Application of ANN to the water-lubricated flow of non-conventional crude

Dubdub, Ibrahim; Rushd, Sayeed; Al-Yaari, Mohammed; Ahmed, Ezz

doi:10.1080/00986445.2020.1823842

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…After finalizing the ANN model, it is important to establish a link or relationship between the input and the output. In general, a sensitivity analysis is used to check the effect of a small change in an input parameter on the output variable [ 27 , 28 , 29 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Dubdub

2023

Polymers

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Among machine learning (ML) studies, artificial neural network (ANN) analysis is the most widely used technique in pyrolysis research. In this work, the pyrolysis of polypropylene (PP) polymers was established using a thermogravimetric analyzer (TGA) with five sets of heating rates (5–40 K min−1). TGA data was used to exploit an ANN network by achieving a feed-forward backpropagation optimization technique in order to predict the weight-left percentage. Two important ANN model input variables were identified as the heating rate (K min−1) and temperature (K). For the range of TGA values, a 2-10-10-1 network with two hidden layers (Logsig-Tansig) was concluded to be the best structure for predicting the weight-left percentage. The ANN demonstrated a good agreement between the experimental and calculated values, with a high correlation coefficient (R) of greater than 0.9999. The final network was then simulated with the new input data set for effective performance. In addition, a sensitivity analysis was performed to identify the uncertainties associated with the relationship between the output and input parameters. Temperature was found to be a more sensitive input parameter than the heating rate on the weight-left percentage calculation.

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Section: Resultsmentioning

confidence: 99%

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Dubdub

2023

Polymers

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“…The following section includes a widespread review in the field of AI and ML to solve the relevant issues. It also unfolds several types of ML and AI techniques which are used for data cleaning, management [64], interpretation [65], parameter prediction [66][67][68], estimation [69], controlling [70], and decision making [71,72]. The achievements and developments have proved the advantages of ML and AI for large data storage capabilities and highly efficient estimations.…”

Section: Application Of Artificial Intelligence In Oil and Gas Indust...mentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

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The field of machine learning has proven to be a powerful approach in smart manufacturing and processing in the chemical and process industries. This review provides a systematic overview of current state of artificial intelligence and machine learning and their applications in textile, nuclear power plant, fertilizer, water treatment, and oil and gas industries. Moreover, this study reveals the current dominant machine learning methods, pre and post processing of models, increased utilization of machine learning in terms of fault detection, prediction, optimization, quality control, and maintenance in these sectors. In addition, this review gives the insight into the actual benefits and impact of each method, and complications in their extensive deployment. Finally in the current impressive state, challenges, future development in terms of algorithm and infrastructure aspects are highlighted.

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“…Model performance could be evaluated using the percentage of average relative deviation (ARD%), mean squared error (MSE), coefficient of determination (R 2 ), root mean square error (RMSE), and standard deviation (STD) [70][71][72][73]. ese evaluation indices are written as…”

Section: Models' Evaluationmentioning

confidence: 99%

Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach

Chen

Hammid²,

Akbarov

et al. 2022

International Journal of Chemical Engineering

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This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. In order to develop a comprehensive artificial intelligence model in this study, sixty-nine data samples were collected. To this end, the Gaussian process regression approach with four basic function kernels (Matern, squared exponential, exponential, and rational quadratic) was exploited. It was found that Matern outperformed other models with R2 = 0.987, MARE (%) = 6.048, RMSE = 0.0577, and STD = 0.0574. This precise yet simple model can be a good alternative to the complex thermodynamic, mathematical-analytical models of the past.

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Application of ANN to the water-lubricated flow of non-conventional crude

Cited by 7 publications

References 32 publications

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach

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