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Carbon dioxide (CO 2 ) is one of the most important greenhouse gases. In the period between 1980 and 1998, CO 2 emissions increased more than 21% and projections suggest that the emissions will continue to increase globally by 2.2% between 2000 and 2020 and 3.3% in the developed countries. The sequestration of CO 2 in deep unminable coal beds is one of the more promising of several methods of geological sequestration that are currently being investigated. CO 2 can adsorb onto coal, and there are several studies demonstrating that CO 2 dissolves in coals and swells them. At very low pressures (P < 1 bar), CO 2 dissolution does not seem to be a problem; however, high pressures are necessary for CO 2 sequestration (P > 50 bar). In this study, we evaluated the kinetics and equilibrium of sorption of CO 2 on Brazilian coals at low pressures. The adsorption equilibrium isotherm at room temperature (30 °C) was measured through the static method. The results showed that the Freundlich model or the Langmuir model is suitable to describe the equilibrium experimental results. The CO 2 adsorption capacity of Brazilian coals are in the range of 0.089-0.186 mmol CO 2 /g, which are typical values for coals with high ash content. The dynamics of adsorption in a fixed-bed column that contains granular coal (particle sizes of 0.8, 2.4, and 4.8 mm) showed that the adsorption rate is fast and a mathematical model was developed to describe the CO 2 dynamics of the adsorption in a fixed-bed column. The linear driving force (LDF) was used to describe the rate of adsorption and the mass-transfer constants of the LDF model (K s ) are in the range of 1.0-2.0 min -1 .

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Human exposure is a relevant factor when operating in critical environments and depends on a thorough analysis and consideration towards driving the teams to a safer and more productive environment. Reducing such exposure through digital technologies benefits the whole workforce in their decisions and maneuvers, like simulations, training, and other critical activities that can be executed remotely and prior to the actual activity. This paper presents a case study to demonstrate how augmented and virtual reality can be used to create a high fidelity virtual environment emulating the real industrial facility. This approach enriches the Digital Twin with the association of data and the virtual environment. It leverages on display and interaction capabilities of hardware devices, and intelligence and data querying capabilities of industrial software, empowering the workers with enhanced training capabilities and access to information increasing safety and efficiency. A real application of this technology is presented in this paper through the case study of the PredictMain4.0 project of Repsol Sinopec Brazil (RSB), which aimed at the integration of digital technologies, including augmented reality (AR) and virtual reality (VR). The PredictMain4.0 project was executed using data and data models of PETROBRAS’ P-50, a FPSO (Floating Production Storage and Offloading) operating in Brazil, and illustrates how different AR/VR applications can be developed and used in combination with engineering, operation, and maintenance databases. This includes 3D models, digitalized critical procedures, and the ability to integrate field teams into a single virtual environment, allowing real interaction in a digital setting that is linked to the real world. Considering the digitalized procedures, this paper aims to establish how virtual simulation and training can make teams more confident and prepared to execute the same physical asset procedures. After consulting with stakeholders from many different teams, the PredictMain4.0 project team selected three critical operating modules in the FPSO (Power Generation, Water Injection, and Gas Compression). For each one, considered which situations were relevant, should they occur. These situations led to developing a training and simulation framework, allowing instructors to create different scenarios and use advanced features such as digital measurement, real-time data collection, and collaborative sessions. The case study indicates that the development of such applications can save more than $1 million per year in maintenance costs considering the decrease in downtime and avoiding risks of accident.