CHEMICAL ENGINEERING TRANSACTIONS VOL. 67, 2018 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Valerio Cozzani, Bruno Fabiano, Davide Manca Copyright © 2018, AIDIC Servizi S.r.l. I SBN 978-88-95608-64-8; I SSN 2283-9216 Potential and Limits of IoT for Hazardous Job in Process Industries Paolo Bragatto a , Luca Faramondi b, *, Francesco Failla b , Maria Grazia Gnoni c a Department of Technological Innovations, INAIL Italian Workers Compensation Authority, Monteporzio Catone, Rome, Italy b Unit of Automatic Control, University Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy c Department of Innovation Engineering, University of Salento, Via Monteroni, 73100, Lecce, Italy l.faramondi@unicampus.it In process industries, including refineries, petrochemical plants, air fractioning plants, Oil and gas depots, there are many hazards for workers (both for employees and contractors). Occupational Hazards include thermal extremes, high concentration of toxic or flammable gas and low concentration of oxygen. These hazards are usually controlled by means of procedures, operating instruction, gas sensors, alarms, personal and collective protection equipment. Whilst a few hazards are well known and localized inside the plants, for instance the classified confined spaces or the classified ATEX areas, in other cases, hazards are associated to a high uncertainty, hence, it’s difficult to find a trade-off between the precautionary safety requirements and the work practicality and easiness. The worker, moreover, must be protected, when the hazard is present but cannot be overwhelmed by heavy protection or oversize solution. The potential of IoT enabling technologies, including smart sensoring and human-machine communication, have a huge potential for reducing the uncertainties in hazard detection and promoting a more dynamic approach. The main idea is the adoption of a solution based on wearable and fixed sensors used to dynamically monitoring the environments in order to provide, in real time, information about situation context in order to help the workers to better estimate the actual level of risk. The use of IoT poses new problems, including web security, privacy, workers’ union acceptance. The implementation of IoT solution requires a special attention to these details, in order to avoid defeats in innovation projects. The paper illustrates the preliminary results developed inside the INAIL Bric project SmartBench related to the use of IoT and RFID beacons to provide information in real time about the equipment, the environment and the worker’s physical condition. 1. Introduction In last decade, the diffusion of novel data transmission technologies has allowed the integration of new devices and items in the cloud of companies and systems. The affirmation of the Internet of Things (IoT) represents the basis of the Industry 4.0 which inherits strengths and weaknesses of each technology involved in the IoT framework. Nowadays, the application of IoT concept to the industries leads to an improvement of services efficiency, a smart monitoring of the production, and an accurate tracking of the products from the initial phases to the delivery (Atzori 2010). Concerning the IoT technologies and products applied in the context of industries, Da Xu et al. (2014) present an overview about the most applied communication technologies in Industry 4.0, and the most cited are the radio-frequency identification (RFID) tags, the Bluetooth standard, and the near-field communication (NFC). The common aspect of these kind of solutions is the transfer of information or power between two or more devices that are not connected by an electrical conductor; indeed, in the context of industries, thanks to its flexibility this feature represents a relevant advantage. Moreover, this kind of products and technologies represent the basis of the Wireless Sensor Networks (WSNs) which mainly use interconnected intelligent sensors to sense and monitor. In this way, the classical cloud system of enterprises and industries is extended by involving smart objects such as machines, shelves, pallets, and each element that belong to the production chain. Kortuem et al. (2010) classify these smart objects by defining three different categories: activity-aware,