Application of Adaptive Neuro-Fuzzy Inference System and Optimization Algorithms for Predicting Methane Gas Viscosity at High Pressures and High temperatures Conditions

Sambo, Chico; Yin, Yap; Djuraev, Ulugbek; Ghosh, Deva Prasad

doi:10.1007/s13369-018-3423-8

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…Also, DNN models can determine flaws in data training and accurately correct them. The feed forward neural network is mostly adopted for prediction. , …”

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

confidence: 99%

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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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“…Also, DNN models can determine flaws in data training and accurately correct them. The feed forward neural network is mostly adopted for prediction. , …”

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

confidence: 99%

“…The feed forward neural network is mostly adopted for prediction. 44,45 Suppose N x ( ): and b l N l , respectively. Given a nonlinear activation function σ, which is applied element-wise, the FNN is recursively defined as follows; Input layer:…”

Section: Deep Neural Networkmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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“…In addition, seven different machine-learning techniques were utilized for the prediction of gas viscosity. The previous research was mostly directed only at artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) 1 , 2 , 39 , 40 . To the best of the author’s knowledge limited studies have applied ML techniques for Methane and Nitrogen viscosity prediction.…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

Gomaa,

Abdalla,

Salem

et al. 2024

Sci Rep

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The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

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“…Because of its learning ability and versatile mapping skills without requiring considerable physical based knowledge, [ 11 ] machine learning has attracted increased interest in the system modelling and simulation industry [ 12 , 13 ]. Machine learning (ML) have been used to mimic a variety of engineering applications (such as building energy systems, including TES [ 14 , 15 ], thermal performance in battery systems [ 16 ], human thermal comfort in passenger vehicles using an organic phase change material [ 17 ], agriculture [ 18 ], maintenance of gas pipelines subjected to corrosion [ 19 ], subsea pipelines annotation [ 20 ], predicting water saturation [ 21 ], viscosity of methane gas at high temperature conditions [ 22 ], integrity of corroded oil and gas pipelines [ 23 ]) other than machine learning-based indoor occupancy predictions on PCMs integrated building energy systems [ 24 , 25 ]. A state-of-the art review on the applications of the phase change materials on storage systems can be found in [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Soomro

Mokhtar

Salilew

et al. 2022

Sensors

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In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.

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Application of Adaptive Neuro-Fuzzy Inference System and Optimization Algorithms for Predicting Methane Gas Viscosity at High Pressures and High temperatures Conditions

Cited by 15 publications

References 38 publications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

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