SinoTechIntel Academic Portal
Open AccessDOI: 10.1016/j_cjche_1448Original Research

Structural parameters and molecular model of Shendong subbituminous coal

Xiaoping Su¹,Ning Li¹,Longjian Li¹,Reyila Tuerhong¹,Yongchong Yu¹,Ping Zhang¹,Qiong Su¹,Tao Shen¹,Ming Sun¹,Xiaoxun Ma¹

Northwest Minzu University, Lanzhou 730124, China

Read Executive PreviewQuick FAQ
Structural parameters and molecular model of Shendong subbituminous coal
Graphical Abstract / Figure
Published In
Chinese Journal of Chemical Engineering
Published:September 11, 2024Edition:Vol. 76, Issue 1 • pp. 124-134Citation:Xiaoping Su et al. (2024), Chinese Journal of Chemical Engineering
Impact Factor3.8 (Q1 - Elsevier)
Sponsored Research Partner
Keywords & Index Terms:Subbituminous coalStructural parameters13C CP/MAS SSNMRMolecular modelFTIRXRDXPSCoal structure

Key Takeaways & Executive Findings

  • • Comprehensive structural characterization of Shendong subbituminous coal using FTIR, XRD, XPS, and 13C SSNMR. • Determination of key structural parameters: normalized formula C100H68.5O35.7N1.2S0.2, average aromatic rings 1.98, and microcrystalline parameters (d002=0.1832 nm, Lc=1.4688 nm, La=2.0852 nm, Nc=9.017). • Construction of a molecular model with atom composition C214H214O49N2S, validated against experimental 13C SSNMR spectra. • Insights into the chemical structure of subbituminous coal, providing a basis for its efficient transformation and utilization.
Sponsored Research Highlight

Abstract

Coal has a highly complex chemical structure, similar to polymers, coal is a macromolecular structure composed of a large number of “similar compounds”, which is called the basic structural unit. Understanding coal structure is the basis of its transformation and utilization. Shendong (SD) coal was analyzed by FTIR, XRD, XPS, and NMR. The results show that SD coal normalized structure formula is C100H68.5O35.7N1.2S0.2 and the average number of aromatic rings is 1.98. eCH2d content accounts for about 82% in aliphatic CeH region, and the ratio of ether bond CeO, aromatic ether CeO and C]O is about 2:1:11 in oxygen-containing functional group region. The d002, LC, La and NC of SD coal micro-crystalline structure parameters are 0.1832 nm, 1.4688 nm, 2.0852 nm and 9.017, respectively. Aromatic carbon and aliphatic carbon ratios of SD coal are 55.67% and 29.97%, aromatic cluster size and average methylene chain length are 0.224 and 1.817. Based on these structural parameters, molecular model of SD coal was constructed with 13C SSNMR experimental spectra as a reference. The model was constructed with an atom composition of C214H214O49N2S.

1. Introduction

China is relatively rich in coal resources and subbituminous coal accounts for about 40% of the total coal [1]. Shendong coalfield (SD), a kind of subbituminous coal, is one of the eight largest coalfields in the world, with the advantages of convenient mining and excellent quality [2]. Understanding SD coal structure is the basis of its transformation and utilization.

It is well-known that coal has a highly complex chemical structure, similar to polymers, coal is a macromolecular structure composed of a large number of “similar compounds” known as basic structural unit [3,4]. Due to the complexity and diversity of coal, its molecular structure has not been fully understood [5]. The methods to study coal structure are mainly chemical and physical methods. The chemical method is complicated, time-consuming and low sensitivity, which is gradually replaced by physical method [6]. The earliest structural models were based on conventional analyses such as proximate analysis and ultimate analysis [7]. With the development of a series of advanced analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and solid-state nuclear magnetic resonance spectroscopy (SSNMR), more than 125 coal molecular models have been proposed [8,9]. Wang et al. [10] investigated the Inner Mongolia lignite structure by using XRD, 13C NMR, TEM and XPS techniques and constructed its molecular model, the molecule formula is C166H130O49. Lin et al. [11] investigated Indonesian brown structure by using 13C NMR, FTIR, XPS and XRD, the results indicated that the ratio of bridge carbon/surrounding carbon is 0.0696 and the molecule formula for Indonesian brown coal is C190H170O50N2. Baysal et al. [12] investigated Turkish lignites by using XRD, 13C NMR, FTIR and XPS, the results shown that the XRD aromaticity of the coals ranged between 0.642 and 0.777. Okolo et al. [13] investigated South African bituminous coal by using XRD, FTIR, 13C NMR and TEM, the results shown that the aromaticity of the samples determined by 13C NMR ranged from 0.74 to 0.87. Zhang et al. [14] investigated Xishan bituminous coal by using ultimate analysis, TEM, LD-TOF M

SinoTechIntel Interactive Document Reader
Page 1–5 of Preview
100%
Download Full PDF

Loading authentic research manuscript (Pages 1–5)...

Sponsored Research Partner
Cite This Research Paper
Xiaoping Su, Ning Li, Longjian Li, Reyila Tuerhong, Yongchong Yu, Ping Zhang, Qiong Su, Tao Shen, Ming Sun, Xiaoxun Ma (2024). Structural parameters and molecular model of Shendong subbituminous coal. Chinese Journal of Chemical Engineering. https://doi.org/10.1016/j_cjche_1448
SinoTechIntel Academic & Legal Disclaimer

Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.

Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.

Frequently Asked Questions

What is the molecular formula of Shendong subbituminous coal?

The normalized structure formula is C100H68.5O35.7N1.2S0.2, and the constructed molecular model has an atom composition of C214H214O49N2S.

Which analytical techniques were used to characterize Shendong coal?

The coal was analyzed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and solid-state nuclear magnetic resonance spectroscopy (SSNMR).

What are the key structural parameters of Shendong coal?

Key parameters include an average aromatic ring number of 1.98, microcrystalline parameters (d002=0.1832 nm, Lc=1.4688 nm, La=2.0852 nm, Nc=9.017), and aromatic carbon ratio of 55.67%.

How was the molecular model of Shendong coal constructed?

The molecular model was constructed based on structural parameters derived from experimental analyses, with 13C SSNMR spectra as a reference, resulting in a model with formula C214H214O49N2S.

Why is understanding coal structure important?

Understanding coal structure is fundamental for its efficient transformation and utilization, as it provides insights into its chemical composition and reactivity.

Recommended Scientific Literature & Research Partners

Related Technical Papers & Translations

Research Paper
Design and optimization of a high-efficiency distillation process for cellulosic fuel ethanol integrated with thermal coupling and molecular sieve adsorption

Design and optimization of a high-efficiency distillation process for cellulosic fuel ethanol integrated with thermal coupling and molecular sieve adsorption

To address the challenges of high energy consumption and prominent costs in the traditional three-columns distillation process for cellulosic fuel ethanol, a distillation—molecular sieve coupling separation process is proposed. This process integrates a three-column (crude distillation column, first distillation column, second distillation column) system with a 3A molecular sieve adsorption deep dehydration unit. A thermal coupling network is constructed via differential pressure design (steam from medium/high-pressure columns as mutual heat sources, reboiler liquid waste heat for feed preheating), and molecular sieve adsorption conditions are optimized. The study first performs a thermodynamic consistency test on the ethanol—water system, determines optimal non-random two-liquid (NRTL) model binary interaction parameters via experimental data regression for Aspen Plus simulation. Aiming at minimum total annual cost (TAC), Aspen Plus is used to optimize process parameters (theoretical tray number, feed location, reflux ratio, side-draw position, etc.). Economic analysis shows this process reduces CO2 emission costs by 27.56%, TAC by 15.58% (to 5.123 × 106 USD·a-1), and increases ethanol purity to >99.6%, providing an effective solution for green, efficient separation.

Read Abstract & PDF
Research Paper
A cohesion loss model for determining residual strength of deep bedded sandstone

A cohesion loss model for determining residual strength of deep bedded sandstone

Rock residual strength, as an important input parameter, plays an indispensable role in proposing the reasonable and scientific scheme about stope design, underground tunnel excavation and stability evaluation of deep chambers. Therefore, previous residual strength models of rocks established were reviewed. And corresponding related problems were stated. Subsequently, starting from the effects of bedding and whole life-cycle evolution process, series of triaxial mechanical tests of deep bedded s

Read Abstract & PDF
Research Paper
Federated model with contrastive learning and adaptive control variates for human activity recognition

Federated model with contrastive learning and adaptive control variates for human activity recognition

Recent attention to privacy issues demands a communication-safe method for training human activity recognition (HAR) models on client activity data. Federated learning (FL) has become a compelling technique to facilitate model training between the server and clients while preserving data privacy. However, classical FL methods often assume independent and identically distributed (IID) data among clients. This assumption does not hold true in practical scenarios. Human activity in real-world scena

Read Abstract & PDF