Hydrogen is increasingly seen as an important element of future ‘energy landscape’ and while its production through renewable means offers great promise, it is apparent that such sources may not be sufficient to meet expected demands in the short to medium term. It is important, therefore, to consider how this ‘gap’ may be bridged most responsibly by the efficient use of non-renewable resources, writes Keelan Glennane of University College Dublin.

In this paper, I discuss the background of the hydrogen economy, the limitations presented by renewable production, how non-renewables, in particular methane, may provide the key to helping to meet the expected demands and how this process modelling and simulation can pay a vital role in ensuring that such hydrogen production can be done responsibly.

Introduction

The birth of the ‘hydrogen economy’ can be reasonably placed in the period of the oil crisis of the 1970s, and despite limited progress towards this until the early 21st century, it has come to the fore again with the realisation that such a fuel source must form a significant part of the portfolio of responsible energy sources (Goltsov, Goltsova and Vasekin, 2008; Pacala and Socolow, 2004).

Hydrogen is predicted to have the potential of providing a quarter of the global energy consumption demand, within a multitude of large adjustable markets, if promoted by the government policies accordingly (Holger, 2020; Lacey, Kann and Gallagher, 2020).

Furthermore, the link between hydrogen and fuel cells means that it can also be used to revolutionise the mobility sector. Fuel cells are intrinsically a carbon-free technology producing electricity, heat, and water as the sole by-products, while at the same time having fewer of the limitation’s battery powered vehicles (range and a reliance on expensive (and often difficult to source) raw materials).

The hydrogen market was estimated to be worth $130 billion (in 2020) and is predicted to rise to $201 billion by 2050 (MarketsandMarkets, 2021).

Producing ‘green hydrogen’ using emerging renewable electrolyser and photolysis technology, which operates using the basic principles of splitting water into its two constituents (oxygen and hydrogen) is most preferable.

However, these technologies require further capital investment to become more cost competitive, and the process requires refinement to scale-up the technology sufficiently (Nikolaidis and Poullikkas, 2017).

Currently, steam methane reforming (SMR) methodology in conjunction with carbon sequestration (CS) technology is providing offers the most promising short- to medium-term viable production method capable of meeting the large-scale hydrogen demand predicted.

Project overview

The project focused on the influence (often complex and interdependent) of several operational parameters on the SMR process to explore their influence on process performance (ultimately aiding process optimisation) using process simulation software.

The pressure drop is investigated using the Ergun Equation described by Eq. 1, and viscosity is determined using the Chapman-Enskog approach, described by Eq. 2 (Reid, Prausnitz and Poling, 1977).  

Furthermore, the reaction rates for the reforming, Eq. 3, and water-gas-shift (WGS) reaction, Eq. 4, are also examined. The reaction mechanisms, kinetics, equilibrium constants, and operating conditions determined by literature are utilised throughout the investigation (Dehkordi, Savari and Ghasemi, 2011; Xu and Forment, 1989).  

A model based using a plug-flow reactor design was created in Aspen HYSYS to simulate the SMR process, as shown in Fig. 1. A simplified design was developed as the development of a typical SMR arrangement requires additional software to simulate the process.

This model can provide a relatively realistic representation of the SMR process. The conversion of methane and hydrogen yield are the two parameters monitored to analyse the effect of the feed velocity, temperature, and pressure on the system.

Figure 1: Plug Flow Reactor Model Flowsheet to Simulate the SMR Process for Hydrogen Production Developed using Aspen HYSYS Software

Results overview

Pressure Swing Adsorption (PSA) technology has been the most common method to separate hydrogen from the carbon dioxide in the product. Albeit Sorption Enhanced SMR is showing promise although it is currently in the development phase.

Both natural sorbents, such as calcium carbonate, and synthetic sorbents are applicable for the capture of carbon dioxide. This emerging technology can be operated at a lower temperature compared with other membrane technologies and therefore reduce catalytic coking and sintering.

In addition, this carbon-free approach would pivot the process from producing grey to blue hydrogen. The establishment of the optimal balance in carbon dioxide concentration between the two reactors in the SMR process is essential in ensuring process efficiency.

If the carbon dioxide concentration is too high the water-gas-shift (WGS) reaction is reversed, resulting in complete consumption of the carbon dioxide.

However, if the carbon dioxide concentration is too low then catalytic deactivation is increased due to an increase in the potential for carbon deposition and reduction of catalyst oxides is increased (Rosetti Valentina, 2020).

In industry nickel-based catalysts are commonly employed, where the optimum catalytic activity is achieved and limited at about 15-20% nickel content (van Beurden, 2004).

I have found that the void fraction is an important parameter to examine for the optimisation of the SMR process as it has a significant impact on the pressure drop of the system, as illustrated in Fig. 2. While reducing the pressure drop is desirable as it reduces the power requirement of the process, this requires a reduction in the void fraction.  

  Figure 2: Pressure Drop mPa as a Function of the Voidage, ε  Operating with a 0.0127m Diameter Catalyst Generated using the Ergun Equation  

I found that the superficial gas velocity and temperature of the feed also significantly effect the pressure drop of the system, while the operating pressure is relatively insensitive.

Fig. 3(a) illustrates that a slight increase by 0.00035 m/s results in an increase in pressure drop of over one order of magnitude. I found that a 1 Nm2 increase in pressure drop is experienced by the system for an increase in operating pressure by ~31,053 Nm2.

Whereas only a slight increase in temperature of approximately 8.85℃ results in the same pressure drop, illustrated in Fig. 3(b). Thus, considering the most suitable catalytic dimension and shape, and operating conditions the optimal trade-off can be identified and implemented to operate the process most efficiently. 

  Figure 3: Generated using the Ergun Equation (a) Pressure Drop as a Function of Superficial Gas Velocity at an Inlet Gas Pressure of 0.1 mPa and 2.086 mPa for a Constant Methane Gas Viscosity of ≈1.66×10-5Nsm2; (b) Comparison between the Effect of Inlet Feed Temperature  and Operating Pressure Nm2 on the Pressure Drop Nm2 of the SMR Process  

The SMR process optimisation cannot solely be based on the minimisation of the pressure drop, as it is imperative to also consider efficiency of the SMR process in terms of the operational duration, economic feasibility, and the optimal utilisation of materials.

I found that the viscosity will influence the process efficiency, as the various components in the system have a range of viscosities. In addition, developing a comprehensive understanding of the change in viscosity when the components form a mixture is essential in the optimisation of the process, such as the steam-methane stream illustrated in Fig. 4.

Figure 4: Viscosity as a Function of Temperature, from 310K-510K, for all Components, Methane, Hydrogen, Carbon Dioxide, Carbon Monoxide and Water (Steam), and the Methane/Steam Mixture Present in Steam Methane Reforming Generated using the Chapman-Enskog Treatment Provided by Literature (Reid, Prausnitz and Poling, 1977)

The effect of temperature on both the reforming and WGS reactions was also examined, as shown in Fig. 5. As one would expect, both reactions are highly sensitive to temperature the quantification of which provides a powerful diagnostic tool for the optimisation of the SMR process.

An increase in temperature of 50K, results in the SMR reaction rate to increase by just over seven-fold. While the WGS reaction rate, initially only increases by two orders of magnitude, a large increase, by a factor of eight, is observed when the temperature is increased from 623K to 673K.

In addition, further analysis of the effect of operational parameters on the reaction rate, alongside understanding fundamental mechanisms of the process, such as recognising that the rate limiting step is the surface decomposition of methane, can highlight what reduces, or improves, the efficiency of the process.

Figure 5: Comparison Between the Reaction Rates of the Steam Methane Reforming and Water-Gas Shift using the Xu and Froment, i.e., Eq. 3 and Eq. 4, and Aspen HYSYS Aprroach as a Function of Temperature, including the Standard Deviation Results

Finally, I used the Aspen HYSYS model to investigate the effect of flowrate, temperature, and pressure on the SMR process. The results, illustrated in Fig. 6(a)-(c), drew informative conclusions on how to improve the yield of hydrogen and minimise inefficiencies in the plant.

I found that an increase in gas velocity decreased the process efficiency, likely due to a reduction in residence time. An increase in temperature, from 447-640K, drastically improves the yield of hydrogen +164.65%. While the process remained relatively insensitive to a change in operating pressure. 

Figure 6: Comparison between the PFR Aspen HYSYS Model and Literature, (Adris, Lim and Grace, 1994; Dehkordi, Savari and Ghasemi, 2010; Roses, Gallucci, Manzolini and Sint Annaland, 2013) (a) Conversion of Methane as a Function of Inlet Feed Flowrate while Operating with an Inlet Feed Temperature Range of 499.85℃, 549.85℃, 599.85℃ and 629.85℃; (b) Conversion of Methane and Yield of Hydrogen Results as a Function of Temperature with a Constant Bed Pressure of 0.981mPa; (c) Conversion of Methane and Yield of Hydrogen Results as a Function of Pressure with a Constant Temperature.

Conclusions and recommendations

The establishment of a well-considered trade-off is crucial to operate a robust SMR system, while optimising raw materials, considering environmental implications, and maximising economical profitability.

I believe that the SMR production method offers the most promising approach for hydrogen production. It must be recognised that even a slight improvement in the SMR process efficiency obtained by the fine-tuning of operating parameters will have a significant impact on the hydrogen economy.

Furthermore, companies such as EI-H2 are providing the momentum towards green hydrogen production nationally. In my opinion, Ireland will thrive in an energy market with hydrogen as a primary constituent. 

Author: Keelan Glennane, University College Dublin

Nomenclature

dp= Particle Effective Diameter m

K= Equilibrium Constant bar2

k= Rate Coefficient of Reaction kmolbar12kgcat∙hr

Lb= Reactor Bed Length m

M= Molecular Weight gmol

pi= Partial Pressure kPa

P= Pressure Drop Nm2

R= Universal Gas Constant JmolK

r= Rate of Reaction kmolkgcat∙hr

T= Temperature K

u= Gas Superficial Velocity ms

Greek letters

Ωυ= Collusion Integral

ε= Void Fraction -

μg= Viscosity (μP or Nsm2)

ρg= Gas Density kgm3

σ= Hard – Sphere/ Molecular Diameter

Acknowledgement

I would like to acknowledge the continued support and guidance provided by the School of Chemical & Bioprocess Engineering at University College Dublin. In particular, the research project module co-ordinator, Professor Niall English, and my academic advisor, associate professor Damian Mooney, including his assistance in the development of the Aspen HYSYS model and MATLAB code.

References

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