Mohammad Roham
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mohammad Roham has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Ebrahim Soroush
Young Researchers and Elite Club, Ahvaz Branch Islamic Azad University, Ahvaz, Iran
1 shared publication - Mohammad Mesbah
Islamic Azad University
1 shared publication Shohreh Shahsavari
Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran
1 shared publication
Latest Publications
- 3 August 2017
- Published by Taylor & Francis in Petroleum Science and Technology
- Vol. 35 (15) , 1588-1594
Phase equilibrium modeling of semi-clathrate hydrates of the CO2+H2/CH4/N2+TBAB aqueous solution system
In this study, the semi-clathrate hydrate dissociation pressure for the CO2+N2, CO2+H2, CO2+CH4, and pure CO2 systems in the presence of different concentrations of TBAB aqueous solutions is predicted using a strong machine learning technique of multi-layer perceptron neural network (MLP-NN). The developed model, with an overall correlation coefficient (R2) of 0.9961 and mean square error (MSE) of 5.96E−02, presented an excellent accuracy in prognosticating experimental data. A complete statistical evaluation performed to promise the strength and generality of the multi-layer perceptron artificial neural network (MLP-ANN). In addition, the applicability of the proposed network and quality of experimental data was assessed through the Leverage approach.
- 3 August 2017
- Published by Taylor & Francis in Petroleum Science and Technology
- Vol. 35 (15) , 1588-1594
Phase equilibrium modeling of semi-clathrate hydrates of the CO2+H2/CH4/N2+TBAB aqueous solution system
In this study, the semi-clathrate hydrate dissociation pressure for the CO2+N2, CO2+H2, CO2+CH4, and pure CO2 systems in the presence of different concentrations of TBAB aqueous solutions is predicted using a strong machine learning technique of multi-layer perceptron neural network (MLP-NN). The developed model, with an overall correlation coefficient (R2) of 0.9961 and mean square error (MSE) of 5.96E−02, presented an excellent accuracy in prognosticating experimental data. A complete statistical evaluation performed to promise the strength and generality of the multi-layer perceptron artificial neural network (MLP-ANN). In addition, the applicability of the proposed network and quality of experimental data was assessed through the Leverage approach.