Reliable estimation of optimal sulfinol concentration in gas treatment unit via novel stabilized MLP and regularization network

Asil, A. Garmroodi; Shahsavand, Akbar

doi:10.1016/j.jngse.2014.09.033

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…To test our model, within the 200 testing molecules, candidates with poor prediction accuracy were further assessed. Sulfolane, a commercial solvent for organosulfide removal from natural gases in both lab-scale and industrial processes, 43 was selected to serve as the performance reference. More specifically, considering the S cal of sulfolane is 78.48, S cal = 78.48 was set as the standard for the assessment of compounds.…”

Section: Prediction Of Mesh Solubilitymentioning

confidence: 99%

Intelligent Molecular Identification Approach to High-Efficiency Solvents for Organosulfide Capture Using the Active Machine Learning Framework

Chen

Chuanlei

et al. 2023

Energy Fuels

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There is increasing interest in the development of intelligent strategies for rational design and/or identification of promising solvent compounds for volatile, environmentally unfriendly compound capture. However, typical computer-aided methods require huge datasets and/or suffer from accumulated prediction bias. Here, we constructed a computational framework by introducing a stepwise screen approach for molecular descriptors and molecular active selection machine learning to modify the adequate chemical space iteratively. This framework identifies the optimal solvent candidates by molecular similarity search and iterative molecular addition to the training dataset. In a virtual screening of 126,068 compounds, 2443 solvent candidates were successfully identified for the capture of methyl mercaptan (MeSH), one of the major organosulfides in fossil gases.

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Section: Prediction Of Mesh Solubilitymentioning

confidence: 99%

Intelligent Molecular Identification Approach to High-Efficiency Solvents for Organosulfide Capture Using the Active Machine Learning Framework

Chen

Chuanlei

et al. 2023

Energy Fuels

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“…Valuable LPG is a natural gas processing by-product in south Pars gas complex refineries and stored as a liquid in atmospheric pressure tank. LPG delivered to customers as single-phase pressurized liquid products and should meet some specifications for sales as it is shown in Table 1 (Moaseri et al, 2013;Asil and Shahsavand, 2014). LPG is treated to reduce total sulfur content to meet sweetness specifications.…”

Section: Introductionmentioning

confidence: 99%

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Adib

Kazerooni

Falsafi

et al. 2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The acidic compounds such as Mercaptans, H2S and COS are commonly present in the liquid LPG streams in the south Pars gas processing plant. Sulfur contaminants not only lead to odor problems but can form objectionable oxides on combustion and cause environmental pollution. In present study, Support Vector Machine (SVM) is employed to develop an intelligent model to predict the sulfur content of propane and butane products of Liquefied Petroleum Gas (LPG) treatment unit of south Pars gas processing plant of Assaluyeh/Iran. A set of seven input/output plant data each consisting of 365 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 70%, 15%, and 15% of available data respectively. Test results from the SVM developed model showed good compliance with operating plant data. Squared correlation coefficients for developed models are 0.97 and 0.99 for propane and butane sulfur content, respectively. According to the results of the present case study, SVM could be regarded as a reliable accurate approach for modeling the sulfur content of LPG treatment unit of a natural gas processing plant.

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Reliable prediction of heat transfer coefficient in three-phase bubble column reactor via adaptive neuro-fuzzy inference system and regularization network

2018

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Reliable estimation of optimal sulfinol concentration in gas treatment unit via novel stabilized MLP and regularization network

Cited by 12 publications

References 28 publications

Intelligent Molecular Identification Approach to High-Efficiency Solvents for Organosulfide Capture Using the Active Machine Learning Framework

Intelligent Molecular Identification Approach to High-Efficiency Solvents for Organosulfide Capture Using the Active Machine Learning Framework

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Reliable prediction of heat transfer coefficient in three-phase bubble column reactor via adaptive neuro-fuzzy inference system and regularization network

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