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A clean energy future based on a hydrogen economy has been proposed as a feasible alternative to the current combustion of fossil fuels to reduce CO2 and other greenhouse gas (GHG) emissions. However, hydrogen’s “clean” reputation is questionable due to its main production method of steam methane reforming (SMR), which produces large quantities of CO2 emissions. To abide by recent international regulations, the production of hydrogen needs an immediate transition to low-emission production methods. A promising solution is the thermo-catalytic decomposition of methane (TCMD), which thermally decomposes methane into hydrogen gas and solid carbon without any direct GHG emissions. The problem with this process is that the catalyst deactivates quickly and therefore must be replaced periodically for sustained hydrogen yields. This results in high catalyst turnover costs, which is the main bottleneck in the successful commercialisation of this process. By developing a simplified model using the most commercially viable parameters and linking the turnover costs with the deactivation of the catalyst, this study aimed to accelerate the adoption of the TCMD process by better enabling companies to analyse the feasibility of their potential low-emission TCMD solutions.
The most commercially viable solution featured the use of a fluidised bed reactor (FBR) for continuous operation with an iron-based catalyst due to their low-cost. Catalyst regeneration was found to be ineffective and the best method for mitigating catalyst deactivation was the optimisation of the process conditions. A simplified mathematical model was then constructed to enable this adjustment to maximise the production of hydrogen and minimise the turnover costs. This model was based on the ideal continuous stirred tank reactor but incorporated the fluidising behaviour through several variables including the development of the novel “Fluidisation Factor”. An optimisation ratio was also developed to quantify the simulation results and obtain the optimal conditions.
The results showed that the ideal conditions for this process was at the highest temperature before the catalyst starts to sinter (≈950ºC) and at the maximum pressure. The largest catalyst particle size of 150 𝜇m and the maximum amount of catalyst was found to allow for higher fluidisation velocities and to delay the deactivation time respectively. The inlet gas velocity and the catalyst activity limit were found to be highly dependent on the hydrogen production rate and therefore due to the conditions used in this study the optimum inlet gas velocity was 10% of the fluidisation velocity range (and composed of pure methane), whilst the optimum activity limit was at 22% activity. Lastly, a comparison of the heating method found that controlled heating was more suitable than constant heating due to the stable temperatures during deactivation, which prevent catalyst sintering. Comparing the optimised results from this study with the SMR production method found that the estimated catalyst turnover costs were 10¢/kg H2 and 18¢/kg H2 respectively. This demonstrates that the simplified model developed in this work can better enable companies to optimise and assess the feasibility of their proposed TCMD solutions and help transition their hydrogen production processes to cleaner alternatives.
The current advancements in the field of heat exchangers have expanded their range of applications in many industries. The most important factor while investing in the heat exchanger is sizing them for a particular application. The sizing of a shell and tube heat exchanger is always a challenge, often causing customers to invest in either an oversized or undersized heat exchanger. The purpose of this study is to solve the customer’s conundrum of investing in an appropriate size shell and tube heat exchanger. The objectives of this study were (a) to develop and implement a spreadsheet program for optimization of overall heat transfer coefficient of shell and tube heat exchanger, (b) to determine profitability analysis using the Net Present Value (NPV) method, (c) to analyse the price sensitivity using the Monte Carlo simulation. The iteration and optimization are based on Kern’s method. The user defines the process parameters such as the temperature of hot and cold streams, mass flow rates and fluid densities. The user also assumes the tube related properties. The final sizing of the shell side is determined along with Reynolds number and pressure losses on the shell and tube side. The pressure losses are then converted into the operating cost of the heat exchanger to determine NPV. The Monte Carlo simulation calculates 1000 different NPVs for a given scenario, thus facilitating the user’s crucial decision-making process. This study is probably a first instance to combine preliminary sizing of the shell and tube heat exchanger with price sensitivity analysis using the Monte Carlo method.