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Heat transfer from the walls of engine has a significant role on engine combustion, performance and emission characteristics. The study objectives to showcase the efficacy of the new model through an analysis by comparison with existing heat transfer models. New model is based on woschni model in which pressure and temperature is replaced by other fluid properties like density, thermal conductivity and dynamic viscosity. A series of experiments were conducted on a compressed natural gas internal combustion engine across varying hydrogen fractions, EGR ratios, engine speeds and different loads under stoichiometric conditions. The study demonstrates the efficacy of the proposed model by constantly achieving high prediction quality across a wide range of engine calibration coefficients by using Quasi-dimensional Combustion Model (QDCM) on MATLAB. Comparative analyses of new model with different heat transfer models were undertaken to validate the heat transfer rates with experimental results across a broad spectrum of operational conditions. It is observed that heat transfer rate is increased by increasing the engine load as 25%, 50%, 75% and 100% as 90.57J/deg, 130.12J/deg, 200.02J/deg, and 260.26J/deg with new model correspondingly. Heat transfer rate reduced by rise in engine speed with 1100 rpm, 1200 rpm, 1500 rpm and 1700 rpm is as 32.91 kW, 32.16 kW, 25.36 kW and 18.03 kW by new heat transfer model respectively. Artificial neural network (ANN) popular backpropagation algorithm is adopted to predict the heat transfer rate of HCNG engine, the five-input and one-output network structure are used. The values of correlation coefficient (R) and mean square error (MSE) were 0.99957 and 0.22667, 0.99998 and 0.010776, 0.99253 and 4.4762, 0.9961 and 1.2329, 0.99994 and 0.025108 for Woschni, New_Model, Chang, Sitkei and experimental respectively. This research work offers that ANN is a wise option for conventional modeling systems. In this way, the heat transfer rate of hydrogen-added CNG engines may be precisely predicted using ANN modeling. • Experiments were performed on the HCNG engine under a wide range of operating conditions. • The heat transfer rate analysis of different models by QDCM and compared with new model. • In new model, pressure and temperature is replaced by other fluid properties like density, thermal conductivity and dynamic viscosity. • Heat transfer rate increases by increasing the hydrogen fraction and load. • Artificial neural network (ANN) popular backpropagation algorithm is adopted to predict the heat transfer rate of HCNG engine. [ABSTRACT FROM AUTHOR]

Converting surplus electricity into hydrogen is considered a promising way for accommodating large-scale wind or solar power. However, it is expensive to establish a new-built hydrogen network or storage facilities at the current stage. Hydrogen-enriched compressed natural gas (HCNG), a mixture of natural gas and hydrogen, can be an alternative approach to consuming surplus renewable energy in the short-medium term. This paper investigates the feasibility of establishing an electricity-gas-hydrogen coupled energy system by modeling the HCNG network, power grid, and hydrogen electrolyzer in Ireland with consideration of pipeline characteristics. The economic analysis based on the planning results of the coupled system is presented in detail. When the hydrogen injection ratio reaches 20% at the HCNG stations, the system operating cost and carbon emission can be reduced by 24.2% and 6.4%, and 100% wind can be consumed. • A detailed modeling method for HCNG pipeline pressure drop and line pack is proposed. • An EGH-IES planning model encompassing entire HCNG supply chain is established. • A case study is conducted based on the real energy system in Ireland. [ABSTRACT FROM AUTHOR]

Hydrogen energy plays a crucial role in meeting the “carbon peaking and carbon neutrality” goals, and the carbon capture technology is a vital technique for emission reduction in the energy industry. Blending hydrogen with natural gas to produce hydrogen-enriched compressed natural gas (HCNG) facilitates the transportation and utilization of hydrogen energy. At the same time, applying the carbon capture technology to retrofit thermal power units can effectively promote the large-scale consumption of renewable energy and reduce carbon emissions. For this purpose, a detailed model of hydrogen production equipment and fuel cells is established. Then, aimed at the problem of system carbon emissions, a carbon emission and output model of carbon capture thermal power units and a mathematical model of hydrogen doped cogeneration are established, and a stepped carbon trading mechanism is introduced to control carbon emissions. Based on this, an optimal scheduling model for hydrogen-enriched compressed natural gas integrated energy system is established with the goal of minimizing the sum of energy purchase cost, carbon emission cost, wind abandonment cost, and carbon sequestration cost, and taking into account the constraints such as hydrogen blending ratio and carbon capture in the pipeline network, which is solved by using the particle swarm optimization algorithm in conjunction with CPLEX. The analysis of the models built in different scenarios verifies the advantages of the proposed scheduling model in low-carbon economy.

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Transporting green hydrogen by existing natural gas networks has become a practical means to accommodate curtailed wind and solar power. Restricted by pipe materials and pressure levels, there is an upper limit on the hydrogen blending ratio of hydrogen-enriched compressed natural gas (HCNG) that can be transported by natural gas pipelines, which affects whether the natural gas network can supply energy safely and reliably. To this end, this paper investigates the effects of the intermittent and fluctuating green hydrogen produced by different types of renewable energy on the dynamic distribution of hydrogen concentration after it is blended into natural gas pipelines. Based on the isothermal steady-state simulation results of the natural gas network, two convection–diffusion models for the dynamic simulation of hydrogen injections are proposed. Finally, the dynamic changes of hydrogen concentration in the pipelines under scenarios of multiple green hydrogen types and multiple injection nodes are simulated on a seven-node natural gas network. The simulation results indicate that, compared with the solar-power-dominated hydrogen production-blending scenario, the hydrogen concentrations in the natural gas pipelines are more uniformly distributed in the wind-power-dominated scenario and the solar–wind power balance scenario. To be specific, in the solar-power-dominated scenario, the hydrogen concentration exceeds the limit for more time whilst the overall hydrogen production is low, and the local hydrogen concentration in the natural gas network exceeds the limit for nearly 50% of the time in a day. By comparison, in the wind-power-dominated scenario, all pipelines can work under safe conditions. The hydrogen concentration overrun time in the solar–wind power balance scenario is also improved compared with the solar-power-dominated scenario, and the limit-exceeding time of the hydrogen concentration in Pipe 5 and Pipe 6 is reduced to 91.24% and 91.99% of the solar-power-dominated scenario. This work can help verify the day-ahead scheduling strategy of the electricity-HCNG integrated energy system (IES) and provide a reference for the design of local hydrogen production-blending systems. [ABSTRACT FROM AUTHOR]

Abstract: In this study statistical analysis methods were used for optimizing a spark ignition engine fueled by NG and hydrogen mixtures. Firstly designs of experiment and range analysis of the results have been carried out in order to improve the efficiency of experiments and reduce the workload. And then, a flexible model of this kind of engine that is catered to multidimensional optimization has been built. After that, the genetic algorithm is used to optimize the model. Finally the optimum control parameters of this operated point are determined to be hydrogen fraction 30–40%, excess air ratio 1.45–1.6 and ignition timing 20–22° BTDC at 1200r/min, 0.4MPa. The comparison of the optimized results and the original CNG performance showed that CH4, CO, NO x , and BSFC decrease by 70%, 83.57%, 93%, and 5%, respectively. This proved that the combination of artificial neural network and genetic algorithm is an effective way to optimize the hydrogen blend natural gas engine. [Copyright &y& Elsevier]