Journal of Energy Research and Reviews

Natural gas plays an important role in industry as a clean energy, with the intensification of the Russia-Ukraine war, there is a large-scale energy shortage in Europe, and the natural gas supply in Europe has a natural gas crisis due to the cut-off of the Nord Stream No.1 pipeline. Therefore, it is necessary to accurately predict the consumption of natural gas. In order to fulfill this requirement, this paper uses the Lagrangian Support Vector Regression model with Sorensen kernel based on the Nonlinear Auto-Regressive model and Grey Wolf Optimizer for 5-step forecasting of monthly natural gas consumption in all European countries. Under three time lags, comparing the 5-step predict results of GWO-LSVR with SVR, RF, LightGBM, XGBoost, and MLP, those five models’ hyperparameters also optimized by GWO, it found that GWO-LSVR has smallest MAPE in almost all cases, and the numerical results of MAPE generated by GWO-LSVR is from 5.844% to 11.622%, the smaller the forecasting step size, the better the effect. Moreover, compares the difference of GWO and WOA, it is found that GWO can obtained better model hyperparameters and smaller MAPE results. To sum up, the proposed GWO-LSVR model has strong generalization performance and robustness, and is a reliable natural gas consumption prediction model.

MFCs are bio-electrochemical devices that are capable of transforming chemical energy stored in waste organic matter into direct electrical energy through catalytic activity of microorganisms under anaerobic conditions. Bio-electrochemical systems, such as microbial fuel cells (MFCs), serve as greener alternatives to conventional fuel energy. In recent years, MFCs have drawn science community interest as a method for direct bioelectricity recovery from wastewater while simultaneously treating the wastewater. Moreover; they gain a competitive advantage over other water treatment technologies due to their unique features such as huge energy benefits, less environmental impact, good operating stability, and high economic efficiency. Reports reveal that MFCs are the subject of much interest to researchers, and the number of papers on MFCs in wastewater treatment is increasing. The ever-growing demand for green waste management and renewable sources of energy has enthused research efforts all over the world. This study, therefore, investigated the effect of process variables on the bio-electrical performance of H-type microbial fuel cells fueled with brewery wastewater and inoculated with distillery plant waste. From the experimental results, 1150mV maximum voltage output, 92.85%, 91.40%, 68.87%, and 70.10% removal efficiencies of COD, BOD, TN and TP respectively were obtained at 35ºC, pH 7, and 5 days. These results confirmed that brewery wastewater effectively treated would generate a considerable amount of direct bio-electricity. Results also revealed that the MFC provides an alternative insight into an effective treatment of wastewater that can simultaneously generate a direct bio-electricity in a sustainable and eco-friendly manner.

The benefits of biogas as alternative energy to other fossil fuel sources, due to its renewability, environmentally friendly nature, health benefits, etc., cannot be overemphasized. There are numerous models for predicting biogas production rate from bio-materials, including the modified Gompertz equation. These models are primarily dependent on specific biomass parameters. When any of these parameters, like the slurry volume, changes, another round of experiments must be conducted and curve fitted before biogas yield predictions can be made. This could be time-consuming and costly. Using experimentally published data, simple empirical models can be developed for predicting biogas yields over a range of input parameters. This will eliminate the need for always performing experiments before biogas yield predictions can be made. In light of this, scarce literature provides explicit models for predicting biogas yield over a range of parameters based on published data. This study developed non-linear regression models using published data on parameters that affect biogas yields, like the slurry volume, carbon-to-nitrogen ratio, temperature, total solids, volatile solids, hydraulic retention time, and pH. The data covered seven readily available bio-wastes, including cow dung, cow dung with plant waste, cow dung with poultry dung, poultry dung with grass, pig dung, and plant wastes. On validation of the models, the results showed that the models had a relatively low standard error of estimates, Akaike information criterion, Schwarz criterion, and Hannan-Quinn information criterion. Furthermore, the coefficients of determination, R^2, were between 94.62 and 98.93%. The percentage average absolute deviation (% AAD) for each model was less than 7 %. The non-linear models were found to adequately predict the biogas yields within the limits of the available data set.

Hydrogen is one of the types of energy discovered in recent decades, which is based on the electrolysis of water in order to separate hydrogen from oxygen. These include grey hydrogen, black hydrogen, blue hydrogen, yellow hydrogen, turquoise hydrogen, and green hydrogen. Generally, hydrogen can be extracted from a variety of sources, including fossil fuels and biomass, water, or a combination of the two.  Green hydrogen has the potential to be a critical enabler of the global transition to sustainable energy and zero-emissions economies. Worldwide, there is unprecedented momentum to realize hydrogen's long-standing potential as a clean energy solution. Green hydrogen is a carbon-free fuel and the source of its production is water, and the production processes witness the separation of its molecules from its oxygen counterpart in the water by electricity generated from renewable energy sources such as wind and solar energy. Green hydrogen is one of the most important sources of clean energy, which may be why it is called green hydrogen. It is a clean source of energy, and its generation is based on renewable energy sources, so no carbon gases are released during its production. Green hydrogen produced by water electrolysis becomes a promising and tangible solution for the storage of excess energy for power generation and grid balancing, as well as the production of decarbonized fuel for transportation, heating, and other applications, as we shift away from fossil fuels and toward renewable energies. Green hydrogen is being produced in countries all over the world because it is one of the solutions to reducing carbon emissions, and it is clean, environmentally friendly energy that is derived from clean renewable energy. However, due to the combination of renewable generation and low-carbon fuels, projects for the production of green hydrogen are very expensive. The goal of this review is to highlight the various types of hydrogen, with a focus on the more practical green hydrogen.

Evaluating the subsurface characteristics of reservoirs is an important part of gas storage, hydrocarbon exploration, and production in sedimentary basins. This process combines geological, geophysical, and engineering data to understand the subsurface geology, and fluid distribution, determine reserves and predict the fluid movement in the reservoir. And in the case of empty reservoir, the storage capacity, sealing strength is analysed. The primary goal of reservoir characterization is to create a precise and dependable reservoir model to maximize the production procedure and reduce the associated risks of hydrocarbon exploration and production. This review examines different approaches used for reservoir characterization in sedimentary basins, including geological, geophysical, and engineering methods. Each method's advantages and disadvantages are discussed, alongside their uses in different reservoir contexts. The importance of combining multiple lines of evidence to enhance the accuracy of the reservoir models is also examined.