Multi-variate hybrid modeling for pacific ocean acidification: predicting future pH trends and analyzing key biogeochemical drivers DOI Creative Commons

K. Vasanth,

R. Kishore,

Vijayan Sugumaran

et al.

CSI Transactions on ICT, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

Abstract Ocean acidification, driven by rising atmospheric carbon dioxide levels, poses a significant threat to the health of marine ecosystems, particularly in Pacific Ocean. This study employs multi-variate hybrid machine learning approach predict future pH trends within and analyze influence key biogeochemical drivers on these trends. Hybrid models, strategically combining strengths individual algorithms, were developed for predicting several ocean acidification parameters. A performance analysis demonstrated superior accuracy models compared their counterparts. The predicted reveal concerning shift towards increased acidity Ocean, highlighting urgency understanding mitigating its impacts. In-depth was conducted identify relative factors changing dynamics. research aims provide crucial insights developing targeted mitigation strategies protecting vulnerable ecosystems from escalating consequences acidification.

Language: Английский

Investigation of Wettability and IFT Alteration during Hydrogen Storage Using Machine Learning DOI Creative Commons

Mehdi Maleki,

Mohammad Rasool Dehghani,

Ali Akbari

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38679 - e38679

Published: Sept. 30, 2024

Language: Английский

Citations

10
Estimation of hydrogen solubility in aqueous solutions using machine learning techniques for hydrogen storage in deep saline aquifers DOI Creative Commons

Mohammad Rasool Dehghani,

Hamed Nikravesh,

Maryam Aghel

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 29, 2024

The porous underground structures have recently attracted researchers' attention for hydrogen gas storage due to their high capacity. One of the challenges in storing aqueous solutions is estimating its solubility water. In this study, after collecting experimental data from previous research and eliminating four outliers, nine machine learning methods were developed estimate To optimize parameters used model construction, a Bayesian optimization algorithm was employed. By examining error functions plots, LSBoost method with R² = 0.9997 RMSE 4.18E-03 identified as most accurate method. Additionally, artificial neural network, CatBoost, Extra trees, Gaussian process regression, bagged regression support vector machines, linear had values 0.9925, 0.9907, 0.9906, 0.9867, 0.9866, 0.9808, 0.9464, 0.7682 2.13E-02, 2.43E-02, 2.44E-02, 2.83E-02, 2.85E-02, 3.40E-02, 5.68E-02, 1.18E-01, respectively. Subsequently, residual plots generated, indicating performance across all ranges. maximum - 0.0252, only 4 points estimated an greater than ± 0.01. A kernel density estimation (KDE) plot errors showed no specific bias models except model. investigate impact temperature, pressure, salinity on outputs, Pearson correlation coefficients calculated, showing that 0.8188, 0.1008, 0.5506, respectively, pressure strongest direct relationship, while inverse relationship solubility. Considering results research, method, alongside approaches like state equations, can be applied real-world scenarios storage. findings study help better understanding solutions, aiding systems.

Language: Английский

Citations

4
Estimation the pH of CO2-saturated NaCl solutions using gene expression programming: Implications for CO2 sequestration DOI

Mohammad Rasool Dehghani,

Parmida Seraj Ebrahimi,

Moein Kafi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104047 - 104047

Published: Jan. 25, 2025

Language: Английский

Citations

0
Current Status and Reflections on Ocean CO2 Sequestration: A Review DOI Creative Commons
Shanling Zhang, Sheng Jiang, Hongda Li

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 942 - 942

Published: Feb. 16, 2025

Climate change has become one of the most pressing global challenges, with greenhouse gas emissions, particularly carbon dioxide (CO2), being primary drivers warming. To effectively address climate change, reducing emissions an urgent task for countries worldwide. Carbon capture, utilization, and storage (CCUS) technologies are regarded as crucial measures to combat among which ocean CO2 sequestration emerged a promising approach. Recent reports from International Energy Agency (IEA) indicate that by 2060, CCUS could contribute up 14% cumulative reductions, highlighting their significant potential in mitigating change. This review discusses main technological pathways sequestration, including oceanic water column oil gas/coal seam geological saline aquifer seabed methane hydrate sequestration. The current research status challenges these reviewed, particular focus on offers density approximately 0.5 1.0 Gt per cubic kilometer hydrate. article delves into formation mechanisms, stability conditions, advantages hydrates. via hydrates not only high but also ensures long-term low-temperature, high-pressure conditions seabed, minimizing leakage risks. makes it technologies. paper analyzes difficulties faced technologies, such kinetic limitations monitoring during process. Finally, this looks ahead future development providing theoretical support practical guidance optimizing application promoting low-carbon economy.

Language: Английский

Citations

0
A Comparative Study of Ensemble Learning Techniques and Mathematical Models for Rigorous Modeling of Solution Gas/Oil Ratio DOI
Hossein Yavari, Jafar Qajar

SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 26

Published: Feb. 1, 2025

Summary The solution gas/oil ratio (Rs) represents the quantity of gas dissolved in oil under reservoir conditions. It is a vital parameter petroleum engineering, defining content available during production. While many experimental techniques exist for measuring this ratio, they often require considerable time and resources. Thus, mathematical intelligent models are essential accurate determination. A total 720 data points from diverse geographical regions were collected published studies research, using gas-specific gravity, temperature, bubblepoint pressure, API gravity as inputs, with output. Statistical physical analyses assessed impact parameters on revealing that temperature does not always decrease gas. Beyond specific point, known inversion higher temperatures enhance solubility. set was split, 80% allocated training 20% testing. accuracy Al-Marhoun model, originally established 160 sets Middle East, evaluated test data, which produced root mean square error (RMSE) 468.79 scf/STB. recalibration coefficients 576 differential evolution (DE) algorithm led to formulation New Model 1. By incorporating effect 2 developed. Testing results showed 1 achieved an RMSE 100.97 scf/STB, while reached 105.1 both showing better compared previous models, including model. Subsequently, machine learning applied, multilayer group method handling (GMDH), voting regressor (VR), extra trees (ET), histogram-based gradient boosting regression (HGBR), extreme (XGBoost), categorical features support (CatBoost) modeling process. Notably, such ET, HGBR, XGBoost, CatBoost effectively captured data. performance statistical visual analyses. HGBR model outperformed all others, achieving 0.0044 scf/STB value 73.03 demonstrating its clear superiority among considered models.

Language: Английский

Citations

0
Numerical simulation of CO 2 injection, and dissolved gas injection for enhanced oil recovery in complex reservoirs with transmissible and non-transmissible faults DOI

O. Courtney Leonard,

Azubuike Hope Amadi, Pwafureino Reuel Moses

et al.

Geosystem Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: March 3, 2025

Language: Английский

Citations

0
Multi-variate hybrid modeling for pacific ocean acidification: predicting future pH trends and analyzing key biogeochemical drivers DOI Creative Commons

K. Vasanth,

R. Kishore,

Vijayan Sugumaran

et al.

CSI Transactions on ICT, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

Abstract Ocean acidification, driven by rising atmospheric carbon dioxide levels, poses a significant threat to the health of marine ecosystems, particularly in Pacific Ocean. This study employs multi-variate hybrid machine learning approach predict future pH trends within and analyze influence key biogeochemical drivers on these trends. Hybrid models, strategically combining strengths individual algorithms, were developed for predicting several ocean acidification parameters. A performance analysis demonstrated superior accuracy models compared their counterparts. The predicted reveal concerning shift towards increased acidity Ocean, highlighting urgency understanding mitigating its impacts. In-depth was conducted identify relative factors changing dynamics. research aims provide crucial insights developing targeted mitigation strategies protecting vulnerable ecosystems from escalating consequences acidification.

Language: Английский

Citations

0