To produce an alternative fuel like Methanol from scratch, on a miniature scale- the reactants CO2 and H2O are sourced locally. Zero Emission Fuels B.V. (ZEF) does this by collecting carbon dioxide from the air, via carbon capture techniques and compressing it to high pressure of 55bars. As a consequence of humidity inside the captured CO2, condensation is observed which is detrimental to the life of the compressor. To avoid this, a packed-bed drying system is designed as an auxiliary to the rolling piston compressor. This study aims to understand and model these two systems and subsequently integrate them to examine the effects of their dynamic operation. The purpose of this model is to provide a design tool, with the ability to predict the reaction of the system when sized differently, in various environments.

At first, the physics and governing principles of the two subsystems are studied and a layered modelling approach was devised. The systems were modelled individually and then integrated using Simulink- a tool that allows this layering up of the models. To build a dynamic model of such a setup, a characteristic time scale analysis was performed to identify the relevant phenomena inside each subsystem. Significant internal factors to be modelled were variations in relative humidity and temperature caused by the drying system, the changes in thermo-physical properties of the gas due to a change in temperature and the pressure fluctuations caused by the compressor. The external factors to be modelled were the changes in ambient temperature over operation time, caused by a variation in solar irradiation. The main finding was that the compressor's characteristic time scale (300s) was considerably smaller than that of the drying system (4-8hours/day). This meant an almost instantaneous operation of the compressor, rendering it uninteresting in the dynamic model of the integrated system.

A mechanistic dynamic model of the drying system, based on literature, was made with inherent controls to flip it between adsorption and desorption modes. It is responsive to the changes in the ambient and allows optimisation by changing the duty cycles of energy inputs. The model was then validated using experimental data from two working prototypes at ZEF. Some heat and mass transfer properties were then fitted to conform to these setups. A methodology to obtain these parameters while sizing a new design is discussed. The key performance indicators, which are cycle time and cycle capacity are given by the model and are crucial in deciding the energy consumption of the system.

On the other hand, an empirical model is built to replicate the rolling-piston compressor. The compressor is treated like a grey box and is described by a polytropic coefficient. A novel approach to create a compressor map for a positive displacement compressor, that is not mass-produced, is chosen. This compressor map is then used to predict the mass flow and output temperature of the system. Since the compressor map is empirical, the model was also validated using data from the literature.

Finally, the drying system and the compressor were integrated, in the model and experiments, by means of a capillary tube. A model was made for this as well to obtain the `interacting parameters'- which were the mass flow into the compressor, mass flow out of the capillary tube and the temperature and pressure of this re-loop flow. The effects of integration and the consequences to other systems upstream and downstream of this setup were scrutinised.

To conclude, a validated model is built to size new iterations of the setup. Multiple cycle models of a packed bed drying system and compressor maps of positive displacement compressors- that are not found commonly in literature are explored in this study and recommendations to further develop them are given towards the end.