Multivariate KPI for energy management of cooling system in food industry

Within EU, the food industry is currently ranked among the energy-intensive sectors, mainly as a consequence of the cooling system shareover the total energy demand. As such, the definition of appropriate key performance indicators (KPI) for ammonia chillers can play a strategic role for the efficient monitoring of the energy performance of the cooling systems. The goal of this paper is to develop an appropriate management approach, to account for energy inefficiency of the single compressors, and to identify the specific variables driving the performance outliers. To this end, a new KPI is proposed which correlates the energy consumption and the different process variables. The construction of the new indicator was carried out by means of multivariate statistical analysis, in particular using Kernel Partial Least Square (KPLS).This method is able to evaluate the maximum correlation between dataset and energy consumption employing nonlinear regression techniques. The validity of the new KPI is discussed on a case study relevant to the cooling system of a frozen ready meals industry. The assessment of the proposed metric is one against Specific Energy Consumption (SEC) like indicator, typically used in the context of the Energy Management Systems

Experimental and computational investigation of a new solar integrated collector storage system

The paper discusses a combined experimental-numerical analysis of an innovative solar thermal device to be used as an Integrated Collector Storage (ICS) system providing domestic hot water. In this equipment the collector acts also as a storage unit, without requiring an external vessel. Due to its simple configuration, the ICS device was successfully used in several circumstances, especially in extreme situations such as in post-earthquake tent cities or to reach remote users in Africa. In order to assess the efficiency of this collector, the draw-off process was investigated measuring the value of the mean temperature of the water discharging from the tap as cold water filled the collector. In the present configuration the draw-off is not completely optimised and a detailed analysis was carried out in order to investigate the mixing of cold and hot water in the solar collector during the discharge phase. A series of thermocouples was placed in selected positions around the shield of the collector to investigate the evolution of the near wall temperature. Furthermore, a numerical analysis based on Large-Eddy Simulation (LES) of the mixing process inside the collector was carried out using an open source, in-house, finite-volume computational code. Even if some restrictive hypotheses were made on the thermal boundary conditions and the absence of stratification, the LES results gave interesting findings to improve the collector performance

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

Parallel drone scheduling vehicle routing problems with collective drones

We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behaviour of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently. In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the \emph{Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones} and we introduce for the first time the variant with multiple trucks, called the \emph{Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones}. For the latter variant, we propose two Constraint Programming models and a Mixed Integer Linear Programming model. An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models' behaviour on the version with multiple trucks. Some insights about future research are finally discussed

A flexible algorithm for detecting challenging moving objects in real-time within IR video sequences

Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City

A CFD-based virtual test-rig for rotating heat exchangers

Rotating heat exchangers are used in steel industry, air conditioning and thermal power plants to pre-heat air used in steam generators or for waste heat recovery. Here we focus on a rotating heat exchanger on a so-called Ljungström arrangement operated in thermal power plants to pre-heat the air fed to the steam generators. In these devices the heat exchange between two fluids is achieved through a rotating matrix that gets in contact alternatively with the two fluid streams and acts as a thermal accumulator. To increase the heat capacity and the overall exchange surface, the rotating matrix is filled by a series of folded metal sheets. In the paper we de-scribe a methodology to account for the effects of the Ljungström in a virtual test-rig implemented in a Computational Fluid Dynamics environment. To this aim, a numerical model based on the work of Molinari and Cantiano was derived and implemented in the OpenFOAM library. RANS numerical results were compared with those of a mono-dimensional tool used by ENEL to design Ljungström heat exchangers and validated against available measurements in a real configuration of a thermal power plant

Performance analysis of a common-rail Diesel engine fuelled with different blends of waste cooking oil and gasoil

An experimental campaign was performed to study the behavior of a common-rail Diesel engine in automotive configuration when it is fuelled with blends of Diesel fuel (DF) and waste cooking oil (WCO). In particular the tested fuels are: B20 blend, composed of 20% WCO and 80% DF; B50, composed of 50% WCO and 50% DF; WCO 100% and 100% DF. In order to fuel the engine with fuel having a similar viscosity, this quantity, together with density, has been meas-ured at temperature ranging from rom to about 80 °C. According to these measurements, before fuelling the engine B20 was heated up to 35 °C and B50 to 75 °C. An in-house software was developed to acquire the data elaborated by the electronic control unit. Results show the trend in torque and global efficiency at different gas pedal position (gpp) and different engine speed. The experiments show that larger discrepancies are measured at smaller gpp values, while at larger ones dif-ferences become smaller. A similar trend is noticed for engine global efficiency

On the Evaluation of Sequential Machine Learning for Network Intrusion Detection

Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal characteristics of Network traffic Flows (NetFlows), and use them for NIDS tasks. However, the applications of these sequential models often consist of transferring and adapting methodologies directly from other fields, without an in-depth investigation on how to leverage the specific circumstances of cybersecurity scenarios; moreover, there is a lack of comprehensive studies on sequential models that rely on NetFlow data, which presents significant advantages over traditional full packet captures. We tackle this problem in this paper. We propose a detailed methodology to extract temporal sequences of NetFlows that denote patterns of malicious activities. Then, we apply this methodology to compare the efficacy of sequential learning models against traditional static learning models. In particular, we perform a fair comparison of a `sequential' Long Short-Term Memory (LSTM) against a `static' Feedforward Neural Networks (FNN) in distinct environments represented by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results highlight that LSTM achieves comparable performance to FNN in the CICIDS2017 with over 99.5\% F1-score; while obtaining superior performance in the CTU13, with 95.7\% F1-score against 91.5\%. This paper thus paves the way to future applications of sequential learning models for NIDS

Recensioni: Sbagliando non si impara. Perché facciamo sempre le scelte sbagliate in amore, sul lavoro e nella vita quotidiana

Le scienze cognitive e l'economia comportamentale nel giro di un decennio si sono trasformate da discipline esoteriche per soli addetti ad argomento comune, quotidianamente dibattuto su quotidiani e social. Il volume di Sara Garofalo ne è un esempio perché offre al lettore una serie di esempi, esercizi e test in cui semplici situazioni di vita quotidiana rivelano l'ormai nota fallibilità della architettura delle scelte della nostra specie, ovvero la nostra 'razionalità limitata' preconizzata dalle ricerche, ogni volta riconosciute con il Nobel, dello psicologo Herbert Simon tra gli anni Cinquanta e Settanta del secolo scorso, poi sviluppata in economia dallo psicologo Daniel Kahneman e dall'economista comportamentale Richard Thaler in anni recenti