Prediction carbonization yields and the sensitivity analyses using deep learning neural networks and support vector machines

Altikat, A.; Alma, Mehmet Hakkı

doi:10.1007/s13762-022-04407-1

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…By testing ANN with 48 different network structures and combining sensitivity analysis, it was found that gas flow rate was the most critical factor in the generation of biochar, followed by holding time and pyrolysis temperature (Altikat and Alma 2022a). The deep artificial neural network (DNN) model showed that the effect of holding time on the yield of biochar and bio-oil was higher than other parameters under copyrolysis conditions (Altikat and Alma 2022b). It should be pointed out that ANN was proved to be able to predict the pyrolysis kinetics of biochar (Mayol et al 2018).…”

Section: Optimization Of Process Parametersmentioning

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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Section: Optimization Of Process Parametersmentioning

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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“…It can contain one or more of the following: N 2 , CO 2 , CH 4 , C x H y , and C x H y O z . SG is formed by the following processes: (i) partial oxidation [7,8]; (ii) wet reforming [9][10][11]; (iii) dry reforming [12][13][14]; (iv) autothermal reforming [15,16]; (v) hydrothermal gasification [17][18][19]; (vi) pyrolysis, or partial combustion, carbonization, or gasification [20][21][22][23]. SG can be manufactured from any carbonaceous (organic) material, including municipal waste.…”

Section: Introductionmentioning

confidence: 99%

Conversion of Waste Synthesis Gas to Desalination Catalyst at Ambient Temperatures

Antia¹

2023

Waste

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In this study, a continuous flow of a synthetic, dry, and acidic waste synthesis gas (WSG) (containing N2, H2, CO, CH4, and CO2) at ambient temperatures was first passed through a fixed bed reactor (FBR) containing halite + m-Fe0 and then a saline bubble column diffusion reactor (BCDR) containing m-Fe0. The FBR converted 47.5% of the CO + CH4 + CO2 into n-C0. Passage of the n-C0 into the BCDR resulted in the formation of the desalination catalyst (Fe0:Fe(a,b,c)@C0) + CH4 + CO + CO2 + CxHy, where 64% of the feed n-C0 was converted to gaseous products. The desalination pellets can remove >60% of the water salinity without producing a reject brine or requiring an external energy source. The gaseous products from the BCDR included: CxHy (where x < 6), CO, CO2, and H2.

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Bootstrap aggregation with Christiano–Fitzgerald random walk filter for fault prediction in power systems

Branco,

Cavalca,

Ovejero

2024

Electr Eng

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Prediction carbonization yields and the sensitivity analyses using deep learning neural networks and support vector machines

Cited by 6 publications

References 27 publications

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

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

Conversion of Waste Synthesis Gas to Desalination Catalyst at Ambient Temperatures

Bootstrap aggregation with Christiano–Fitzgerald random walk filter for fault prediction in power systems

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