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Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.

Changes in the main weather features are becoming perceivable in Italy. In the last years, the average temperature and the intensity of rainstorms are increasing. Such phenomena could lead to changes in the earth-atmosphere interaction. Accounting for the high vulnerability of the Italian territory, an obvious consequence would concern the effects on the hydro-geological hazards, including landslides and floods. As a matter of fact, the higher frequency of high magnitude flood and landslide events represent an alarm bell. It is necessary to explore with all available instruments the consequences of the potential climate changes. Since quantitative predictions are not possible, the only way to run is depicting rational scenarios for the most vulnerable contexts adopting a multidisciplinary approach. Based on projections of potential climate changes in the 21st century, the report examines their potential impact in the context of the Italian peninsula. After a general overview about typical slope responses to weather changes, some quantitative scenarios have been depicted for representative geomorphological contexts. The last part of the paper examines changes in the expected risk, based on the characteristics of the Italian territory and its vulnerability, looking at the initiatives to undertake for its mitigation.

The electrical resistance of Ni 50 Ti 30 Hf 20 and Ni 50 Ti 40.5 Hf 9.5 alloys during Stress Assisted Two Way Memory Effect was investigated with the aim to deduce the relationship between electrical resistance and deformation across the transformation range. Results show an almost linear relationship although a crossing point between increasing and decreasing deformation may compromise the use of these alloys actuators.

The passivation of iron and steel (DIN 1623) was studied by integral and depth selective conversion electron Mossbauer spectroscopy and Auger electron spectroscopy. Thickness and phase composition of the passive layer formed in sulphate solution and in a phosphate buffer were determined in dependence on the anodic potential and the duration of the passivating procedure. The experimental results lead to the conclusion that not the whole oxidic layer is responsible for the passivity but only a very thin intermediate layer formed between the cubic substrate and the rhombic oxide (γ-FeOOH) cover.

AbstractCumulus‐intact and ‐denuded unfertilized oocytes from two mouse strains were exposed to 1.5 m ethanol (EtOH) or two cryoproteclant solutions, 1.5 M propanediol (PROH) or 1.5 M dimethylsulfoxide (DMSO), for 4.5 min at 27°C, and the proportion of activating or degenerating oocytes studied. Exposure to DMSO did not significantly increase activation above that of oocytes not exposed to DMSO. Treatment of oocytes in PROH resulted in the activation of up to 87% of viable oocytes. This was significantly higher (P <01) than in control oocytes and comparable to the rate of activation after treatment with EtOH (59–96% activation). In solutions at 1°C, 47% of control oocytes were activated, which was not significantly different from the rate of activation in EtOH (36%) or PROH (50%) at 1°C. Following treatment with PROH, up to 87% of oocytes degenerated within a period of 6 h in vitro. The age of the oocytes (h post hCG) and the time of cumulus removal with the enzyme hyaluronidase, relative to the time of exposure to the chemicals, influenced the level of degeneration in most groups. Significantly fewer oocytes degenerated when cumulus cells were removed before treatment (0–31%) than when the cumulus was left intact throughout the treatment and 6 h culture period (10–87%). Exposure to PROH at 1°C reduced oocyte degeneration to 5%. We conclude that PROH causes significantly greater losses of oocytes as a result of parthenogenetic activation and degeneration than of exposure to DMSO.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

AbstractIf the energy‐density of prey bodies is not uniform, predators usually should maximize their rate of energy intake while foraging by selectively consuming energy‐rich regions of their prey and discarding other parts. In this study, the hypothesis of selective body‐part consumption was tested using two species of dasyurid marsupials, Sminthopsis youngsoni and Ningaui ridei, and their invertebrate prey in arid central Australia. Energy‐densities were similar for several divergent types of prey, including whole insects, centipedes, and spiders (20.2 ± 2.19 (sd) J/mg ash‐free dry mass), but there were marked differences in energy‐density among orders of insects and also between different body regions of invertebrates. In captivity, the two dasyurid species consumed different body regions of prey in the same way. For beetles and cockroaches, the rank order for selection of body parts was abdomen, thorax, head, then legs; for centipedes it was body, then head + legs; and for spiders it was opisthosoma, prosoma and then legs. Selection of prey body regions by the marsupials correlated closely with the energy‐densities of these regions, and also with the rates of energy intake that they yielded. In the field, selection of body parts of arthropod prey by dasyurids probably occurs primarily when prey is abundant. This selection should allow maximization of rates of energy intake in favourable periods and should, in turn, allow dasyurid predators to effectively exploit pulses of prey resources in the temporally variable desert environment.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.