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Parametric optimization and structural feature analysis of humic acid extraction from lignite

Humic acid (HA) is a complex organic compound made up of small molecules. A variety of raw materials are used to manufacture HA, due to which the structure and composition of HA vary widely. In this study, nitric acid oxidation of two coal samples from Lakhra (Pakistan) was followed by HA extraction...

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Main Authors: Rashid, Tazien, Sher, Farooq, Jusoh, Mazura, Joya, Tayab Ali, Zhang, Shengfu, Rasheed, Tahir, Lima, Eder C.
Format: Article
Language:English
Published: Academic Press Inc. 2023
Subjects:
TP Chemical technology
Online Access:http://eprints.utm.my/107270/1/MazuraJusoh2023_ParametricOptimizationAndStructuralFeatureAnalysis.pdf
http://eprints.utm.my/107270/
http://dx.doi.org/10.1016/j.envres.2022.115160
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spelling my.utm.1072702024-09-01T06:35:59Z http://eprints.utm.my/107270/ Parametric optimization and structural feature analysis of humic acid extraction from lignite Rashid, Tazien Sher, Farooq Jusoh, Mazura Joya, Tayab Ali Zhang, Shengfu Rasheed, Tahir Lima, Eder C. TP Chemical technology Humic acid (HA) is a complex organic compound made up of small molecules. A variety of raw materials are used to manufacture HA, due to which the structure and composition of HA vary widely. In this study, nitric acid oxidation of two coal samples from Lakhra (Pakistan) was followed by HA extraction using 2.5, 3.0 and 3.5% KOH solutions. The impact of different operating parameters such as, the effect of KOH concentrations, KOH-coal proportion, extraction time and pH range influencing the HA extraction efficiency was optimally investigated. Commercial HA applications possess numerous challenges, including valuable applications and sub-optimal extraction techniques. A significant limitation of conventional experimental methods is that they can only investigate one component at a time. It is necessary to improve the current processing conditions, this can only be achieved by modelling and optimization of the process conditions to meet market demands. A comprehensive evaluation and prediction of HA extraction using Response Surface Methodology (RSM) are also being reported for the first time in this study. The maximum HA extraction efficiency of 89.32% and 87.04% for coal samples 1 and 2 respectively was achieved with the lowest possible pH of 1.09 (coal sample 1) and 1(coal sample 2), which is remarkably lower as compared to those reported in the literature for conventional alkaline extraction process. The model was evaluated for two coal samples through the coefficient of determination (R2), Root Means Square Error (RMSE), and Mean Average Error (MEE). The results of RSM for coal sample 1 (R2 = 0.9795, RMSE = 4.784) and coal sample 2 (R2 = 0.9758, RMSE = 4.907) showed that the model is well suited for HA extraction efficiency predictions. The derived humic acid from lignite coal was analyzed using elemental analysis, UV–Visible spectrophotometry and Fourier-transformed infrared (FTIR) spectroscopy techniques. Scanning Electron Microscopy (SEM) was applied to analyze the morphological modifications of the extracted HA after treatment with 3.5% KOH solution. For agricultural objectives, such as soil enrichment, enhancing plant growth conditions, and creating green energy solutions, this acquired HA can be made bioactive. This study not only establishes a basis for research into the optimized extraction of HA from lignite coal, but it also creates a new avenue for the efficient and clean use of lignite. Academic Press Inc. 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/107270/1/MazuraJusoh2023_ParametricOptimizationAndStructuralFeatureAnalysis.pdf Rashid, Tazien and Sher, Farooq and Jusoh, Mazura and Joya, Tayab Ali and Zhang, Shengfu and Rasheed, Tahir and Lima, Eder C. (2023) Parametric optimization and structural feature analysis of humic acid extraction from lignite. Environmental ResearchVolume, 220 (NA). pp. 1-12. ISSN 0013-9351 http://dx.doi.org/10.1016/j.envres.2022.115160 DOI : 10.1016/j.envres.2022.115160
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
Rashid, Tazien
Sher, Farooq
Jusoh, Mazura
Joya, Tayab Ali
Zhang, Shengfu
Rasheed, Tahir
Lima, Eder C.
Parametric optimization and structural feature analysis of humic acid extraction from lignite
description Humic acid (HA) is a complex organic compound made up of small molecules. A variety of raw materials are used to manufacture HA, due to which the structure and composition of HA vary widely. In this study, nitric acid oxidation of two coal samples from Lakhra (Pakistan) was followed by HA extraction using 2.5, 3.0 and 3.5% KOH solutions. The impact of different operating parameters such as, the effect of KOH concentrations, KOH-coal proportion, extraction time and pH range influencing the HA extraction efficiency was optimally investigated. Commercial HA applications possess numerous challenges, including valuable applications and sub-optimal extraction techniques. A significant limitation of conventional experimental methods is that they can only investigate one component at a time. It is necessary to improve the current processing conditions, this can only be achieved by modelling and optimization of the process conditions to meet market demands. A comprehensive evaluation and prediction of HA extraction using Response Surface Methodology (RSM) are also being reported for the first time in this study. The maximum HA extraction efficiency of 89.32% and 87.04% for coal samples 1 and 2 respectively was achieved with the lowest possible pH of 1.09 (coal sample 1) and 1(coal sample 2), which is remarkably lower as compared to those reported in the literature for conventional alkaline extraction process. The model was evaluated for two coal samples through the coefficient of determination (R2), Root Means Square Error (RMSE), and Mean Average Error (MEE). The results of RSM for coal sample 1 (R2 = 0.9795, RMSE = 4.784) and coal sample 2 (R2 = 0.9758, RMSE = 4.907) showed that the model is well suited for HA extraction efficiency predictions. The derived humic acid from lignite coal was analyzed using elemental analysis, UV–Visible spectrophotometry and Fourier-transformed infrared (FTIR) spectroscopy techniques. Scanning Electron Microscopy (SEM) was applied to analyze the morphological modifications of the extracted HA after treatment with 3.5% KOH solution. For agricultural objectives, such as soil enrichment, enhancing plant growth conditions, and creating green energy solutions, this acquired HA can be made bioactive. This study not only establishes a basis for research into the optimized extraction of HA from lignite coal, but it also creates a new avenue for the efficient and clean use of lignite.
format Article
author Rashid, Tazien
Sher, Farooq
Jusoh, Mazura
Joya, Tayab Ali
Zhang, Shengfu
Rasheed, Tahir
Lima, Eder C.
author_facet Rashid, Tazien
Sher, Farooq
Jusoh, Mazura
Joya, Tayab Ali
Zhang, Shengfu
Rasheed, Tahir
Lima, Eder C.
author_sort Rashid, Tazien
title Parametric optimization and structural feature analysis of humic acid extraction from lignite
title_short Parametric optimization and structural feature analysis of humic acid extraction from lignite
title_full Parametric optimization and structural feature analysis of humic acid extraction from lignite
title_fullStr Parametric optimization and structural feature analysis of humic acid extraction from lignite
title_full_unstemmed Parametric optimization and structural feature analysis of humic acid extraction from lignite
title_sort parametric optimization and structural feature analysis of humic acid extraction from lignite
publisher Academic Press Inc.
publishDate 2023
url http://eprints.utm.my/107270/1/MazuraJusoh2023_ParametricOptimizationAndStructuralFeatureAnalysis.pdf
http://eprints.utm.my/107270/
http://dx.doi.org/10.1016/j.envres.2022.115160
_version_ 1809136650479992832
score 13.211869

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