Academic and research departmentsCentre for Environment and Sustainability.
Raimondo is a Material Engineering graduate who has specialised in numerical methods and programming with a problem-solving orientation. He is using these skills to develop his expertise in sustainability activities such as Circular Economy solutions, Corporate Social and Environmental Responsibility and Life-Cycle Thinking. He is adept at using applied mathematics and Data Analysis for Sustainable Engineering.
IoT to support Circular Economy Business Model in the Infrastructure sector
The project partner is Costain Group Plc.
The focus of the research is to define how IoT solutions and Circular Economy (CE) principles can work together to generate economic value. From this understanding, it will be possible to shape specific Circular Business Models and strategies for the different Costain sectors and in different situations.
The research gap emerged so far is that the synergy between CE and IoT technology is not formalised yet. There is a general understanding that the digital solutions can leverage sustainable practices as the CE, however, the way to do it has not been clarified and formalized yet.
This mainly because the CE is born as a theoretical and academic concept, whereas the IoT definition come first from practical and industrial solutions and the two fields struggle to integrate each other.
How can digital solutions and the IoT be designed to support Sustainability? And how can the value creation process of CE help the IoT solution to be cost-effective?
To answer these questions, as the first step it is necessary to have a clear holistic view of the two topics and then structure frameworks concerning their value generation process and identifying what the common themes are. To do this, the research will be divided into two parts. The former therefore will focus on the theoretical definition of all the aspects that combine to give the definition of a business model and that are related to the IoT and CE concepts. The latter will focus on practical application analysing case studies.
The next step of the research will be to clarify how these and other possible drivers can be integrated with Circular Business Models in the different Costain sectors.
When the general framework has been defined, it will be necessary to engage with Costain’s stakeholders and IoT innovation projects to collect data from practical implementation and test the insight of the research. In this regard, two Costain innovation projects can be analysed initial examples form Costain’s innovation projects, such as:
REVIS: the project has the scope to monitoring air pollution in urban and industrial areas with smart sensors. In this way, a software of artificial intelligence will be able to give suggestions as to the best and least polluted roads, to flag the abnormal emissions, pinpoint their sources and to give suggestion to how the air quality of the area can best been improved. The business model and the criticality of this project will be studied to understand how a project with strong environmental benefits can also generate economic value for possible customers and what type of customer relationship is most suitable.
IREAP: Placing a variety of sensors, the project will monitor the performances of industrial buildings. The data gathered will enable the optimization the building’s consumptions, and leads to an improvement in the comfort and the productivity of the workers. In this kind of project, the IoT sensors will generate data that are directly useful to improve sustainable performances of the building. It is necessary to quantify these benefits both in term of evaluating the value of the solution and to improve the facility management.
Following this method, similar IoT-related projects in the different sectors can be evaluated to understand how they can drive CE and which are the best guidelines for design and their implementation.
Ameduri, S., Ciminello, M., Dimino, I., Catignani, A., & Mancinelli, R. (2019). Archive of Mechanical Engineering.
An optimal sensor placement methodology is implemented and herein proposed for SHM model-assisted design and analysis purposes. The kernel of this approach analysis is a genetic-based algorithm providing the sensor network layout by optimizing the probability of detection (PoD) function while, in this preliminary phase, a classic strain energy approach is adopted as well established damage detection criteria. The layout of the sensor network is assessed with respect to its own capability of detection, parameterized through the PoD. A distributed fiber optic strain sensor is adopted in order to get dense information of the structural strain field. The overall methodology includes an original user-friendly graphical interface (GUI) that reduces the time-to-design costs needs. The proposed methodology is preliminarily validated for isotropic and anisotropic elements.