Multivariate data analysis applied in the evaluation of crude oil blends

Sad, Cristina M. S.; Silva, Mayara da; Santos, Francine D.; Pereira, Laíne B.; Corona, Rayane R.B.; Silva, Samantha R.C.; Portela, Natália A.; Castro, Eustáquio Vinícius Ribeiro de; Filgueiras, Paulo R.; Lacerda, Valdemar

doi:10.1016/j.fuel.2018.11.045

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Each principal component (PC) contains two types of information: scores and loadings. Scores represent the projection values of the original data on each PC, while loadings represent the weight information between the original variables and each PC [36,37]. The variance contribution rate is used to measure the importance of PCs, indicating the proportion of variance accounted for by a particular PC.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Efficient Identification of Crude Oil via Combined Terahertz Time-Domain Spectroscopy and Machine Learning

Yang,

Ma,

Huang

et al. 2024

Photonics

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The quality of crude oil varies significantly according to its geographical origin. The efficient identification of the source region of crude oil is pivotal for petroleum trade and processing. However, current methods, such as mass spectrometry and fluorescence spectroscopy, suffer problems such as complex sample preparation and a long characterization time, which restrict their efficiency. In this work, by combining terahertz time-domain spectroscopy (THz-TDS) and a machine learning analysis of the spectra, an efficient workflow for the accurate and fast identification of crude oil was established. Based on THz-TDS of 83 crude oil samples obtained from six countries, a machine learning protocol involving the dimension reduction of spectra and classification was developed to identify the geological origins of crude oil, with an overall accuracy of 96.33%. This work demonstrates that THz spectra combined with a modern numerical scheme analysis can be readily employed to categorize crude oil products efficiently.

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Efficient Identification of Crude Oil via Combined Terahertz Time-Domain Spectroscopy and Machine Learning

Yang,

Ma,

Huang

et al. 2024

Photonics

View full text Add to dashboard Cite

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“…Thus, it is essential to carefully select the most prominent input, if the machine learning models expect to give an efficient and accurate prediction. There are studies that have used the Principal Component Analysis (PCA) (Ding et al, 2017;Sad et al, 2019) and the Independent Component Analysis (ICA) (Fan et al, 2016;Jianwei et al, 2017;Xian et al, 2020) as a dimension reduction method in the stock market, as well as in crude oil markets.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

A novel hybrid approach to forecast crude oil futures using intraday data

Manickavasagam

Visalakshmi

Apergis

2020

Technological Forecasting and Social Change

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Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This study presents two novel hybrid models to forecast WTI and Brent crude oil prices using combinations of machine learning and nature inspired algorithms. The first approach, MARSplines-IPSO-BPNN, Multivariate Adaptive Regression Splines (MARSPlines) find the important variables that affect crude oil prices. Then, the selected variables are fed into an Improved Particle Swarm Optimization (IPSO) method to obtain the best estimates of the parameters of the Backpropagation Neural Network (BPNN). Once these parameters are obtained, the variables are fed into the BPNN model to generate the required forecasts. The second approach, MARSplines-FPA-BPNN, generates the parameters of BPNN through the Flower Pollination Algorithm (FPA). The forecasting ability of these new models is compared to certain benchmark models. The findings document that the MARSplines-FPA-BPNN model performs better than the other competitive models.

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“…API gravity rises when temperatures rise, although specific gravity falls as temperatures rise. The standard measuring temperature for the API and specific gravity by density meter is 15 °C or 60 °F [7][8][9][10][11]. The main goal of this work is to build a semi-automated MPFA system and use it to improve the API and specific gravity of crude oil from Iraq, which is a major economic resource.…”

Section: Introductionmentioning

confidence: 99%

Upgrading API and Specific Gravity Specifications of Iraqi Crude Oil Using a Laboratory-Built Multipumping Fuel Analysis (MPFA) System

Mohammed Turki khathi,

Ali Abdulkhabeer Ali

2023

J. K. Chem. Sci.

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Iraqi heavy crude oil (26.85 API) is processed by a laboratory-made fuel injection system and through the deasphalting oil (DAO) extraction process by different solvents and the application of different variables. The system was built with low-cost parts found in local markets. The purpose of a manufactured fuel system is to enhance the API and specific gravity (Sp.gr.) specifications of the collected samples and obtain the reduced crude oil through the DAO process. The extraction was carried out with a temperature range, a solvent flow rate, a mixing coil length, a solvent entry time, and different types of solvents. In general, results show that the API and Sp.gr of DAO increased from 26.85 and 0.8936 to 39.28 and 0.8132, respectively. Also, sulfur content decreased from 4.1967 wt.% to 2.201 wt.% at optimum conditions. The semi-automated MPFA system was successfully used to enhance crude oil specifications sensitive and accurate manner.

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Multivariate data analysis applied in the evaluation of crude oil blends

Cited by 12 publications

References 32 publications

Efficient Identification of Crude Oil via Combined Terahertz Time-Domain Spectroscopy and Machine Learning

Efficient Identification of Crude Oil via Combined Terahertz Time-Domain Spectroscopy and Machine Learning

A novel hybrid approach to forecast crude oil futures using intraday data

Upgrading API and Specific Gravity Specifications of Iraqi Crude Oil Using a Laboratory-Built Multipumping Fuel Analysis (MPFA) System

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