As an alternative to the construction of new infrastructure, repurposing existing natural gas pipelines for hydrogen transportation has been identified as a low-cost strategy for substituting natural gas with hydrogen in the wake of the energy transition. In line with that, a 342 km, 36″ natural gas pipeline was used in this study to simulate some technical implications of delivering the same amount of energy with different blends of natural gas and hydrogen, and with 100% hydrogen. Preliminary findings from the study confirmed that a three-fold increase in volumetric flow rate would be required of hydrogen to deliver an equivalent amount of energy as natural gas. The effects of flowing hydrogen at this rate in an existing natural gas pipeline on two flow parameters (the compressibility factor and the velocity gradient) which are crucial to the safety of the pipeline were investigated. The compressibility factor behaviour revealed the presence of a wide range of values as the proportions of hydrogen and natural gas in the blends changed, signifying disparate flow behaviours and consequent varying flow challenges. The velocity profiles showed that hydrogen can be transported in natural gas pipelines via blending with natural gas by up to 40% of hydrogen in the blend without exceeding the erosional velocity limits of the pipeline. However, when the proportion of hydrogen reached 60%, the erosional velocity limit was reached at 290 km, so that beyond this distance, the pipeline would be subject to internal erosion. The use of compressor stations was shown to be effective in remedying this challenge. This study provides more insights into the volumetric and safety considerations of adopting existing natural gas pipelines for the transportation of hydrogen and blends of hydrogen and natural gas.

This study aims to experimentally investigate the potential of solubility trapping mechanism in increasing CO2 storage during EGR by CO2 injection and sequestration in conventional natural gas reservoirs. A laboratory core flooding process was carried out to simulate EGR on a sandstone core at 0, 5, 10wt% NaCl formation water salinity at 1300 psig, 50 o C and 0.3ml/min injection rate. The results show that CO2 storage capacity was improved significantly when solubility trapping was considered. Lower connate water salinities (0 and 5 wt%) showed higher CO2 solubility from IFT measurements. With 10% connate water salinity, the highest accumulation of the CO2 in the reservoir was realised with about 70% of the total CO2 injected stored; an indication of improved storage capacity. Therefore, solubility trapping can potentially increase the CO2 storage capacity of the gas reservoir by serving as a secondary trapping mechanism in addition to the primary structural and stratigraphic trapping and improving CH4 recovery.

It has been proven that vehicle emissions such as oxides of nitrogen (NOx) are negatively affecting the health of human beings as well as the environment. In addition, it was recently highlighted that air pollution may result in people being more vulnerable to the deadly COVID-19 virus. The use of biofuels such as E5 and E10 as alternatives of gasoline fuel have been recommended by different researchers. In this paper, the impacts of port injection of water to a spark ignition engine fueled by gasoline, E5 and E10 on its performance and NOx production have been investigated. The experimental work was undertaken using a KIA Cerato engine and the results were used to validate an AVL BOOST model. To develop the numerical analysis, design of experiment (DOE) method was employed. The results showed that by increasing the ethanol fraction in gasoline/ethanol blend, the brake specific fuel consumption (BSFC) improved between 2.3% and 4.5%. However, the level of NOx increased between 22% to 48%. With port injection of water up to 8%, there was up to 1% increase in engine power whereas NOx and BSFC were reduced by 8% and 1%, respectively. The impacts of simultaneous changing of the start of combustion (SOC) and water injection rate on engine power and NOx production was also investigated. It was found that the NOx concentration is very sensitive to SOC variation.

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

This investigation was carried out to highlight the influence of the variation of permeability of the porous media with respect to the injection orientations during enhanced gas recovery (EGR) by CO2 injection using different core samples of different petrophysical properties. The laboratory investigation was performed using core flooding technique at 1300 psig and 50 °C. The injection rates were expressed in terms of the interstitial velocities to give an indication of its magnitude and variation based on the petrophysical properties of each core sample tested. Bandera Grey, Grey Berea, and Buff Berea sandstone core samples were used with measured permeabilities of 16.08, 217.04, and 560.63 md, respectively. The dispersion coefficient was observed to increase with a decrease in permeability, with Bandera Grey having the highest dispersion coefficient and invariably higher mixing between the injected CO2 and the nascent CH4. Furthermore, this dispersion was more pronounced in the horizontal injection orientation compared to the vertical orientation with, again, the lowest permeability having a higher dispersion coefficient in the horizontal orientation by about 50%. This study highlights the importance of the permeability variation in the design of the injection strategy of EGR and provides a revision of the CO2 plume propagation at reservoir conditions during injection.