Constructing Straight Pores and Improving Mechanical Properties of GangueBased Porous Ceramics

Xu, Hang; Du, Huiling; Kang, Le; Cheng, Qiudi; Feng, Danni; Xia, Siyu

doi:10.32604/jrm.2021.016090

Cited by 72 publications

(29 citation statements)

References 32 publications

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“…Based on the Archimedes principle [ 15 ], the apparent porosity is measured, which is shown in Equation (2):

where M 1 (g) and M 2 (g) are the oven-dried weight and the fully submerged weight of the sample, respectively. With the boiling time of 1 h, and M 3 being the fully impregnated weight while suspended in deionized water,

(%) is the apparent porosity of the samples.…”

Section: Methodsmentioning

confidence: 99%

“…However, its use rate is still limited. Meanwhile, coal gangue (CG), a by-product in the coal industry, has been widely studied and used in studies [ 13 , 14 , 15 , 16 ]. Currently, many researchers have focused on studying the use of CG in pavements and cementing building materials thanks to its high content of Al 2 O 3 and SiO 2 .…”

Section: Introductionmentioning

confidence: 99%

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Microstructure Evolution and Improved Permeability of Ceramic Waste-Based Bricks

Zhou

Kang

et al. 2022

Materials

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The resource and large-scale utilization of waste ceramic materials, magnesium slag, and coal gangue are one of the important ways for the sustainable development in metallurgy, coal, and other related enterprises. In this paper, waste ceramic materials were used as aggregates; coal gangue and magnesium slag were used as mixed binder; and the all solid-waste-based permeable bricks with excellent performance were prepared by forming pressure at 5 MPa. The mechanical properties and water permeability of the all-solid-waste-based permeable bricks were evaluated. The results proved that the porous channel of permeable brick is mainly composed of waste ceramic materials with a particle size of 2–3 mm. Pore structures below 200 μm were mainly composed of fine aggregate and mixed binder. Using 60% coarse aggregate, 20% fine aggregate, 10% coal gangue, and 10% magnesium slag as raw materials, the all-solid-waste-based permeable bricks were obtained by pressing at 6 MPa and sintering at 1200 °C, which exhibited the best performance, and its water permeability, compressive strength, and apparent porosity were 1.56 × 10−2 cm/s, 35.45 MPa, and 13.15%, respectively. Excellent water permeability, compressive strength, and apparent porosity of the all solid-waste-based permeable bricks were ascribed to the high content of connecting open pores, and closely adhesive force were ascribed to the porous microstructure constructed by the grading of waste ceramic materials and the tight conjoined points of the liquid phases in coal gangue and magnesium slag at a high sintering temperature.

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“…Based on the Archimedes principle [ 15 ], the apparent porosity is measured, which is shown in Equation (2):

(%) is the apparent porosity of the samples.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microstructure Evolution and Improved Permeability of Ceramic Waste-Based Bricks

Zhou

Kang

et al. 2022

Materials

Self Cite

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“…With the development of artificial intelligence technology, the application of deep learning in various fields has gradually matured. [54][55][56][57][58] Deep learning methods based on neural networks have recently attracted the attention of researchers in the field of battery state estimation. They have been introduced into the SOC estimation of lithium-ion batteries, [59][60][61][62][63][64] because of the ability of these methods to automatically extract features and their good generalization performance.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

233

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Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

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“…They include those resulting from electrical “abuse” (over‐charging, fast charging, over‐discharging, and external short‐circuit) and mechanical abuse (compression and container breaching). In recent years, due to the mechanical misuse of lithium batteries, fire accidents caused by battery thermal runaway of electric vehicles have occurred from time to time 12–15 . Binghe Liu et al studied that under the condition of mechanical abuse, LIBs would successively undergo mechanical deformation, internal short‐circuit, thermal runaway, and explosion/fire 16 …”

Section: Introductionmentioning

confidence: 99%

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Wang

Duan

et al. 2022

Intl J of Energy Research

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Summary With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights Use EIS to quickly and effectively predict the internal temperature changes of LIBs. No hardware temperature sensors and thermal model are required. The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed.

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Constructing Straight Pores and Improving Mechanical Properties of GangueBased Porous Ceramics

Cited by 72 publications

References 32 publications

Microstructure Evolution and Improved Permeability of Ceramic Waste-Based Bricks

Microstructure Evolution and Improved Permeability of Ceramic Waste-Based Bricks

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

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