Novel intelligent systems to assist the energy transition and improve sustainability and people's life can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which led to the emergence of the concept of "smart campus" over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart regions.

In this paper, the design of an Organic Rankine Cycle (ORC) is optimized by means of numerical simulations. The systems of interest are the subcritical and transcritical thermodynamic cycles.Optimizations are performed with the objective of determining the design that maximizes the specific power output. The design variables include the operating parameters (pressures, mass flow rates), and the best working fluid is determined by comparing the performance of 36 refrigerants. Optimization runs are performed for a wide range of geofluid temperatures (from 80 to 180°C), and for a wide range of condenser temperature (from 0.1 to 50°C). The results are summarized in charts that may be used as efficient tools for designing optimal geothermal power plants. Finally, an approximate analysis allowed to develop new correlations for predicting the maximal specific power output of an ORC.

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