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The performance of porous carbon materials as sorbents is often compromised by the presence of humidity. Studying the kinetics of water vapour adsorption on activated carbons will undeniably help to overcome this issue. This has been approached in this work by evaluating the influence of several operational factors on the dynamic adsorption of water vapour in these materials. Specifically, different carbon types, particle sizes, air flows and ambient conditions (temperature and relative humidity (RH)) were systematically investigated. The impact of each isolated parameter on both the maximum water uptake and the uptake rate was analyzed by fitting the experimental data to the Linear Driving Force (LDF) kinetic model. The results show that except for the particle size, the studied variables play a role in the water sorption kinetics, although to a different extent. It was also confirmed that the LDF model can adequately describe the kinetics of water vapour adsorption independently of the experimental conditions. Finally, the complete water vapour adsorption process can be described by this model, obtaining a different value of the kinetic constant for the sequential stages, involving different adsorption mechanisms.

It is very important to be able to predict the breakthrough time of gas mask filters under real life circumstances. This article describes the use of a very simple predictive equation, the Wheeler-Jonas equation, that yields excellent predictions but only within a very restrictive set of boundary conditions. In order to make this model work in a more realistic environment, it has been gradually adapted to take into account a number of parameters related to this environment: a non-constant inlet concentration, a breather flow, new physical forms of activated carbon, the relative humidity and temperature of the ambient air, chemisorbed gases and mixtures of organic vapours. As (nearly) all of these parameters can be calculated independently of each other, based on data that are either readily available or that can be measured, their influence on the complexity of the model stays low. This makes this combined model both easy to use and powerful in predicting breakthrough times of gas mask filters under real-life conditions.