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In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the cause of an accident can prevent catastrophic consequences. Traditional methods mostly focus on combining model algorithms and utilize intricate composite model neural network architectures. However, it is crucial to investigate whether greater complexity necessarily leads to better performance. In addition, the consideration of the impact of dataset construction and data preprocessing on model performance is also needed for model building. This paper proposes a framework named DeepLOCA-Lattice to experiment with different preprocessing approaches to fundamental deep learning models for a comprehensive analysis of the diagnosis of LOCA breach size. The DeepLOCA-Lattice involves data preprocessing via the lattice algorithm and equal-interval partitioning and deep-learning-based models, including the multi-layer perceptron (MLP), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and the transformer model in LOCA breach size diagnosis. After conducting rigorous ablation experiments, we have discovered that even rudimentary foundational models can achieve accuracy rates that exceed 90%. This is a significant improvement when compared to the previous models, which yield an accuracy rate of lower than 50%. The results interestingly demonstrate the superior performance and efficacy of the fundamental deep learning model, with an effective dataset construction approach. It elucidates the presence of a complex interplay among diagnostic scales, sliding window size, and sliding stride. Furthermore, our investigation reveals that the model attains its highest accuracy within the discussed range when utilizing a smaller sliding stride size and a longer sliding window length. This study could furnish valuable insights for constructing models for LOCA breach size estimation. [ABSTRACT FROM AUTHOR]

The failure of wellbore sealing will cause leakage of greenhouse gases, such as carbon dioxide and methane, which will harm oil and gas recovery and environmental safety. Cementing is an important part of wellbore sealing. Only good cementing can keep the wellbore seal for a long time and improve the well life. In this study, we considered the construction of a horizontal shale oil well in eastern China as the background and analysed the rheological properties of the annulus fluid. We developed a displacement motion model and a calculation model for the annulus dynamic equivalent circulation density, and numerical simulations were used to study the impact of the dosage and injection sequence of the pad fluid on the displacement efficiency and annulus dynamic equivalent circulation density. The results show that when the pad fluid is composed completely of flushing fluid, the displacement performance is better than that of the spacer. By increasing the dosage of the flushing fluid from 0.3 times the annular volume to 1.0 times, the displacement efficiency can be increased by 3.3%, and the retention of the drilling fluid is also reduced by 3.6%. However, it can lead to a significant reduction in the annulus dynamic equivalent circulation density and increase in the risk of leakage. After adding the spacer, the structure of the flushing fluid–spacer provides the optimal injection sequence. Considering the application status in the field example well, it was shown that it can not only ensure the safety of cementing operations, but also improve the displacement efficiency. The results of this study have important theoretical significance and application value and can provide guidance for the optimisation design of the engineering scheme. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions. [ABSTRACT FROM AUTHOR]

Herein, DES lignin was obtained by pretreatment of grapevine with a deep eutectic solvent (ChCl-LA). A novel chitosan-DES lignin composite aerogel material (CS-LIG aerogel) was prepared to adsorb methylene blue (MB), Congo red (CR), catechin (C), and epicatechin (EC). The CS-LIG aerogel was systematically characterized by modern technological instruments. It was demonstrated that the DES lignin was successfully incorporated and had an important effect on the morphological structure and adsorption of dyes and natural products in the aerogel. The adsorption kinetic models for both adsorbed CR and MB are pseudo-second-order models. Adsorption isotherms followed Langmuir for the adsorption of CR and Freundlich for the adsorption of MB. The π-π interaction and hydrogen bonding of DES lignin aromatic groups in CS-LIG aerogels were responsible for the adsorption of C and EC with 86.42 % and 90.85 % removal rates, respectively. This study opens a new avenue for the high-value utilization of DES lignin and the preparation of chitosan-based composites for the adsorption of dyes and purification of natural products. • DES lignin incorporation enhances the thermal stability of CS aerogel. • Adsorption of dyes on CS-LIG aerogels is mainly electrostatic and hydrogen bonding. • The CS-LIG aerogel can selectively adsorb C and EC from proanthocyanidins. • Lignin π-π interactions of aromatic groups are primarily reason for adsorb C and EC. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DESs) have been extensively applied to pretreat lignocellulose; however, comparative research on acidic and alkaline DES pretreatment is relatively lacking. Herein, pretreatment of grapevine agricultural by-products with seven DESs were compared in terms of removal of lignin and hemicellulose and component analysis of the pretreated residues. Among the tested DESs, both acidic choline chloride-lactic (CHCl-LA), and alkaline potassium carbonate-ethylene glycol (K 2 CO 3 -EG) were effective in delignification. Thereafter, the CHCl-LA and K 2 CO 3 -EG extracted lignin was compared by analyzing their physicochemical structure changes and antioxidant properties. The results showed that the thermal stability, molecular weight, and phenol hydroxyl percentage of CHCl-LA lignin were inferior to K 2 CO 3 -EG lignin. It was found that the high antioxidant activity of K 2 CO 3 -EG lignin was mainly attributed to the abundant phenol hydroxyl, guaiacyl (G), and para-hydroxy-phenyl (H). By comparing acidic and alkaline DES pretreatments and their lignin nuances in biorefining, novel insights are derived for the scheduling and selection of DES for lignocellulosic pretreatment. • CHCl-LA pretreatment has a great advantage in removing lignin and hemicellulose. • K 2 CO 3 -EG pretreatment benefits lignin structure and thermal stability preservation. • Significant chemical shift in the guaiacyl lignin unit of K 2 CO 3 -EG lignin. • K 2 CO 3 -EG lignin retains more phenol hydroxyl have better antioxidant properties. [ABSTRACT FROM AUTHOR]

During the cementing process, the oil-well cement (OWC) inevitably comes into contact with oil-based mud (OBM), which contaminates the OWC due to the incompatibility between OBM and the OWC. As a solution, hydrophilic surfactants can reduce interfacial tension and enhance the wetting compatibility of oil and water. Therefore, adding surfactants to the OWC is expected to alleviate contamination caused by OBM. In this study, a new strategy was proposed to combine non-ionic surfactants and anionic surfactants to form a composite surfactant (OP-S2), which overcomes the defect that excessive use of anionic surfactants deteriorates the performance of the OWC—showing great potential in adjusting the compatibility between OBM and the OWC and reducing cement contamination. The mechanism of contamination alleviation during the cementing process was elucidated by comparing the anticontamination properties of a single non-ionic surfactant (OP-S1) and OP-S2. In addition, molecular dynamics (MD) and dissipative particle dynamics (DPD) simulations were used to verify the mechanism of different types of surfactants in removing contamination further, providing theoretical guidance for future research on anti-contamination agents. [Display omitted] • Combining non-ionic and anionic surfactants to form a composite surfactant (OP-S2). • OP-S2 solves the defect that excessive single surfactant worsens OWC performance. • OP-S2 can meet the safety and quality requirements of ancient Wells. • Through MD and DPD simulation, the mechanism of surfactant removal was explored. [ABSTRACT FROM AUTHOR]

Encapsulation has been extensively applied in the self-healing of cementitious materials due to the diversity of encapsulating materials. Nonetheless, the weak interfacial bonding of polymer capsules and cementitious materials commonly reduces the healing efficiency, the preparation of which is complicated and not environmentally friendly. In this paper, a nano-silica reinforced calcium alginate/epoxy resin capsule (S1) was prepared using a simple and green chelating method at room temperature. The strong bonding of the shell containing nano-silica with cementitious materials makes it easier for capsules to be triggered to achieve higher self-healing efficiency. Their effects on cementitious materials were measured to determine the reinforcing mechanisms, microstructure and mechanical properties of capsules. The experimental results demonstrated that S1 possessed higher thermal stability (310.0 °C), higher mechanical strength and lower water absorption (46.95% in cement filtrate) than samples without nano-silica (S0). The compressive strength of S1 cementitious material increased by 18.9% compared with S0 cementitious material because of the decrease in porosity and the increased mechanical properties of the S1 capsule. Moreover, the self-healing efficiency of S1 cementitious material improved greatly by 112.7%, which was related to the more efficient rupture of S1. The nano-silica in the capsule increased the surface roughness of S1 and reacted with cement matrix via a pozzolanic reaction, thereby achieving efficient self-healing of cementitious system through a stronger interface. [Display omitted] • A nano-silica reinforced calcium alginate/epoxy resin capsule was prepared through green chelation at room temperature. • The improvement in thermal stability mechanical strength of capsules by adding nano-silica were analyzed. • The influence of capsules on the compressive strength and self-healing efficiency of cementitious system was investigated. • The effect of nano-silica on the interface between cement matrix and capsules was explored. [ABSTRACT FROM AUTHOR]