An open-source Python package for lumped parameter modeling of sorbent-filled storage tanks for Hydrogen, CO2, Methane, and other fluids

Excess renewable energy can be stored by converting it to gaseous fuels such as hydrogen and methane via electrochemical reactions. Similarly, it can be stored mechanically by compressing gases such as CO 2 . The energy density of such systems is dependent on the storage density of the fluids, which is typically low under ambient conditions. Adsorption onto surfaces increases the density of fluids, avoiding the requirement for compression at high pressures and liquefaction at low temperatures. However, adsorption also alters the thermodynamics of key processes in the storage tank such as (i) refueling, (ii) discharging, (iii) dormancy, and (iv) boil-off. These effects are currently not well explored in the literature, especially for novel materials, because of the difficulties of conducting tank-scale experiments and process simulations. To alleviate this issue, we have developed an open-source python package capable of running lumped parameter dynamic simulations of sorbent-filled fluid tanks. In this paper, we explain the theory behind the model, and provide several case studies covering different applications to demonstrate the validity of our package and provide illustrative examples for potential users. Through the release of this package, we provide an easy-to-use platform for materials researchers to conduct process simulations on their novel sorbent materials, which could help ease their adoption in real industrial applications. • A python package simulating the thermodynamics of fluid storage tanks is presented. • It can simulate both conventional and sorbent-filled gas and liquid storage tanks. • The package (pytanksim) is free and open source for access by a wider audience. • Case studies are provided to validate the model and display its versatility. • These include liquid and cryo-compressed hydrogen, methane, and compressed CO 2 .