Nanoparticles synthesis in wet-operating stirred media: Preliminary investigation with DEM simulations

The growing demand of nanomaterials is pushing towards the development of alternative strategies for the safe and sustainable production of nanoparticles. At the same time, to ensure high performances, a fine control over the product specifications is required. We focused on a bottom-up method combined with a mechanical disaggregation technique using a wet bead-stirring process, since it provides numerous advantages over other approaches, including the minimization of the nanoparticles air dispersion and a greater control over the final product. However, given the broad variability of the parameters involved in both the setup and operation of the process, it is essential to combine the experiments with a theoretical-simulative study to optimize the design. The present activity consists in the preliminary simulation of the interactions among the grinding beads, modelled through the discrete element method (DEM), and the magnetic stirrer. This approach, providing information regarding the frequency and energy of collisions, which can be related to the properties of the produced nanoparticles, allows a fine tuning of the process parameters

A field study on human factor and safety performances in a downstream oil industry

afety culture and awareness by workers are pivotal tools for the implementation of systematic procedures aiming to risk mitigation in the process industry. The evaluation of human factors on safety performance can reveal unsafe attitudes and failures in training, supervision and management, whose correction greatly contribute to the enhancement of safety program. In this work, the role of human factors in an oil industry was studied by the collection of field data through a structured questionnaire filled by shift, daily and outsourced workers. A deep investigation on the variables involved in the process was carried out, firstly quantifying three conceptual key dimensions (individual, human resource management, equipment and technology) and then analyzing data by means of Response Surface Methodology (RSM), to identify the statistical significant factors and the overall level of safety awareness, behaviour and risk perception of the respondents

Safety Concerns and Chemical Aspects of Improvised Explosive Devices and Homemade Explosives

The continuous changes in socio-political scenarios of the last decades led to an impressive increase in terrorist events related to the use of improvised explosive devices (IEDs). The energetic material contained in them, representing the essential part of the apparatus, is object of intense investigation owing to the need of optimizing many variables, namely the chemical energy storage of the detonating compound, the availability of raw materials required for its synthesis, the ease of process synthesis by commonly used tools and the stability of the chemical energy carrier towards transport and handling. This critical analysis proposes a classification of the detonating compounds or mixtures according to their chemical, thermodynamic and ballistic properties that make them basic ingredients in IEDs and homemade explosives. The wide and always growing variety of ingredient combination poses a challenging problem of chemical identification, owing to an interference of signals in analytical data regression. Finally, a discussion on technical realizations of such improvised weapons is outlined in light of the recent protocols of process safety and disaster control

Make Bow-tie Dynamic by Rethinking it as a Hierarchical Bayesian Network. Dynamic Risk Assessment of an LNG Bunkering Operation

In the present era, the spread of cyber-physical systems in the framework of the so-called Industry 4.0, is leading towards a complete automation of industrial processes, which are increasingly decentralized, smart, and require fewer and fewer frontline personnel. The risk assessment process is certainly not excluded from the revolution, and in perspective needs to be automatic, dynamic and linked with the conditions that emerge, moment by moment, in the life of a complex system. Analytical techniques can help in converting data in information and hence system knowledge to spot trends in operational performance, thus improving risk assessment quality. Even though the bow-tie approach is widely used within the context of complex systems, it still evidences several limitations, mainly connected to the actual assessment of likelihood and interdependencies in the fault and event trees. This paper shows how a bow tie analysis can be reframed as a Hierarchical Bayesian Network, where the probability distributions of the network nodes are updated with real time predictions during the operations. The proposed model was then applied to the risk assessment of a shore-to-ship LNG bunkering operation

Inverse Estimation of Temperature Profiles in Landfills Using Heat Recovery Fluids Measurements

In addition to leachate and gas emission analysis, temperature variations in municipal solid waste landfills are routinely monitored for safety and health reasons, such as the increased production of biogas or the danger of spontaneous combustion phenomena if the temperature exceeds 70–75°C. The increasing constraints on greenhouse gas emissions and the convenience of fuel and heat recovery have helped develop a global approach to landfills' operation and maintenance, generally referred to as bioreactor landfill management. The heat recovery piping we are presently designing can be a significant part of this approach. The heat gained by a fluid circulated in a closed network through the landfill is transferred to an external heat exchanger or used directly as warm water. Additionally, it can help reduce landfill temperature levels and control biogas generation. Since the pipes diameter is large enough to allow for a radial temperature gradient, this information can be used for an inverse estimation of the temperature profile in the landfill which constitutes the boundary conditions of the resulting heat transfer problem. In this paper, we describe an algorithm for regularising the resulting ill-posed free boundary estimation problem using sampled data of the heat recovery fluid on exiting the landfill

Integration of New Technologies and Alternative Methods in Laboratory-Based Scenarios

In this study, we report a preliminary requirements analysis to recognize needs and possibilities for integrating new technologies and methods for lab-based learning in the field of Industry 4.0 and Internet of Things. To this aim, different scenarios, such as real, remote and virtual labs, are considered to be addressable within an integrated learning environment that focuses on alternative methods (i.e. Serious Games, Self-Regulated and Collaborative Learning) and new technologies (i.e. Open Badges, Mixed Reality and Learning Analytics). To support the design of the laboratory-based learning environment, qualitative interviews were conducted with both expert lecturers and relevant students in the field of engineering, to provide complementary perspectives. These interviews were carried out to analyze the requirements, and to identify possible benefits that relevant stakeholders expect by using these teaching and learning methods and technologies. A qualitative content analysis has been started on the interviews to define which is the perception of the new technologies and teaching methods. The different points of view about technologies and methods coming from expert lecturers’ and relevant students’ interviews are provided

A Bayesian belief network for local air quality forecasting

This study is focused on the development of a Bayesian network for air quality assessment and aims at offering a pragmatic and scientifically credible approach to modelling complex systems where substantial uncertainties exist. In particular, the main object is the prediction of the occurrence of suitable conditions for the stagnation of pollutants in a given area. The analytical modeling of the network provides a set of independent nodes, represented by the outputs of a forecasting meteorological Limited Area Model, from which descend the conditions for the stagnation of pollutants in different areas of the city (through measurements of the heuristic pollutant from monitoring stations) and finally the global conditions. The urban area of Genoa (Italy) was selected in order to test the actual capability of the model prototype. Network training was performed by means of historical data resulting from significant statistical series of the past years by the air quality-monitoring network. The system used for data assimilation, construction and network learning is completely based on an open source statistical processing software

Nanotechnology in machining processes: recent advances

In this brief survey, the use of nanoparticle dispersions in machining processes is discussed and the relevant applicational performances are analysed and related to the structural and chemical composition of the embedded nanophase. The paper is divided in two basic parts. In the former, the metalworking nanofluids are classified with respect to the physico-chemical properties of the nanostructured phase suspended in the base fluid. In the latter, some aspects concerning the production of metalworking nanofluids are analysed and a new green and economically viable technique based on a cementation process for metal nanoparticle synthesis is proposed as an alternative approach to the conventional manufacturing techniques