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Abstract The present work focuses on the numerical simulation of Moderate or Intense Low oxygen Dilution combustion condition, using the Partially-Stirred Reactor model for turbulence-chemistry interactions. The Partially-Stirred Reactor model assumes that reactions are confined in a specific region of the computational cell, whose mass fraction depends both on the mixing and the chemical time scales. Therefore, the appropriate choice of mixing and chemical time scales becomes crucial to ensure the accuracy of the numerical simulation prediction. Results show that the most appropriate choice for mixing time scale in Moderate or Intense Low oxygen Dilution combustion regime is to use a dynamic evaluation, in which the ratio between the variance of mixture fraction and its dissipation rate is adopted, rather than global estimations based on Kolmogorov or integral mixing scales. This is supported by the validation of the numerical results against experimental profiles of temperature and species mass fractions, available from measurements on the Adelaide Jet in Hot Co-flow burner. Different approaches for chemical time scale evaluation are also compared, using the species formation rates, the reaction rates and the eigenvalues of the formation rate Jacobian matrix. Different co-flow oxygen dilution levels and Reynolds numbers are considered in the validation work, to evaluate the applicability of Partially-Stirred Reactor approach over a wide range of operating conditions. Moreover, the influence of specifying uniform and non-uniform boundary conditions for the chemical scalars is assessed. The present work sheds light on the key mechanisms of turbulence-chemistry interactions in advanced combustion regimes. At the same time, it provides essential information to advance the predictive nature of computational tools used by scientists and engineers, to support the development of new technologies.

Abstract In Concentrated Solar Power Plants, steam turbines controlled with standard Proportional Integrative Derivative (PID) methods may suffer from performance downgrading in power generation when the steam conditions deviate from nominal ones. An enhancement of standard steam turbine controller can be the key to achieve optimal performance also in non-nominal steam conditions. This paper presents the improvement of the PID control concept by exploiting Fuzzy Logic, an artificial intelligence technique that allows taking into account the human experience and knowledge on the system behavior. A real Concentrated Solar Power Plant has been modeled focusing on generated power control loop, its stability and performance analysis, knowledge useful to design a Fuzzy Inference System. A fuzzy logic controller is proposed to continuously adapt the PID parameters, to improve the steam turbine governor action. Its performance is compared to the classical PID tuned according to three different approaches. The fuzzy logic PID controller extends the simplicity of PID and adapts the control action to actual operating condition by providing the system with a sort of “decision-making skill”. The possibility to design implementable algorithms on a Programmable Logic Controller, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work, through the minimization of the controller complexity with a reduced number of fuzzy sets and fuzzy rules within the fuzzy inference system.

Abstract Small Mediterranean islands are remote, off-grid communities characterized by carbon intensive electricity systems coupled with high energy consuming desalination technologies to produce potable water. The aim of this study is to propose a novel dynamic, multi-objective optimization approach for improving the sustainability of small islands through the introduction of renewable energy sources. The main contributions of our approach include: (i) dynamic modelling of desalination plant operations, (ii) joint optimization of system design and operations, (iii) multi-objective optimization to explore trade-offs between potentially conflicting objectives. We test our approach on the real case study of the Italian Ustica island by means of a comparative analysis with a traditional non-dynamic, least cost optimization approach. Numerical results show the effectiveness of our approach in identifying optimal system configurations, which outperform the traditional design with respect to different sustainability indicators, limiting the structural interventions, the investment costs and the environmental impacts. In particular, the optimal dynamic solutions able to satisfy the whole water demand allow high levels of penetration of renewable energy sources (up to more than 40%) to be reached, reducing the net present cost by about 2–3 M€ and the CO2 emissions by more than 200 tons/y.

Abstract We present a general method, based on controlled static aberrations induced in the reflectors, to boost receiver performances in solar concentrators. Imaging mirrors coupled with dense arrays suffer from severe performance degradation since the solar irradiance distribution is bell-shaped: mismatch losses occur in particular when the cells are series connected. The method consists in computing static deformations of the reflecting surfaces that can produce, for an adopted concentration ratio, a light spot matching the receiver features better than conventional reflectors. The surfaces and the deformations have been analytically described employing the Zernike polynomials formalism. The concept here described can be applied to a variety of optical configurations and collecting areas. As an example, we extensively investigated a dense array photovoltaic concentrator, dimensioned for a nominal power of about 10 kWe. The “flat” distribution of light we obtain can exploit the PV device cells close to their efficiency limit. A significant gain is thus obtained, with no need of secondary optics or complex dish segmentation and of special features in the receiver electrical scheme. In the design, based on seven 2.6 m mirrors, we addressed also non-optical aspects as the receiver and the supporting mechanics. Optical and mechanical tolerances are demonstrated not to exceed accurate, but conventional, industrial standards.

Abstract In order to contribute to the decarbonization of the economy, efficient alternatives to coal and coke should be found not only in the power sector but also in the industrial sectors (like steel, silicon and manganese production) in which coal and coke are used as a reductant and for steel production also as a fuel. To this aim many research works have been focused on the development of a coke substitute based on woody biomass and known as “biocarbon”. There are still barriers to overcome, among them: the biocarbon low density, poor mechanical strength and high reactivity. In this paper a new biocarbon production methodology is proposed, based on: pyrolysis at 600 °C, densification (using pyrolysis oil as binder), reheating of the obtained pellet. Response surface methodology with a Box-Behnken experimental design was utilized to evaluate the effects of the process conditions on the pellet’s quality. Responses showed that densification was mainly affected by oil content and pelleting temperature, while pelleting pressure had a minor influence. The pelleting process has been finally optimized using Derringer’s desired function methodology. Optimal pelletizing conditions are: temperature equal to 60 °C, pressure equal to 116.7 MPa, oil content concentration of 33.9 wt%. These results are relevant for metal production industries at a global level. The identified optimal parameters of the new biocarbon production process can contribute to replace coke with sustainable fuels and probably reduce great part of the related greenhouse gases emissions.

Abstract In recent years, distributed energy systems (DESs) have been recognized as a promising option for sustainable development of future energy systems, and their application has increased rapidly with supportive policies and financial incentives. With growing concerns on global warming and depletion of fossil fuels, design optimization of DESs through economic assessments for short-run benefits only is not sufficient, while application of exergy principles can improve the efficiency in energy resource use for long-run sustainability of energy supply. The innovation of this paper is to investigate exergy in DES design to attain rational use of energy resources including renewables by considering energy qualities of supply and demand. By using low-temperature sources for low-quality thermal demand, the waste of high-quality energy can be reduced, and the overall exergy efficiency can be increased. The goal of the design optimization problem is to determine types, numbers and sizes of energy devices in DESs to reduce the total annual cost and increase the overall exergy efficiency. Based on a pre-established DES superstructure with multiple energy devices such as combined heat and power and PV, a multi-objective linear problem is formulated. In modeling of energy devices, the novelty is that the entire available size ranges and the variation of their efficiencies, capital and operation and maintenance costs with sizes are considered. The operation of energy devices is modeled based on previous work on DES operation optimization. By minimizing a weighted sum of the total annual cost and primary exergy input, the problem is solved by branch-and-cut. Numerical results show that the Pareto frontier provides good balancing solutions for planners based on economic and sustainability priorities. The total annual cost and primary exergy input of DESs with optimized configurations are reduced by 21–36% as compared with conventional energy supply systems, where grid power is used for the electricity demand, and gas-fired boilers and electric chillers fed by grid power for thermal demand. A sensitivity analysis is also carried out to analyze the influence of energy prices and energy demand variation on the optimized DES configurations.

Abstract The methodology, the site and the dataset as well as the emissions scenario considered in the weather file definition influence the numerical evaluation of efficiency measures resilience. With a complete statistical and critical approach, the paper analyzes the importance of these aspects by means of a residential case study simulated in Benevento, a city of south Italy. Using data monitored from 2015 to 2020, a current weather file is built with different methodologies. The comparison indicates that there is not repeatability of the year chosen as a reference for the various months and thus the resolution of the building energy balance could bring different results. Some future climate projections are also generated on medium (2050 s) and long (2080 s) term considering different emission scenarios. With long term projection, the heating degree days are reduced also of − 21% meanwhile the cooling degree days are more than double compared with the current condition. This suggests a remarked transition towards a dominant cooling climate for Benevento. Moreover, when the climate change is considered, the insulation intervention and the installation of double glazed low emissive window is not resilient because the heating energy need decreases also of −56%, but the cooling energy need increases of + 62% (2080 s). If the efficiency measures include also the cool roof and the external shadings, the cooling demand could be reduced until –33% in some scenarios (e.g. RCP 4.5-50th percentile) and increased (+31%) in some others (e.g. 2080 s).

Abstract Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June–July Italian electricity demand for the period 1990–2007. We find a relationship between summer (June–July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach. Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions. This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe.