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Effectively dealing with invasive species is a pervasive problem in environmental management. The damages that stem from invasive species are well known. However, controlling them cost-effectively is an ongoing challenge, and mathematical modeling and optimization are becoming increasingly popular as a tool to assist management. In this paper we investigate problems where optimal control theory has been implemented. We show that transforming these problems from state–costate systems to state-control systems provides the complete qualitative description of the optimal solution and leads to its theoretical expression for free terminal time problems. We apply these techniques to two case studies: one of feral cats in Australia, where we use logistic growth; and the other of wild-boars in Italy, where we include an Allee effect. [ABSTRACT FROM AUTHOR]

The joint inversion of multiple data sets encompasses the advantages of different geophysical methods but may yield to conflicting solutions. Global search methods have been recently developed to address the issue of local minima found by derivative-based methods, to analyse the data compatibility and to find the set of trade-off solutions, since they are not unique. In this paper, we examine two evolutionary algorithms to solve the joint inversion of electrical and electromagnetic data. These nature-inspired metaheuristics also adopt the principle of Pareto optimality in order to identify the result among the feasible solutions and then infer the data compatibility. Since the joint inversion is characterized by more than one objective, we implemented the algorithm multi-objective particle swarm optimization (MOPSO) to jointly interpret time-domain electromagnetic data and vertical electrical sounding. We first tested MOPSO on a synthetic model. The performance of MOPSO was directly compared with that of a multi-objective genetic algorithm, the non-dominated sorting genetic algorithm (NSGA-III), which has often been adopted in geophysics. The adoption of MOPSO and NSGA-III enabled avoiding both simplification into a single-objective problem and the use of a weighting factor between the objectives. We tested the two methods on real data sets collected in the northwest of Italy. The results obtained from MOPSO and NSGA-III were highly comparable to each other and largely consistent with literature findings. The MOPSO performed a rigorous selection of the best trade-off solutions and its convergence was faster than NSGA-III. The analysis of the Pareto Front reported data incompatibility, which is very common for real data due to different resolutions, sensitivities and depth of investigations. Notwithstanding this, the multi-objective optimizers provided a complementary interpretation of the data, ensuring significant advantages with respect to the separate optimizations we carried out using the single-objective particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

Abstract The ROBEX (Robotic Exploration of Extreme Environments) alliance, as formed by the German Helmholtz association, aims to explore synergies and bring together technological challenges and scientific questions between two, up to now unrelated, fields: space and deep sea. The final goal of the alliance targets field tests for available and newly developed instrumentation for the deep sea and on a terrestrial lunar analogue. In this regard, two different test campaigns were conducted, one in the area of Svalbard, Norway and one on mount Etna in Sicily, Italy. The volcano environment served as a lunar analogue, enabling seismic scientific experiments and testing of robotic mobility algorithms. The complete field mission infrastructure consists of a stationary lander, a mobile element and instrument carriers. The modular instrument carrier, commonly referred as Remote Unit (RU), was developed accounting for two different mass requirements: 3 kg (RU3) and 10 kg (RU10). While developed in the frame of ROBEX resumes the idea of a lightweight instrument carrier as developed for the MASCOT (Mobile Asteroid surface scout) mission. The RU houses the instrument, shelters it and provides all essential support functions such as rudimentary thermal control (via foil covering), power provision, data acquisition and handling and data transmission to the control centre. This paper presents theoretical and experimental results of the RU3 power subsystem analysis during the mount Etna field campaign. Drawing upon this analysis, necessary adjustments and revisions to further develop the system towards a more power efficient structure for terrestrial and extraterrestrial usage can be concluded. Highlights • RU suitable for harsh environments-space analog demonstration missions. • Test Data confirmed theoretical estimations for power management and consumption. • RU suitable for planetary exploration investigated (exemplary Moon application). • RU capable of surviving a complete lunar day when outfitted with photovoltaic cells. • A qualitative outlook on needed adaptations, focused on mass savings was considered. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimisation of small-scale micro gas turbines totally powered by the concentrated solar power to generate electricity in the range of 5–30kWe. The objective of this paper is to investigate of the potential of such systems for solar power generation at reasonable costs. The computational model uses a component-based approach for thermodynamic performance simulation and features an integrated economic model which allows for the evaluation of economic performance indicators including levelised cost of electricity. The integrated model is coupled to a genetic algorithm optimisation framework to find system designs with optimal techno-economic performance. Two cases of fixed 5kWe rated power and 5-30kWe systems are studied. The performance simulation considers the operation strategy and the safe operation limits. A multi-objective optimisation is performed for each case to find trade-offs between the performance and cost of the system. The levelised cost of electricity and annual solar to electric efficiency are considered for comparison purposes. Results show that a levelised cost of electricity of about 170€/MWh can be achieved for a system installed in Italy. Lower cost of electricity as low as 85€/MWh could be achieved when considering economy of scale and locations with higher annual insolation. • A methodology to evaluate annual performance in real conditions is introduced. • Operation strategies considering system constraints and DNI variations are applied. • The optimised system improves solar efficiency by 5% with the same specific cost. • The optimal size of dish-MGT systems is 20–25kWe with LCOE of 170€/MWh for Italy. [ABSTRACT FROM AUTHOR]

In Italy in 2020, only 15.5% of school building heritage was retrofitted from an energy and environmental point of view. In this paper, the cost-optimal method was applied to two different school buildings belonging to the same Italian cold climate zone but characterized by different structural and technological solutions. The research aims at defining the cost-effective redevelopment solution among several ones proposed to apply to this building type. At the same time, this paper provides a critical analysis of the methodology applied, highlighting deficiencies related to a not proper evaluation of environmentally friendly retrofitting measures. In a cost-effective context, the main results show that the intervention on the heating system is more convenient than the retrofitting of the envelope. The energy saving is equal to about 35% for both considered schools. Among the different proposed requalification configurations, the adoption of PV (photovoltaic) electric generation is included. In this regard, an optimization procedure was implemented in a generative design environment to maximize energy production with reference to different design parameters. As a result, a solution with south oriented PV modules with a tilt angle of 42° and arranged in 0.7 m spaced rows proved to be the most effective. [ABSTRACT FROM AUTHOR]

• Development of a global optimization procedure to estimate the main downbursts parameters. • The performance efficiency of two global metaheuristic optimization algorithm is compared. • Ten different real scale thunderstorm downburst outflows are simulated. Strong localized downbursts generated in thunderstorms can produce surface winds very dangerous for civil structures and infrastructures. Modelling and simulating such severe wind systems is therefore extremely important for structural safety and design wind speed evaluation. This paper deals with the downburst wind field simulation by means of an optimization algorithm that uses a downburst analytical model, previously developed by the authors, and two metaheuristic algorithms, namely the Differential Evolution (DE) and the Teaching-Learning-Based Optimization (TLBO), for the downburst kinematic and geometrical parameters evaluation. The optimization problem minimizes the relative error between recorded and simulated wind speed and direction time histories. A comparison is made between the performance of two algorithms for ten thunderstorm events measured in north-western Italy between October 2011 and October 2015. Both algorithms provide solutions which are coherent with the downburst parameters values present in literature. TLBO outperforms DE since it has a faster convergence rate to the optimal solution. [ABSTRACT FROM AUTHOR]

The aim of the paper was to evaluate the environmental burden of the organic waste management systems implemented in Umbria region in Italy, in order to provide useful information for taking strategic decisions aimed at the improvement and optimization. The two most widespread scenarios were analyzed: source-segregated collection followed by organic fertilizer production and not-differentiated collection followed by mechanical and biological treatment and disposal in landfill of the biostabilized material. The environmental performance was assessed through Life Cycle Assessment methodology, assuming one ton of organic waste as functional unit. Most of data for life cycle inventory were provided by actual facilities while background data were obtained from EcoInvent database. The alternative scenarios were compared through the IMPACT 2002+ method and the assessment was carried out on both midpoint and endpoint levels. Results showed that the landfilling of the undifferentiated organic waste has the least impact on the analyzed impact categories, except on the Global Warming, mostly due to the uncollected methane released by the landfill. As regards the aerobic composting of the source-segregated organic fraction, the efforts to reduce the impact should be mainly focused on the reduction of the air emissions (hydrogen sulfide, particulate, ammonia and NMVOC) from the biostabilization process. Furthermore the sensitivity analysis indicated that the increase of the biogas collection efficiency could significantly improve the performance of the not-differentiated collection scenario. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a railway timetable optimization for the very dense Simplon corridor, a major railway connection in the Alps between Switzerland and Italy. The key to deal with the complexity of this scenario is the use of a novel aggregation-disaggregation method. Starting from a detailed microscopic representation as it is used in railway simulation, the data is transformed by an automatic procedure into a less detailed macroscopic representation, that is sufficient for the purpose of capacity planning and amenable to state-of-the-art integer programming optimization methods. This macroscopic railway network is saturated with trains. Finally, the optimized timetable is re-transformed to the microscopic level in such a way that it can be operated without any conflicts among the train paths. Using this micro-macro aggregation-disaggregation approach in combination with integer programming methods, it becomes for the first time possible to generate a profit maximal and conflict free timetable for the complete Simplon corridor over an entire day by a simultaneous optimization of all trains requests. In addition, this also allows us to undertake a sensitivity analysis of various problem parameters. [ABSTRACT FROM AUTHOR]