Pollutant emissions in common-rail diesel engines in extraurban cycle: rapeseed oils vs diesel fuel

The new energy strategy of EU (i.e., Directive 2009/28/EC) requires increasing the use of biofuels in transports up to at least 10% of the total fuel consumption. In the last years, the share of Diesel engines in automotive applications reached about 55% in EU market, thus trying to widen the alternatives to Diesel fuel is very important. In this framework straight vegetable oils (SVO) can represent one of the available possibilities at least in some specific applications (i.e., public transportation, hybrid or marine propulsion, etc.). SVO properties may be very different form Diesel fuel, thus operating a Diesel engine with SVO might result in some problems, especially in automotive configuration where the electronic unit acts as if it is working with Diesel fuel. This reflects in possible engine power and torque reduction, maintenance problems, and pollutant emissions during vehicles running. The latter aspect is the focus of the present paper. In this work, we used a turbocharged, four stroke, four cylinders, water cooled, commonrail multijet Diesel engine in automotive configuration to simulate the extraurban cycle according to the EU standard, comparing pollutant emissions in case of SVO and gasoil fuelling

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Industrial energy management systems in Italy: state of the art and perspective

Despite the economic crisis, the impact of industry sector Share on the total primary energy demand in Italy is still significant. The certification of companies according to the standard ISO 50001:2011 ("Energy management systems Requirements and guidelines for use"), can represent a key element in the achievement of objectives set in the 20-20-20 Climate-Energy Package. This paper illustrates the state of implementation of ISO 50001 certifications in Italy, reporting on the results of a questionnaire carried out as a part of a master's thesis project at Sapienza, University of Rome in collaboration with FIRE (Italian Federation for the Rational Use of Energy) that included the major certification bodies, certified companies and consultants. The purpose is to outline the current situation, identify the perspectives and highlight the pros and cons related to the implementation of an Energy Management System (EnMS). The big picture shows that Italy, one of the leading countries in energy efficiency policies, suffer from a significant delay in the implementation of the EnMS in industry with respect to Germany. The results of the survey also show that the definition of energy performance indicators, as hell as the individuations of an energy baseline and a. monitoring plan constitute the requirements most critical to comply with for companies than for consultants. It also appears that more than 35% of companies already ISO 50001 certified have received benefits in terms of cumulative energy saving above 5%, and that the main reason why they have implemented an EnMS is related to the potential impact on increasing the competitiveness of the core business

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

The V_c-sigma_c relation in high and low surface brightness galaxies

We investigate the relation between the asymptotic circular velocity, V_c, and the central stellar velocity dispersion, sigma_c, in galaxies. We consider a new sample of high surface brightness spiral galaxies (HSB), low surface brightness spiral galaxies (LSB), and elliptical galaxies with HI-based V_c measurements. We find that: 1) elliptical galaxies with HI measurements fit well within the relation; 2) a linear law can reproduce the data as well as a power law (used in previous works) even for galaxies with sigma_c < 70 km/s; 3) LSB galaxies, considered for the first time with this respect, seem to behave differently, showing either larger V_c values or smaller sigma_c values.Comment: 2 pages, 2 figures, to appear in Proc. IAU Symp. 222, "The Interplay among Black Holes, Stars and ISM in Galactic Nuclei" eds. Th. Storchi Bergmann, L.C. Ho & H.R. Schmitt (Cambridge University Press

The Circumnuclear Ring of Ionized Gas in NGC3593

We present the results of narrow-band Halpha+NII imaging of the early-type spiral NGC3593 in combination with a study of the flux radial profiles of the NII (lambda: 654.80, 658.34 nm), Halpha, and SII (lambda: 671.65, 673.08 nm) emission lines along its major axis. The galaxy is known to contain two counterrotating stellar discs of different size and luminosity. We find that the Halpha emission mainly derives from a small central region of 57 arcsec x 25 arcsec. It consists of a filamentary pattern with a central ring. This has a diameter of about 17 arcsec (~ 0.6/h kpc) and it contributes about half of the total Halpha flux. The ring is interpreted as the result of the interaction between the acquired retrograde gas which later formed the smaller counterrotating stellar disc and the pre-existing prograde gas of the galaxy.Comment: Accepted for pubblication in Astronomy and Astrophysics; one latex file (corsini.tex), and 2 encapsulated postscript figures (corsini_fig1.ps,corsini_fig2.ps). To be compiled with aa.cls latex2e macro style (pslatex option): 6 pages after latex compilatio

On the use of artificial Intelligence for condition monitoring in horizontal-axis wind turbines

Wind power is one of the fastest-growing renewable energy sectors and is considered instrumental in the ongoing decarbonization process. However, wind turbines (WTs) present high operation and maintenance costs caused by inefficiencies and failures, leading to ever-increasing attention to effective Condition Monitoring (CM) strategies. Nowadays, modern WTs are integrated with sensor networks as part of the Supervisory Control and Data Acquisition (SCADA) system for supervision purposes. CM of wind farms through predictive models based on routinely collected SCADA data is envisaged as a viable mean of improving producibility by spotting operational inefficiencies. In this paper, we introduce an unsupervised anomaly detection framework for wind turbine using SCADA data. It involves the use of a multivariate feature selection algorithm based on a novel Combined Power Predictive Score (CPPS), where the information content of combinations of variables is considered for the prediction of one or more key parameters. The framework has been tested on SCADA data collected from an off-shore wind farm, and the results showed that it successfully detects anomalies and anticipates major bearing failures by outperforming a recent deep neural approach