Performance of Data Segmentation ANN Model for Elemental Sulfur Solubility Prediction in Natural Gas Transportation Pipeline
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85159597097
Journal Title
2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023
Start Page
287
End Page
291
Rights Holder(s)
SCOPUS
Bibliographic Citation
2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023 (2023) , 287-291
Suggested Citation
Piemjaiswang R., Khaisri S., Sema T., Chalermsinsuwan B., Nimmanterdwong P. Performance of Data Segmentation ANN Model for Elemental Sulfur Solubility Prediction in Natural Gas Transportation Pipeline. 2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023 (2023) , 287-291. 291. doi:10.1109/ICCAE56788.2023.10111337 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82867
Title
Performance of Data Segmentation ANN Model for Elemental Sulfur Solubility Prediction in Natural Gas Transportation Pipeline
Author's Affiliation
Other Contributor(s)
Abstract
During the gas transportation, the pressure differential along the pipeline leads to the formation of unwanted impurities including elemental sulfur. The elemental sulfur formation is interestingly limited by the solubility of sulfur in the vapor phase. Our work applied the LM-ANN method (Levenberg-Marquardt Artificial Neural Network) to provide accurate and fast prediction of sulfur solubility in natural gas pipeline. Various sulfur solubility models were developed using the proposed method in order to predict sulfur solubility under two operating pressure ranges including; models for data with pressure lower than 25 MPa (LP model) and pressure higher than 25 MPa (HP model). The proposed models could capture the variation of gas composition and conditions (temperature and pressure) while the empirical model was required to be adjusted as the changing values of input variables. The proposed models provided a promising performance with R2 and RMSE increased from the base model up to 0.9841 and 0.036 for LP model and 0.9913 and 0.0093 for HP model, respectively. The proposed models have advantages over the empirical models as they could cover the variation of gas composition and conditions while the traditional model require to be adjusted as the values of input variables change.