A novel study of the gas lift process using an integrated production/injection system using artificial neural network approach

Abdollahi, Farshad; Hosseini, Seyednooroldin; Sabet, Maziar; Esmaeili-Faraj, Seyyed Hamid; Amiri, Fatemeh

doi:10.1007/s40808-020-00958-9

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…A reliable databank should be available to construct and validate empirical 39 and semi-empirical 40 correlations and machine learning methods 41 . Hence, this study has collected 119 datasets about the acetone-detecting ability of the In 2 O 3 /Fe 2 O 3 nanocomposite sensors from valid references 4,8,9,21 .…”

Section: Experimental Data For the Acetone Sensing By In 2 O 3 /Fe 2 ...mentioning

confidence: 99%

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

Iranmanesh

Pourahmad

Shabestani

et al. 2023

Sci Rep

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This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors.

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Section: Experimental Data For the Acetone Sensing By In 2 O 3 /Fe 2 ...mentioning

confidence: 99%

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

Iranmanesh

Pourahmad

Shabestani

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…ANN is a widely employed machine learning method inspired by biological neural networks. is method simulates the knowledge acquisition and reasoning processes occurring the human brain [37][38][39][40][41]. Given the learning task is to train a function f: X ∈ R D ⟶ Y ∈ R 1 , where D denotes the number of input attributes, an ANN model employed to learn the function f typically includes the input, hidden, and output layers.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Dinh

Hoang

Tran

2021

Applied and Environmental Soil Science

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Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.

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Hybrid approach for gas viscosity in Yemeni oil fields

Al-Gathe,

Baarimah,

Al-Khudafi

et al. 2023

Earth Sci Inform

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The estimation of a gas viscosity experimentally is often difficult. So, accurate determination of gas viscosity has been the main challenge in a gas reservoir development. There are many correlations to estimate this property. Often time, the results of these correlations do not agree with experimental data, thereby causing a considerable amount of error. The difficulty of these correlations can be propagated simply by tuning against some experimental data using artificial intelligent model. Currently, the achievements of artificial neural networks (ANN) techniques alone to predict gas viscosity open the door to use the hybrid system. In this model, the Particle Swarm Optimization (PSO) algorithm is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 868 data points obtained from the laboratory measurements of gas viscosity were used. The data include measured gas viscosity, specific gas gravity, temperature, pressure, molecular weight, pseudo-critical temperature and pressure and non-hydrocarbon components (H2S, CO2, and N2). The performance of the PSONN model is compared with performance of ANN and other empirical model to show the most general and accurate model for predicting gas viscosity. From the results of this study, we found that the PSONN model is more reliable and accurate with the absolute present relative (APRE) error and mean square error (RMS) of 2.76 and 5.49 respectively.

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A novel study of the gas lift process using an integrated production/injection system using artificial neural network approach

Cited by 11 publications

References 14 publications

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Hybrid approach for gas viscosity in Yemeni oil fields

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