Adsorption mechanism of ammonia nitrogen and phenol on lignite surface: Molecular dynamics simulations and quantum chemical calculations

Sun, Meijie; Gu, Suqian; Li, Xiangyang; Zheng, J. P.; Xu, Zhiqiang; Chen, Yang; He, Hao; Wang, Lufan

doi:10.1016/j.fuel.2022.127157

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…Principal component analysis (PCA) is another dimensionality reduction algorithm commonly used by chemical engineers to find the features that best explain the data in the training set [59]. Additional applications of PCA include the detection of outliers [60]. Other algorithms for spotting anomalies include deep belief subspace analysis (DBSCAN) and long short-term memory (LSTM) [61].…”

Section: Machine Learning; Data Representations and Modelsmentioning

confidence: 99%

Application of machine learning in chemical engineering: outlook and perspectives

Al Sharah,

Abu Owida,

Alnaimat

et al. 2024

IJ-AI

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Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.

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Section: Machine Learning; Data Representations and Modelsmentioning

confidence: 99%

Application of machine learning in chemical engineering: outlook and perspectives

Al Sharah,

Abu Owida,

Alnaimat

et al. 2024

IJ-AI

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show abstract

“…17 The microwave absorption properties of organic sulfur compounds in most coals in the microwave environment at frequency of 915 MHz are higher than that at 2450 MHz. 18 Currently, the application of quantum chemical calculations in coal is in the development stage, and some scholars use quantum chemical to study the reaction mechanism of action related to coal. Yin et al 19 analyzed the main interactions among the structures of ammonia nitrogen, phenol, and lignite by quantum chemical simulations and found the adsorption mechanism between lignite and typical fractions in wastewater.…”

Section: Introductionmentioning

confidence: 99%

“…The sulfur occurrence environment in coal, such as the length of aliphatic chain, aromaticity, and type of heteroatom, remarkably affects the dielectric properties of thiophene 17 . The microwave absorption properties of organic sulfur compounds in most coals in the microwave environment at frequency of 915 MHz are higher than that at 2450 MHz 18 …”

Section: Introductionmentioning

confidence: 99%

Microwave absorption properties and quantum chemistry of organic sulfur compounds in coal

Tao

Wang

et al. 2023

Asia-Pacific J Chem Eng

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The microwave absorption properties of dielectrics determine the promotion degree of microwave energy to chemical reaction. The wave‐absorbing characteristics of organic sulfur compounds in high‐sulfur coking coal need to be studied to improve the efficiency of microwave desulfurization of coal. In this study, the imaginary part of complex dielectric constants (ε′′) and dielectric loss tangent (tan σ) of benzene mercaptan, difurfuryl disulfide, and benzothiophene were characterized. The dipole moment, molecular configuration, and frontier molecular orbital energy of organic sulfur compounds were simulated and calculated under the different external energy fields. The migration law of mercaptan, sulfide, and thiophene in coal in microwave field were analyzed. The results show that the ε'′ and tan σ of benzothiophene slightly changed with the change in microwave frequency in the frequency range of 500–2500 MHz, and the microwave absorption properties are weaker than those of benzene mercaptan and difurfuryl disulfide. Therefore, difurfuryl disulfide and benzene mercaptan molecules are more active in the microwave field than benzothiophene. The sulfur components in benzene mercaptan and difurfuryl disulfide can dissociate in the form of small inorganic sulfur molecules of carbon sulfides and disulfides, respectively. After microwave irradiation, the relative content of mercaptan and thioether in coal was decreased, whereas the relative content of thiophene remained almost unchanged.

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Simulation study of the effect of OGs on the adsorption of formaldehyde on modified activated carbon

Hu,

Zhu

2024

J Mol Model

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Adsorption mechanism of ammonia nitrogen and phenol on lignite surface: Molecular dynamics simulations and quantum chemical calculations

Cited by 10 publications

References 45 publications

Application of machine learning in chemical engineering: outlook and perspectives

Application of machine learning in chemical engineering: outlook and perspectives

Microwave absorption properties and quantum chemistry of organic sulfur compounds in coal

Simulation study of the effect of OGs on the adsorption of formaldehyde on modified activated carbon

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