• Energy Research

Abstract Environmental problems have offered new challenges to the energy production sector, starting from the need of increasing the efficiency of thermodynamic systems to preserve fuel consumption. Accordingly, multi-energy generation systems are among the most efficient generation systems, and Heat Recovery Steam Generators (HRSGs) represent key components of such systems. In this regard, this paper shows a comprehensive approach to optimize a HRSG by using a multi-objective Genetic Algorithm (GA). The aim is to highlight how to determine the optimal values of geometrical and thermodynamic design variables according to two objective functions: the global costs to be minimized and the steam turbine output power to be maximized. Starting from a reference HRSG design, thermodynamic modeling and simulations as well as the optimization procedure are performed in MATLAB®. A validation is carried out to show the accuracy of the modeling approach. Latin Hypercube Sampling is applied to create a uniform sample to select the design variables based on a global sensitivity analysis, producing a significant reduction of computational efforts. Then, the GA optimization is performed to achieve the Pareto front, collecting the best trade-off design solutions. Economic savings up to 20% are achieved limiting the HRSG size.

Abstract Environmental problems have offered new challenges to the energy production sector, starting from the need of increasing the efficiency of thermodynamic systems to preserve fuel consumption. Accordingly, multi-energy generation systems are among the most efficient generation systems, and Heat Recovery Steam Generators (HRSGs) represent key components of such systems. In this regard, this paper shows a comprehensive approach to optimize a HRSG by using a multi-objective Genetic Algorithm (GA). The aim is to highlight how to determine the optimal values of geometrical and thermodynamic design variables according to two objective functions: the global costs to be minimized and the steam turbine output power to be maximized. Starting from a reference HRSG design, thermodynamic modeling and simulations as well as the optimization procedure are performed in MATLAB®. A validation is carried out to show the accuracy of the modeling approach. Latin Hypercube Sampling is applied to create a uniform sample to select the design variables based on a global sensitivity analysis, producing a significant reduction of computational efforts. Then, the GA optimization is performed to achieve the Pareto front, collecting the best trade-off design solutions. Economic savings up to 20% are achieved limiting the HRSG size.

The current World situation is particularly complex and heavy, from many points of view, among which energy and socio-economic ones. The COVID-19 pandemic, besides causing a health crisis, has brought out, even more, the need to reduce emissions for a healthier environment. The role played by the existing building stock is fundamental, as it is responsible for 36% of CO2 emissions in Europe. The proposed work aims to define some energy refurbishment interventions, for a residential building in south Italy, regarding the building envelope, the heating and cooling systems, and the addition of systems from renewable energy sources in order to improve its energy labeling. The methodological approach has followed the guidelines for the energy certification of buildings in Italy. It was possible to evaluate the improvement in building energy labels according to the proposed efficiency measures, highlighting those which allow for a tax relief recently introduced by the Government. Finally, the addition of photovoltaic and solar collector systems was evaluated to allow even the fulfillment of nZEB standard.

The current World situation is particularly complex and heavy, from many points of view, among which energy and socio-economic ones. The COVID-19 pandemic, besides causing a health crisis, has brought out, even more, the need to reduce emissions for a healthier environment. The role played by the existing building stock is fundamental, as it is responsible for 36% of CO2 emissions in Europe. The proposed work aims to define some energy refurbishment interventions, for a residential building in south Italy, regarding the building envelope, the heating and cooling systems, and the addition of systems from renewable energy sources in order to improve its energy labeling. The methodological approach has followed the guidelines for the energy certification of buildings in Italy. It was possible to evaluate the improvement in building energy labels according to the proposed efficiency measures, highlighting those which allow for a tax relief recently introduced by the Government. Finally, the addition of photovoltaic and solar collector systems was evaluated to allow even the fulfillment of nZEB standard.

Abstract Finned heat sinks equipped with phase change materials to store and release large amounts of thermal energy and metal foams to increase overall thermal conductivity are a promising solution for heat transfer surfaces thermal management. These devices need to be properly designed in order to achieve an optimal trade-off between fundamental objectives like cost minimization and operation time maximization. Nowadays there are no studies available that figure out how to combine design variables to reach these goals. In the present study, a mathematical model based on volume-averaged porous media equations is employed to predict surface operation time. After comparisons with literature data, multi-objective Pareto optimization of such devices is performed with a genetic algorithm because of the large explored domain of solutions referred to heat sink geometry and foam morphology. The results show that the derived Pareto front covers operation time and device cost between about 2000–6000 s and 200–275 € per device, respectively, while the optimum according to the utopia criterion features an operation time of 4000 s with a cost of about 225 € per device. Data presented here can be a very useful tool to design such devices for heat transfer management in an optimized form.

Abstract Finned heat sinks equipped with phase change materials to store and release large amounts of thermal energy and metal foams to increase overall thermal conductivity are a promising solution for heat transfer surfaces thermal management. These devices need to be properly designed in order to achieve an optimal trade-off between fundamental objectives like cost minimization and operation time maximization. Nowadays there are no studies available that figure out how to combine design variables to reach these goals. In the present study, a mathematical model based on volume-averaged porous media equations is employed to predict surface operation time. After comparisons with literature data, multi-objective Pareto optimization of such devices is performed with a genetic algorithm because of the large explored domain of solutions referred to heat sink geometry and foam morphology. The results show that the derived Pareto front covers operation time and device cost between about 2000–6000 s and 200–275 € per device, respectively, while the optimum according to the utopia criterion features an operation time of 4000 s with a cost of about 225 € per device. Data presented here can be a very useful tool to design such devices for heat transfer management in an optimized form.

Hospitals represent the most energy-intensive buildings in most of the World, mainly because of strict requirements for micro-climatic control and high energy needs for equipment. Thus, the effective energy design and/or retrofit of these buildings can yield huge energy, environmental and economic benefits. In this frame, the study investigates the energy retrofit of a hospital building, i.e., the “D” Pavilion of the “A. Cardarelli” hospital facility in Naples (South Italy). The building is modeled in EnergyPlus and the energy model is calibrated by comparison against measured data, provided by an annual experimental campaign. Hence, in order to address building energy retrofit, a rigorous cost-optimal analysis is conducted by coupling EnergyPlus and MATLAB®. The investigated energy retrofit measures (ERMs) are addressed to the energy systems and to the exploitation of renewable energy sources. ERMs for the building envelope are excluded because they are not effective for the considered case study. The optimization allows to explore a comprehensive domain of retrofit scenarios, and thus, finally, robust cost-optimal solutions are found in correspondence of different discount rates. These solutions yield substantial reductions of primary energy consumption, global cost and polluting emissions with payback periods around 4 years.

Hospitals represent the most energy-intensive buildings in most of the World, mainly because of strict requirements for micro-climatic control and high energy needs for equipment. Thus, the effective energy design and/or retrofit of these buildings can yield huge energy, environmental and economic benefits. In this frame, the study investigates the energy retrofit of a hospital building, i.e., the “D” Pavilion of the “A. Cardarelli” hospital facility in Naples (South Italy). The building is modeled in EnergyPlus and the energy model is calibrated by comparison against measured data, provided by an annual experimental campaign. Hence, in order to address building energy retrofit, a rigorous cost-optimal analysis is conducted by coupling EnergyPlus and MATLAB®. The investigated energy retrofit measures (ERMs) are addressed to the energy systems and to the exploitation of renewable energy sources. ERMs for the building envelope are excluded because they are not effective for the considered case study. The optimization allows to explore a comprehensive domain of retrofit scenarios, and thus, finally, robust cost-optimal solutions are found in correspondence of different discount rates. These solutions yield substantial reductions of primary energy consumption, global cost and polluting emissions with payback periods around 4 years.