Simulation and Experimental Study of Solid-Liquid Extraction of Coal Tar Residue Based on Different Extractants.

ACS Omega

School of Chemistry and Chemical Engineering, Shaanxi Key Laboratory of Low Metamorphic Coal Clean Utilization, Yulin University, Yulin 719000, People's Republic of China.

Published: December 2023

Coal tar residue (CTR) is recognized as a hazardous industrial waste with a high carbon content and coal tar consisting mainly of toxic polycyclic aromatic hydrocarbons (PAHs). The coal tar in CTR can be deeply processed into high-value-added fuels and chemicals. Effective separation of coal tar and residue in CTR is a high-value-added utilization method for it. In this paper, ethyl acetate, ethanol, and -hexane were chosen as extractants to study the extraction process of coal tar from CTR, considering the mass transfer in the liquid phase outside the CTR particles and the diffusion inside the CTR particles, and a mathematical model of the solid-liquid extraction process of CTR was established based on Fick's second law. First, the mass-transfer coefficients () and effective diffusion coefficients () of ethyl acetate, ethanol, and -hexane in solid-liquid extraction at 35 °C were determined to be 1.54 × 10 and 4.99 × 10 m·s, 1.14 × 10 and 3.57 × 10 m·s, and 1.01 × 10 and 3.48 × 10 m·s, respectively. Furthermore, the simulated values obtained by the model also maintained a high degree of agreement with the experimental results, which indicates the high accuracy prediction of the model. Finally, the model was used to investigate the effects of the solvent-solid ratio, temperature, and stirring speed on the extraction rates with the three extractants. According to the analysis with gas chromatography-mass spectrometry (GC-MS), among the three solvents, -hexane extracted the highest content of aliphatic hydrocarbons (ALHs), ethyl acetate extracted the highest content of oxygenated compounds (OCs), and ethanol extracted the highest content of aromatic hydrocarbons (ARHs). The model and experimental data can be used to provide accurate predictions for industrial utilization of CTR.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10734026PMC
http://dx.doi.org/10.1021/acsomega.3c06290DOI Listing

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