Auto-characterization of naturally fractured reservoirs drilled by horizontal well using multi-output least squares support vector regression

Moosavi, Seyedeh Raha; Vaferi, Behzad; Wood, David A.

doi:10.1007/s12517-021-06559-9

Cited by 39 publications

(12 citation statements)

References 66 publications

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“…William's plot was utilized for determining the outliers of the model [45,46]. Figure 4 BioMed Research International limited boundaries: leverage limit, upper limit, and down suspected limit [47]. Outliers are data with higher standardized residual values > 3 or <−3, and the data with hat > hat * (referred to as the warning leverage value) are beyond the applicability domain of the study model [48].…”

Section: Resultsmentioning

confidence: 99%

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Elveny

Akhmadeev

Dinari

et al. 2021

BioMed Research International

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In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.

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Section: Resultsmentioning

confidence: 99%

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Elveny

Akhmadeev

Dinari

et al. 2021

BioMed Research International

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“…Least-squares support vector regression is another machine learning method used in the current study [ 39 ]. This intelligent scenario uses the kernel function to transform the independent variable into a multidimensional space.…”

Section: Methodsmentioning

confidence: 99%

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

et al. 2022

Self Cite

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Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R2) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained.

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“…The considered ANN models are multilayer perceptron neural network (MLPNN) 46 , 47 , cascade feedforward neural network (CFFNN) 48 , recurrent neural network (RNN) 49 , 50 , general regression neural network (GRNN) 48 , and radial basis function neural networks (RBFNN) 51 . The efficiency of the support vector regression with the linear kernel (LSSVR-L) 52 , polynomial kernel (LSSVR-P) 52 , and Gaussian kernel (LSSVR-G) 53 are also evaluated over the considered purpose. The neuro-fuzzy models with the subtractive clustering membership function trained by the hybrid (ANFIS2-H) and backpropagation (ANFIS2-BP) algorithms have also been applied in the current study 54 .…”

Section: Methodsmentioning

confidence: 99%

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Rezaei

Nazarpour

Shahini

et al. 2022

Sci Rep

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Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10–9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.

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Auto-characterization of naturally fractured reservoirs drilled by horizontal well using multi-output least squares support vector regression

Cited by 39 publications

References 66 publications

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

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