Two Stages Thermal and Catalytic Cracking of Polyethylene Terephthalate to Fuel Production

Almutalabi, Shams; Alzuhairi, Mohammed; Hashim, Fadhil A.

doi:10.18280/ijdne.160615

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Compared with previous studies that typically relied only on SST [12][13][14][15][16], the present results also took into account other remotely sensed factors, resulting in much accurate forecasts. Although there were a few studies that analysed multiple factors [30,31,34], neither accuracy on coral bleaching analysis nor monitoring were reported. Furthermore, we also developed a web-based application that was able to make spatial queries to remotely sensed cloud repository, hosted by GEE, by using well-known Python language and the most recent web frameworks.…”

Section: Discussionmentioning

confidence: 99%

Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data

Udomchaipitak¹,

Boonnam²,

Puttinaovarat³

et al. 2022

IJDNE

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With the rapid changes in Earth climates, coral bleaching has been spreading worldwide and getting much severe. It is considered an imminent threat to marine animals as well as causing adverse impacts on fisheries and tourisms. Environmental agencies in affected regions have been made aware of the problem and hence starting to contain coral bleaching. Thus far, they often rely on conventional site survey to determine suitable sites to intervene and commence coral reef reviving process. With the recent advances in remote sensing technology, sea surface temperature (SST), acquired by satellites, has become a viable delegate to coral bleaching. Predicting coral bleaching based solely on SST is limited, as it is only one of many determinants. In addition, areas with different SST levels also exhibit different bleaching characteristics. Hence, area specific models are important for appropriately monitoring the events. Thus far, forecasting the bleaching based on SST alone has limited accuracy, because other disregarded factors are found equally influential. These are turbidity, salinity, and wind speed. Taken into account these geospatial factors, this paper evaluates different machine learning (ML) algorithms, on forecasting coral bleaching levels. Compared with official survey data, it was found that random forest (RF) gave the most accurate results, with accuracy and Kappa of 88.24% and 0.83, respectively. To further assist involved agencies in making data driven solutions to this problem, mapping forecasted by RF were visualized on a web application, implemented with Python and the most recent web frameworks and database systems. The proposed scheme could be extended to modelling coral bleaching in other areas, hence greatly reducing delayed in data acquisition and survey costs.

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Section: Discussionmentioning

confidence: 99%

Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data

Udomchaipitak¹,

Boonnam²,

Puttinaovarat³

et al. 2022

IJDNE

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“…The sulfur content in the starting material and products was defined using SPECTROSCAN Instrument. The physico-chemical characteristics of the catalysts were studied using BET methods, TEM microscopy, and TPD of ammonia [28,[31][32][33][34][40][41][42].…”

Section: Catalyst Sample Catalyst Compositionmentioning

confidence: 99%

Development<strong> </strong>and Testing of Modified Alumina Catalysts for Hydrotreating Petroleum Gasoline Fraction

Tuktin¹,

Omarova²,

Sassykova³

et al. 2022

Preprint

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The oil produced in the oil fields of the Republic of Kazakhstan contains a high percentage of sulfur. Synthesis and improvement of the properties of catalytic systems for the production of fuels with high octane number and low sulfur content is currently an urgent task for Kazakhstan. In this study, catalytic systems with a new composition based on zeolites with the addition of rare-earth metals (E) and phosphorus (P) have been prepared and tested in the process of the catalytic hydrotreating of straight-run gasoline and gasoline of catalytic cracking. In case of NiO-MoO3-E-P-HZSM-HY-Al2O3 catalyst, the octane rating of the gasoline after hydro-processing was increased to 88-90, which is much higher than for other catalysts. The octane number of straight-run gasoline up to 400°C is a maximum of 90 (Research Method) and 83.7 (Motor Method). At the same time, the sulfur content in the resulting gasoline decreases from 0.0088% to 0.0011%. In the case of catalytic cracking gasoline, the sulfur content is reduced from 0.0134% to 0.0012%. The smallest residual sulfur content in the final product, 0.0005% is revealed in case of catalyst CoO-WO3-E-P-HZSM-HY-Al2O3, and it is 2-4 times lower than for catalysts CoO-MoO3-E-P-HZSM-HY-Al2O3 and NiO-MoO3-E-P-HZSM-HY-Al2O3. These amounts of sulfur residue in raw materials is lower than that required by the Euro-5 Standard. The surface of the prepared catalysts was 211.0-274.0 m2/g, diameter of pores d ≈ 1.5-2.5 nm and d ≈ 7.0 nm. The total pore volume of the catalysts was not higher than 0.28-0.41 ml/g. The catalysts developed in this study can be used for hydrotreating raw materials and producing high-octane gasoline with a low sulfur content, corresponding in its characteristics to the Euro-5 Standard.

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Catalytic Pyrolysis of Polyethylene Terephthalate and Its Copyrolysis with Polyethylene

Gulab,

Malik,

Hussain

2024

Environmental Engineering Science

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Two Stages Thermal and Catalytic Cracking of Polyethylene Terephthalate to Fuel Production

Cited by 4 publications

References 25 publications

Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data

Forecast Coral Bleaching by Machine Learnings of Remotely Sensed Geospatial Data

Development<strong> </strong>and Testing of Modified Alumina Catalysts for Hydrotreating Petroleum Gasoline Fraction

Catalytic Pyrolysis of Polyethylene Terephthalate and Its Copyrolysis with Polyethylene

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