Accurate prediction of liquid phase equilibrium adsorption of sulfur compound

Autor:	Maryam Ahmadi-Pour, Armin Mohebbi, Milad Mohebbi
Rok vydání:	2017
Předmět:	chemistry.chemical_classification Work (thermodynamics) Chromatography Chemistry General Chemical Engineering Liquid phase chemistry.chemical_element Thermodynamics 02 engineering and technology General Chemistry 021001 nanoscience & nanotechnology Sulfur Solvent Adsorption Hydrocarbon 020401 chemical engineering Robustness (computer science) Particle 0204 chemical engineering 0210 nano-technology
Zdroj:	Chemical Engineering Research and Design. 126:199-208
ISSN:	0263-8762
DOI:	10.1016/j.cherd.2017.08.024
Popis:	This work highlights the application of two types of artificial neural networks namely PSO-RBF and CSA-LSSVM for prediction of equilibrium adsorption of sulfur compound in liquid phase of hydrocarbon solution of isotherm batch systems. The precision and reliability of developed models were investigated by various graphical and statistical approaches. Initial sulfur concentration, utilized adsorbent weights, molecular weights and densities of solvent and solute, average size of adsorbent particle, Si/Al ratio of adsorbent and temperature were used as input parameters of models and the amount of adsorption was considered as the output. Results show that the predictions of CSA-LSSVM model are a little more precise and reliable compared to the outcomes of PSO-RBF model. The overall values of R 2 and AARD% for CSA-LSSVM model were 0.9920 and 0.55 that show precision and robustness of the applied model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a5bfc8a9bbefad40255c3a3b3d546671 https://doi.org/10.1016/j.cherd.2017.08.024 Zobrazit plný text záznamu Full Text from ScienceDirect Plný text ve formátu HTML
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