International Journal of Energy Research

Focusing on mitigating global challenges arising from hydrocarbon-based sources, the integration of cogeneration power plants with solar photovoltaics offers a viable solution. The intermittent nature of renewable resources presents a challenge to the consistent performance of cogeneration systems. To address these issues, this work introduces a novel framework for integrating cogeneration power plants (CGPPs) with solar photovoltaic systems. The key innovation of this research lies in its dual-algorithm approach that seamlessly blends cogeneration power plants with solar photovoltaic. This study proposed an integrated approach, employing the Derivative Log Sigmoid-Woodpecker Mating Algorithm (DLS-WMA) and Optimized Artificial Neural Networks (O-ANN), to combine cogeneration power plants with solar photovoltaics in industrial distribution systems. The methodology is aimed at achieving a cost-effective, efficient system design, enhancing the efficiency of cogeneration power plants, and introducing energy storage batteries for uninterrupted power generation under diverse atmospheric conditions and loads. Additionally, the proposed system includes rechargeable batteries for energy storage to support critical services when the solar plant is offline and the CGPP cannot meet the power demand. The industrial system’s photovoltaic component is tuned using the DLS-WMA for cost minimization and O-ANN for solar irradiance prediction, ensuring continuous power flow by optimizing both the photovoltaic system and the cogeneration power plant (CGPP) system. Real-time datasets are used to compare the results obtained by this new approach with those of the previous state-of-the-art algorithms. The error with O-ANN prediction is 1.2%, compared to 4.1% with the existing WMA-ANN technique, while the cost-benefit with DLS-WMA shows a 9% improvement over the WMA-ANN technique. The experimental outcomes demonstrate the efficiency of this new approach. Collaboration with industry stakeholders and policymakers is crucial for the large-scale deployment of this system, facilitating the adoption of sustainable energy practices in industrial distribution systems.

The increasing emission of carbon dioxide worldwide has emerged as a major global concern in the context of addressing climate change. Converting CO₂ to high-value carbon materials is a promising solution to capture emitted carbon for achieving carbon neutrality. Furthermore, such conversion can provide carbon nanomaterials for key industries, including the lithium battery and fuel cell industries. Here, it is shown that sub-50 nm tangled carbon nanotubes (CNTs) can be synthesized by adjusting the metaborate concentration and the current density through the electrochemical conversion of carbon dioxide in a molten carbonate salt. The metaborate ion concentration affects the product selectivity and carbon morphology, and the current density is strongly related to the particle size of in situ seed catalysts supplied by the dissolution of an Ni-Fe-Cr alloy anode. The optimized process conditions control the nucleation and growth of carbon via a tip-growth mechanism, thereby promoting the formation of sub-50 nm CNTs rather than bulky irregular carbon particles. The Raman and Brunauer–Emmett–Teller analyses showed that the properties of the prepared CNTs depended on the synthetic parameters. This study provides deep insights into the mechanism underlying carbon synthesis through the electrochemical reduction of a molten carbonate salt.

In pressurized water reactor accident scenarios, the injection of water from the emergency core cooling system (ECCS) (ECC injection) might induce a pressurized thermal shock (PTS), affecting the reactor pressure vessel (RPV) integrity. Therefore, PTS is a vital research issue in reactor safety, and its analysis is essential for evaluating the integrity of RPVs, which determines the reactor life. The PTS analysis comprises a coupled analysis between thermal–hydraulic and structural analyses. The thermal–hydraulic approach is particularly crucial, and reliable computational fluid dynamic (CFD) simulations should play a vital role in the future because predicting the temperature gradient of the RPV wall requires data on the transient temperature distribution of the downcomer (DC). Since one-dimensional codes cannot predict the complex three-dimensional flow features during ECC injection, PTS is one reactor safety issue where CFD simulation can benefit from complement evaluations with thermal–hydraulic system analysis codes. This study reviewed from the viewpoint of the turbulence models most affecting PTS analysis based on papers published since 2010 on single- and two-phase flow CFD simulation for the experiment on PTS performed in the Rossendorf coolant mixing model (ROCOM), transient two-phase flow (TOPFLOW), upper plenum test facility (UPTF), and large-scale test facility (LSTF). The results revealed that in single-phase flow CFD simulation, where knowledge and experience are sufficient, various turbulence models have been considered, and many analyses using large eddy simulation (LES) have been reported. For two-phase flow analysis of air–water conditions, interface capturing/tracking methods were used in addition to two-fluid models. The standard and shear stress transport (SST) models were still in the validated phase, and various turbulence models have yet to be fully validated. In the two-phase flow analysis of steam–water conditions, many studies have used two-fluid models and Reynolds-averaged Navier-Stoke (RANS), and NEPTUNE_CFD, in particular, has been reported to show excellent prediction performance based on years of accumulated validation.

The pore-fracture structure of deep coal reservoirs is highly important for evaluating, exploring, and developing coalbed methane (CBM) resources. This study considers three coal samples from the DJ57 well in the Benxi Formation in the Daning–Jixian block on the eastern margin of the Ordos Basin as the research object. Based on the coal quality parameters of the coal samples, field emission scanning electron microscopy (FE-SEM), gas adsorption experiments, high-pressure mercury intrusion porosimetry (MIP), and microcomputed tomography (micro-CT) scanning were used to quantitatively characterize the nanoscale pores and microscale fractures in deep coal reservoirs and to evaluate the pore-fracture structure at different scales. The results reveal that the pore types in the Benxi Formation coal samples are diverse and include mainly organic matter (OM) pores, inorganic pores (intraparticle and interparticle pores), and microfractures. The organic pores are diverse in shape and mainly exhibit round, oval, and wedge shapes, while the microfractures exhibit slender stripes or serrated curves. The multiscale quantitative characterization of deep coal reservoir pores and fractures is based on a variety of pore characterization methods, and the pore and fracture volume distributions are mainly U-shaped, revealing the coexistence of micropores and microfractures. The volumes of micropores (0.3–2 nm), mesopores (2–50 nm), macropores (50 nm to 10 μm), and microfractures (>10 μm) account for 78.00%, 6.78%, 2.08%, and 13.14%, respectively, of the total pore volume (PV). Based on a full-scale pore-fracture splicing calculation, the total permeability of the Benxi Formation coal samples ranges from 5.77 to 28.22 mD. The observation results indicate that the microfractures are connected to each other, forming a network structure with strong connectivity. The microfractures are mainly associated with pore  μm, accounting for approximately 95% of the total permeability. Moreover, micropores in deep coal reservoirs provide a large space for CBM adsorption, and microfractures enhance the seepage capacity of CBM.

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy.

With the continual increase in CO₂ levels and toward a sustainable society, developing high-performance lithium-ion batteries (LIBs) is crucial. A suitable electrode design is the key to enhancing the quality of battery cells (e.g., cycle retention characteristics and rate capabilities), and the binder plays an important role in providing sufficient adhesion between the active material, conductive agent, and current collector. Despite significant advances in the development of novel binder materials and solutions that can be employed as anode and cathode materials, careful investigations and summaries of the assessment methods for binder materials remain lacking. In this review, we examine the different analyses used to assess the quality of binder materials and how they help in assessing the quality of the electrode design. In addition, future perspectives on binder assessment are presented, which can be applied to future research directed toward binder development for advanced LIBs or post-LIBs.