, I continue to collaborate with Veil on an ongoing basis, allowing me to bridge the gap between academic research and industrial applications.

What are the main research areas you focus on at the university? How does your academic expertise translate into industrial innovation at Veil Energy?

My research activity focuses on the analysis, modelling, and optimisation of complex energy systems that include generation, storage, and distributed consumption plants. The primary goal is to develop advanced algorithms capable of identifying optimal management strategies from economic, energy and environmental perspectives, as well as to propose technological and system solutions that progressively reduce operational costs and environmental impact.

Multisite industrial environments and buildings in the tertiary sector — like those at the core of the E-BOOST architecture — represent ideal use cases for applying these methods. Furthermore, academic research allows me to stay constantly updated on the most advanced technologies and models, ensuring rapid and effective knowledge transfer between academia and industrial applications. This approach is designed to deliver tangible solutions that are centred on innovation and the continuous enhancement of clients’ energy performance.

Can you describe a project or initiative where collaboration between Veil and the university delivered concrete results?

Among the various innovative projects that found practical application, I would like to highlight two that I followed with great interest.

The first of these is the development and implementation of a real-time energy management tool to support an industrial paint shop. The necessity for this change arose after the installation of a photovoltaic system rendered the traditional management model obsolete. The system uses field data collection technologies, industrial automation, optimisation algorithms, and integration with energy markets to continuously control a cogeneration plant and meet electricity and thermal energy demand at the lowest possible cost. The results were evident from the outset, with dynamic and adaptive management in response to solar production fluctuations, leading to average energy cost savings of over 9%.

The second project employed an advanced method for optimising multi-energy systems, capable of designing the entire energy infrastructure of a site “from the ground up,” including the conversion, storage, and distribution of multiple energy carriers. This approach was applied to the design of a new low-impact hotel complex composed of several buildings and enabled the identification of solutions that simultaneously optimised cost, emissions, and energy independence. The resulting configurations were markedly different from those achievable through traditional methods and introduced truly innovative elements to the tertiary building sector.

In addition to their practical value, both projects resulted in national and international scientific publications, demonstrating the strength and value of the dialogue between academic research and industrial innovation.

How did your academic background in energy systems influence the way E-BOOST was architected, particularly in terms of data modeling and machine-level insights?

Without the expertise gained through my academic background and the continuous updating required for research and teaching, I would not have been able to contribute so significantly to the design of E-BOOST. The platform was developed through the combination of in-depth knowledge of energy systems and a solid engineering approach. This resulted in the creation of a comprehensive, effective and scalable tool that can adapt to a range of industrial contexts.

Furthermore, the scientific approach positively influenced the platform’s development. Rather than focusing on a single operational issue, the goal was to design transversal, generalisable functionalities capable of addressing various types of energy systems, even those very different from one another. E-BOOST has evolved from a robust monitoring system into a comprehensive decision-making tool, facilitating industrial-scale energy efficiency and transformation.

E-BOOST combines real-time monitoring with predictive intelligence — can you tell us more about how the algorithms were designed to evolve with industrial complexity and operational variability?

Standard operating conditions allow for the accurate description of energy systems through physical models based on thermodynamics and engineering principles. However, as complexity increases or under abnormal conditions — such as imminent failures or sudden load variations — these models often become insufficient or too slow to provide timely responses. In addition, external factors such as market volatility and the intermittent nature of renewable sources can have a significant impact on management decisions.

In order to address these challenges, E-BOOST has integrated hybrid algorithms from the outset, combining traditional physical models with machine learning. This approach enables real-time responses that take into account the variability and evolution of operating conditions. The development of such algorithms required advanced skills in fast-optimised physical modelling and in designing adaptive systems capable of recognising patterns and anomalies in continuously evolving data.

What are some of the challenges in aligning academic research timelines with the faster-paced needs of industrial applications?

It is a commonly held view that academic research tends to proceed more slowly than industrial innovation. In reality, this perception fails to acknowledge that university research commences from less established premises, addressing unresolved problems and frequently developing technologies that are not yet in existence. The objective is not only to identify applicable solutions, but to do so in a systematic manner, developing robust, generalisable, and enduring approaches.

From my perspective, today’s industrialised solutions represent the natural evolution – and often the first concrete milestone – of a longer journey of scientific experimentation. In this sense, industrial innovation is an extension of research, and collaboration between universities and businesses is the engine that allows this innovation to accelerate, solidify, and generate real impact.

In which areas of energy management do you see the greatest potential for innovation through academic-industry collaboration in the next 5 years?

Energy innovation should be focused on reducing environmental impact at the lowest possible economic and social cost. This involves the strategic replacement of conventional systems with renewable energy sources, primarily solar power. It also encompasses the reduction of final consumption, as every kWh saved eliminates the need for new production.

Improving efficiency does not necessarily require replacing systems with more high-performing, often expensive, and physically limited technologies. Significant results can be achieved by leveraging synergies between existing components — for example, recovering waste energy to power other processes. This approach necessitates integrated and mindful management of the entire system.

It is precisely with this systemic logic that E-BOOST has proven particularly effective, as demonstrated by the experience with the industrial paint shop previously mentioned.

In summary, optimising energy flows systemically is currently one of the most intelligent and effective ways to improve efficiency and sustainably integrate new technologies.

From your perspective, how important is cross-disciplinary knowledge (engineering, economics, policy) in solving today’s industrial energy challenges?

In the energy sector, a multidisciplinary approach is not just preferable, it is essential. All innovations must be technically sound, economically viable, compliant with regulations, and acceptable to their target market if they are to be truly effective.

At E-BOOST, we have integrated functionalities that reflect this vision. These include algorithms for automatically generating energy reports in compliance with current regulations, and modules connected to energy markets, which are constantly updated.

In my academic work, I collaborate with researchers from different disciplines on projects related to price spike resilience, energy market dynamics, and models for sharing costs and incentives in renewables. Simultaneously, I am responsible for the coordination of a cross-disciplinary project that involves students from multiple faculties. The objective of this project is to develop tangible solutions to address climate change through the innovation of energy sources.