A Multi-layer Perceptron-based Approach for Prediction of the Crude Oil Pyrolysis Process

Rasouli, Ahmad Reza; Dabiri, Abdolreza; Nezamabadi‐pour, Hossein

doi:10.1080/15567036.2011.623291

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Past studies in this domain have also sought to model crude oil pyrolysis and predict fuel deposition. Notably, Rasouli et al 29 developed a multilayer perceptron model with a 3.5% error for the pyrolysis of 6 crudes, Norouzpour et al 30 , employed a radial basis function neural network with a 5.8% error for the pyrolysis of 6 crudes, and Mohammadi et al 32 utilized a generalized regression neural network with a 1.04% error for the pyrolysis of 11 crudes. While these models showcased respectable performance, our current study not only extends the dataset size but also harnesses a variety of machine learning techniques, enhancing accuracy and robustness in modeling.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, 2071 experimental TGA findings related to 13 distinct crude oils were gathered from the literature 12 , 13 , 24 , 29 , 30 , 33 – 35 in order to precisely represent crude oil pyrolysis, which is a crucial reaction in the ISC EOR process. The database used in this work is more comprehensive than the one used in Mohammadi et al’s study 31 (i.e.…”

Section: Data Gathering and Preparationmentioning

confidence: 99%

“…This artificial intelligence technique proves valuable in understanding the complex relationships within crude oil pyrolysis and oxidation processes, given the multitude of influencing parameters in ISC. Rasouli et al 29 investigated the pyrolysis of six crudes and represented a multilayer perceptron model for predicting the crude oil residue on the basis of TGA with a 3.5% error. Norouzpour et al 30 modeled crude oil pyrolysis employing a radial basis function neural network based on TGA of six crudes with a 5.8% error.…”

Section: Introductionmentioning

confidence: 99%

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Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

Hadavimoghaddam,

Rozhenko,

Mohammadi

et al. 2023

Sci Rep

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Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R2) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.

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Section: Resultsmentioning

confidence: 99%

Section: Data Gathering and Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

Hadavimoghaddam,

Rozhenko,

Mohammadi

et al. 2023

Sci Rep

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Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

Mohammadi

Hemmati-Sarapardeh

Schaffie

et al. 2021

Journal of Petroleum Science and Engineering

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Modeling And Enhancing Crude Oil Price Forecasting Using Enhanced Set Algebra-Based Heuristic Algorithm-Based Extreme Learning Machine

Behera,

Kumar,

Nayak

2024

Preprint

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This study has two main aspects. Firstly, we combined the Nelder-Mead Simplex Algorithm with the Set Algebra-Based Heuristic Algorithm (SAHA) in order to improve SAHA's capacity to do local searches. This integration resulted in a hybrid learning approach known as ESAHA. After that, we use the Enhanced Simulated Annealing with Hybrid Algorithm (ESAHA) to improve six benchmark functions so that we can see how well ESAHA works. Furthermore, we utilize ESAHA hybrid learning to enhance the weights and biases of an Extreme Learning Machine (ELM), resulting in the creation of a hybrid model referred to as ESAHA-ELM. We utilize the ESAHA-ELM model to predict the final price of crude oil datasets. In addition, we employ the SAHA, BMO, PSO, and GA algorithms to train the ELM and generate four alternative models for the purpose of comparison in the forecasting job. In order to examine the predictive accuracy of each model, we utilize the MAPE and MSE error metrics. Additionally, we implement the Prediction of Change in Direction (POCID) statistical test to determine if there are any significant differences between the models. The experimental investigation shows that the ESAHA-ELM model has statistical relevance in accurately capturing the inherent volatility of financial time series. In addition, it surpasses other models such as SAHA-ELM, MBO-ELM, PSO-ELM, and GA-ELM.

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A Multi-layer Perceptron-based Approach for Prediction of the Crude Oil Pyrolysis Process

Cited by 3 publications

References 12 publications

Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques

Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

Modeling And Enhancing Crude Oil Price Forecasting Using Enhanced Set Algebra-Based Heuristic Algorithm-Based Extreme Learning Machine

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