Comparing the predictions by NIR spectroscopy based multivariate models for distillation fractions of crude oils by F-test

The naphtha catalytic reforming process is evaluated by designing new composite nano-catalysts. Three catalysts were prepared for this process. The first catalyst was molybdenum carbide composite with platinum over HY zeolite (Mo2C.Pt/HY zeolite), the second catalyst was molybdenum carbide composite with platinum over modified zeolite by cerium nitrate (Mo2C.Pt/CeY zeolite), and the last catalyst was bimetallic titanium and platinum with a titanium content of 1% and platinum content of 0.11% over HY zeolite (Pt.Ti/HY zeolite). All catalysts were tested with several tests, mainly X-Ray Diffraction (XRD), BET surface area, and pore volume. All these substances were applied as catalysts for the reforming process of Iraqi heavy naphtha at the following operating conditions: reaction temperature (480, 500, and 520 ), reaction pressure (10, 12.5, and 15 bar), liquid hourly space velocity (LHSV) at 2 hr-1, and constant hydrogen to hydrocarbon ratio (H2/ HC) of 4. All the reforming reactions occurred in a packed bed pilot plant reactor to investigate its stability and activity during the reforming process. All the developed catalyst samples showed sensational stability even at operating under difficult circumstances. The best catalyst was Pt.Ti/HY zeolite based on the results obtained with respect to the octane number (86.2) at 520 and 15 bar. Also, a mathematical model to describe the reforming process with high accuracy was built and simulated using gPROMS software. The results were very satisfying since the most significant error with the wt% of reformate was 4.9% (the experimental aromatics content was 23.94 wt.%, while the predicted result was 21.67 wt.%), while Research Octane Number (RON) error was 4.7% (the experimental RON was 81, whereas the predicted value of RON was 85) among all the results meaning that the simulating was valid to describe the process.

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“…A Riazi-Daubert correlation is used to determine the oil feedstock molar volume (vi) and critical specific volume (𝑣𝑣 𝑐𝑐𝐿𝐿 ) [21,22].…”

Section: Models Based On the Kineticsmentioning

confidence: 99%

Design of New Composites Nano-Catalysts for Naphtha Reforming Process: Experiments and Process Modeling

Jarullah

Ahmed

Ahmed³

et al. 2023

Tikrit j. eng. sci.

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An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data

Arianti

Saputra

Sitorus

2023

Journal of Agriculture and Food Research

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An assessment of multivariate predictions of various properties of crude oil on two ATR/IR spectrometers

Šašić,

Nivens,

Patel

et al. 2024

Microchemical Journal

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Comparing the predictions by NIR spectroscopy based multivariate models for distillation fractions of crude oils by F-test

Cited by 5 publications

References 17 publications

Design of New Composites Nano-Catalysts for Naphtha Reforming Process: Experiments and Process Modeling

Design of New Composites Nano-Catalysts for Naphtha Reforming Process: Experiments and Process Modeling

An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data

An assessment of multivariate predictions of various properties of crude oil on two ATR/IR spectrometers

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