Biochar and bio-oil fuel properties from nickel nanoparticles assisted pyrolysis of cassava peel

Egbosiuba, Titus Chinedu

doi:10.1016/j.heliyon.2022.e10114

Cited by 65 publications

(25 citation statements)

References 77 publications

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“…23 The HHV of biomass ranged from 12.1% to 25.5%, depending on the lignocellulose content, where the high lignin content in biomass exhibits higher HHV and has a higher energy conversion ability. 27 Proximate analysis covers the moisture content (MC), volatile matter (VM), fixed carbon (FC), and ash content (AC) analysis. The MC of biomass represents the mineralized aqueous solution (e.g., cations, anions, or non-charged species) in the cell fluid of biomass.…”

Section: Resultsmentioning

confidence: 99%

“…Initially, OPL was cleaned and sawn into a 10 mm × 2 mm cross-section. 15,27 The OPL was then subjected to thermal drying at 105 °C for 24 h. 28,29 The dried OPL was ground to powder using a Dickson DFY-500 electricgrinder supplied by SE Scientific Supplies (Ampang Jaya, Malaysia). The ground OPL was then sieved to obtain uniform fine particles of 250 μm in size.…”

Section: Biomass Preparation and Characterizationmentioning

confidence: 99%

“…17,46 In most literature, dried cassava peel, empty fruit bunch, palm kernel shell, oil palm fiber, peanut shell, and apricot shell yield 20-40% biochar under optimal conditions. 27,42,[47][48][49] Physicochemical properties of the OPL biochar under optimal conditions…”

Section: Optimization Of Operating Conditions and Model Validationmentioning

confidence: 99%

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Elaeis guineensisleaves for potential renewable sub‐bituminous coal: Optimization of biochar yield by response surface methodology and product characterization

Nik Pauzi,

Ramli,

Chan

et al. 2023

Biofuels Bioprod Bioref

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Pyrolysis of oil palm biomass residue can convert the rich organic matter in biomass into sustainable green energy, thereby addressing the challenge of surplus oil palm biomass residue generated during cultivation. Nonetheless, the pyrolysis of oil palm leaves (OPLs) has received limited attention. In this study, design of experiment‐response surface methodology (DoE‐RSM) was employed to identify the optimal combination of reaction temperature, residence duration, and nitrogen (N₂) flow rate for maximum biochar yield. The elemental analysis, high heating value (HHV), functional group, morphology, pore structure, and thermal behavior were assessed. By varying the operational parameters using response surface methodology, the biochar yield increased by 32.6–85.3%. The DoE‐RSM predicted a theoretical yield of 52.1% at 344 °C, 146 min, and 3.2 scfh N₂ flow rate. The experiment confirmed a comparable yield of 53.7% at the stated parameters. The HHV of 24.14 MJ kg−1 was recorded under optimal conditions, and was comparable to sub‐bituminous and bituminous coal. Fourier transform infrared analysis validated the OPL biochar by weakening O‐H, C=O, C‐OH, and C‐H peaks. Scanning electron microscopy images revealed enlarged pores and altered morphology in the OPL biochar. X‐ray diffraction showed lower crystallinity of the OPL biochar than the feedstock. Raman spectroscopy showed higher ID/IG, indicating a more disordered carbon structure in the OPL biochar. Thermogravimetric analysis confirmed higher temperature for main devolatilization of OPL biochar, and the differential thermogravimetric analysis curve pattern resembled that of Mukah Balingan coal. The findings are helpful for an initiative to convert a large amount of leftover OPLs produced during cultivation and to turn them into high value‐added material for a sustainable contribution to a circular carbon economy.

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Section: Resultsmentioning

confidence: 99%

Section: Biomass Preparation and Characterizationmentioning

confidence: 99%

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Elaeis guineensisleaves for potential renewable sub‐bituminous coal: Optimization of biochar yield by response surface methodology and product characterization

Nik Pauzi,

Ramli,

Chan

et al. 2023

Biofuels Bioprod Bioref

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“…Machine learning can quickly and efficiently accelerate experimental screening, so it is widely applied in the modeling and optimization of biochar synthesis. Because there are many products in the low oxygen or anaerobic pyrolysis of biomass (Egbosiuba 2022;Liu et al 2021a;Thiruvengadam et al 2021), covering biochar, biooil and biogas, accurate prediction of biochar production is the key. Secondly, the specific surface area, pore size distribution and the type and number of surface functional groups of biochar are determined by the composition of raw materials, pyrolysis temperature and pyrolysis mode.…”

Section: Synthesis Optimization Of Biocharmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

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“…Before now, classical adsorbents namely: zeolites [ 25 ], synthetic resins [ 26 ], biowastes [ 27 ], biochar [ 28 ] and poly (methyl methacrylate) [ 29 ] have been randomly used to decontaminate wastewater. Generally, zeolites are aluminosilicate minerals that are hydrated to be in a microporous structure and used extensively as adsorbent material to remove hazardous contaminants from wastewater [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Column adsorption of biological oxygen demand, chemical oxygen demand and total organic carbon from wastewater by magnetite nanoparticles-zeolite A composite

Kovo¹,

Alaya-Ibrahim²,

Abdulkareem³

et al. 2023

Heliyon

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Biochar and bio-oil fuel properties from nickel nanoparticles assisted pyrolysis of cassava peel

Cited by 65 publications

References 77 publications

Elaeis guineensisleaves for potential renewable sub‐bituminous coal: Optimization of biochar yield by response surface methodology and product characterization

Elaeis guineensisleaves for potential renewable sub‐bituminous coal: Optimization of biochar yield by response surface methodology and product characterization

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Column adsorption of biological oxygen demand, chemical oxygen demand and total organic carbon from wastewater by magnetite nanoparticles-zeolite A composite

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