Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

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Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Processes ◽

10.3390/pr8010092 ◽

2020 ◽

Vol 8 (1) ◽

pp. 92 ◽

Cited By ~ 5

Author(s):

Narjes Nabipour ◽

Amir Mosavi ◽

Alireza Baghban ◽

Shahaboddin Shamshirband ◽

Imre Felde

Keyword(s):

Extreme Learning Machine ◽

Chemical Engineering ◽

Electrolyte Solutions ◽

Key Factors ◽

Hydrocarbon Gases ◽

Natural Gases ◽

Solubility Model ◽

Input Variables ◽

Learning Machine ◽

Solubility Estimation

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

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Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

Materials ◽

10.3390/ma13051023 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1023

Author(s):

Abobakr Khalil Al-Shamiri ◽

Tian-Feng Yuan ◽

Joong Hoon Kim

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Prediction Model ◽

Extreme Learning Machine ◽

High Performance ◽

High Performance Concrete ◽

Fine Aggregate ◽

Input Variables ◽

Prediction Reliability ◽

Learning Machine

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

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Classification of Sugar Beets Based on Hyperspectral and Extreme Learning Machine Methods

Applied Engineering in Agriculture ◽

10.13031/aea.12903 ◽

2018 ◽

Vol 34 (6) ◽

pp. 891-897 ◽

Cited By ~ 3

Author(s):

Rongchao Yang ◽

Haiqing Tian ◽

Jiangming Kan

Keyword(s):

Standard Deviation ◽

Sugar Beet ◽

Extreme Learning Machine ◽

Signal To Noise Ratio ◽

Recognition Performance ◽

Pretreatment Method ◽

Standard Normal Variate ◽

Spectral Models ◽

Input Variables ◽

Learning Machine

Abstract. Sugar beet varieties were classified based on hyperspectral technology and the Extreme Learning Machine (ELM) algorithm. The influences of seven pretreatment methods, namely, Savitzky-Golay smoothing (SG), the first derivative (FD) method, SG smoothing combined with the FD method (SG-FD), logarithmic transformation (LT), LT combined with the FD method (LT-FD), the standard normal variate (SNV) method, and SNV combined with the FD method (SNV-FD), on the recognition performance of the ELM model were analyzed to select the best pretreatment method. To simplify the input variables, the standard deviation peak method was used to extract the feature bands for different preprocessed spectral data. The experimental results showed that for different pretreatment methods, the recognition rates of sugar beet varieties by ELM models were all over 80%. Additionally, the combination of different pretreatment methods and FD effectively improved the signal-to-noise ratio and enhanced the accuracy and stability of spectral models. Overall, the recognition accuracy of the ELM models established based on the feature bands was better than that established based on all bands, which suggests that the feature bands extracted by the standard deviation peak method are effective. Based on the SG-FD pretreatment method, the ELM models established using all bands and feature bands both achieved the highest recognition effect. Specifically, the recognition rates of the prediction sets were 93.94% and 95.45%, respectively. Keywords: Hyperspectral, Sugar beet variety, ELM, Different pretreatment methods, Standard deviation peak method.

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