Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth

Foroozesh, Jalal; Khosravani, Abbas; Mohsenzadeh, A.; Mesbahi, Ali Haghighat

doi:10.4236/ajac.2013.411073

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Based on whether discrete or continuous output features are used, supervised learning can be further divided into classification and regression activities [14]. Several existing ML models, such as Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and other existing ML models, are present in the mitigation of gas hydrate formation [16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Methods and Theorymentioning

confidence: 99%

“…Besides that, there are 136 water molecules for a unit cell of structure II and enclosed 24 cavities, including 16 smalls and 8 wide ones [14]. It is therefore important to resolve the problem of gas hydrate development by introducing appropriate preventive and predictive strategies [3,16]. There are many risk control techniques that have been used,'such as the use of thermodynamic hydrate inhibitor (THI) inhibitors and low-dose hydrate inhibitor (LDHI) inhibitors.…”

Section: Introductionmentioning

confidence: 99%

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Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Suresh

Qasim

Lal

et al. 2021

ARFMTS

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The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

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Section: Methods and Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Suresh

Qasim

Lal

et al. 2021

ARFMTS

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“…A typical neuron i could be represented by the following equations: r i = ∑ j = 1 N ( w i j x j + b i ) y i = f false( r i false) where x j and w ij denote the input signals and the neuron’s synaptic weights, respectively, and r i , b i , y i , and f are the linear combiner output, bias term, output signal of the neuron, and activation function, respectively. The authors of refs and used the back-propagation algorithm to minimize the mean squared error (MSE) of the input and output variables. Also depending on the type of model development, the RBFs might need several hidden layers and a transfer function.…”

Section: Machine Learning Models Used For Gas-hydrate-related Studiesmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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“…In [14] presented by "Jalal Foroozesh el al", an investigation was formed for the relation-ship between growth rate of methane hydrate with temperature and pressure using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results have shown that ANIFS is a more potential tool in predication relationship of kinetics of hydrate formation with temperature and pressure in comparison of ANN.…”

Section: A) Hand Calculation Methodsmentioning

confidence: 99%

Design of a Prediction System for Hydrate Formation in Gas Pipelines using Wireless Sensor Network

Moukhtar¹,

Hamdy²,

Salem³

2016

ijacsa

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Abstract-Before the evolution of the Wireless Sensor Networks (WSN) technology, many production wells in the oil and gas industry were suffering from the gas hydration formation process, as most of them are remotely located away from the host location. By taking the advantage of the WSN technology, it is possible now to monitor and predict the critical conditions at which hydration will form by using any computerized model. In fact, most of the developed models are based on two well-known hand calculation methods which are the Specific gravity and K-Factor methods. In this research, the proposed work is divided into two phases; first, the development of a three prediction models using the Neural Network algorithm (ANN) based on the specific gravity charts, the K-Factor method and the production rates of the flowing gas mixture in the process pipelines. While in the second phase, two WSN prototype models are designed and implemented using National Instruments WSN hardware devices. Power analysis is carried out on the designed prototypes and regression models are developed to give a relation between the sensing nodes (SN) consumed current, Node-to-Gateway distance and the operating link quality. The prototypes controller is interfaced with a GSM module and connected to a web server to be monitored via mobile and internet networks.

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Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth

Cited by 6 publications

References 14 publications

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Design of a Prediction System for Hydrate Formation in Gas Pipelines using Wireless Sensor Network

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