Prediction of octane numbers for commercial gasoline using distillation curves: a comparative regression analysis between principal component and partial least squares methods

Issa, Hayder Mohammed

doi:10.1080/10916466.2022.2143814

Cited by 5 publications

(9 citation statements)

References 32 publications

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“…Separate matrices X of computed physical properties and a vector y of measured RVP were used for multiple linear regression in PLSR model development. According to Equations (3) and ( 4), the variables X and y are transformed into "loading" matrices consisting of scores, crucial data about gasoline samples, and the original variables as stated by Geladi and Kowalski [30] and Issa [31].…”

Section: Regression Analysis and Model Developmentmentioning

confidence: 99%

“…Then, RVP for new samples (y pr ) can be predicted with a regression coefficient of b PLS by using PLS regression to construct a linear relation model between X and y. The relative loading weight of these variables can be measured using Equation (5) [31,32] y pr = b PLS X 5…”

Section: Regression Analysis and Model Developmentmentioning

confidence: 99%

“…Root mean square error of calibration (RMSEC) and prediction (RMSEP) [31,45] were used to assess the precision of 9). The root mean square error of crossvalidation (RMSECV), as reported by Kehimkar et al [46] and Issa [31] as shown in Equation ( 10), was employed using the leave-one-out (LOO) and k-fold cross-validation approaches to compare the performance of PLSR, NLR, and NPR approaches, with and without outliers.…”

Section: Regression Analysis and Model Developmentmentioning

confidence: 99%

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Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

Issa

2024

Modelling and Simulation in Engineering

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This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.

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Section: Regression Analysis and Model Developmentmentioning

confidence: 99%

Section: Regression Analysis and Model Developmentmentioning

confidence: 99%

Section: Regression Analysis and Model Developmentmentioning

confidence: 99%

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Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

Issa

2024

Modelling and Simulation in Engineering

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“…Quality detection is crucial in the petroleum sector since it directly affects product integrity, safety, and market competitiveness [1,2]. Precise evaluation of petroleum products guarantees adherence to strict industry standards, regulatory mandates, and client specifications [3,4]. Quality detection processes involve techniques including chromatography, spectroscopy, and viscosity measurements to find impurities, contaminants, and deviations from desired chemical compositions [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Precise evaluation of petroleum products guarantees adherence to strict industry standards, regulatory mandates, and client specifications [3,4]. Quality detection processes involve techniques including chromatography, spectroscopy, and viscosity measurements to find impurities, contaminants, and deviations from desired chemical compositions [5][6][7]. The petroleum business can improve operating efficiency, reduce hazards related to product inconsistency, and protect public health and environmental well-being by upholding high-quality standards [8,9].…”

Section: Introductionmentioning

confidence: 99%

An attempt to promote intelligent petroleum processing: a look at how different programming languages works with deep learning models for gasoline quality detection

Alzahawi,

Issa

2024

Preprint

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The current study explores how several programming languages affect deep learning models for gasoline quality detection, highlighting the significance of choosing the best appropriate language according to project needs. Featuring several scientific computations using an integrated development environment of the Visual Studio Code (VS Code) tool. This work has reached multiple outcomes: Python is known for its rapid development and adaptability, which are well-suited for exploring quality control techniques in petroleum products. Java is recognized for its stability and flexibility to work across many platforms, making it ideal for large-scale deployments that require dependable performance in refinery equipment. C/C++ is lauded for its speed and ability to manage hardware resources effectively, which is essential in real-time production settings, allowing for quick analysis and little delay in quality control processes. The study utilizes a deep learning architecture in Java, C, and Python, with frameworks such as Visual Studio Code to aid in model construction, with the goal of creating flexible tools for monitoring the dynamic quality of petroleum products.

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Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Issa,

Hama Salih

2024

ARO

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Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq; therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

show abstract

Prediction of octane numbers for commercial gasoline using distillation curves: a comparative regression analysis between principal component and partial least squares methods

Cited by 5 publications

References 32 publications

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

An attempt to promote intelligent petroleum processing: a look at how different programming languages works with deep learning models for gasoline quality detection

Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

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