Artificial neural networks in lube oil refining

• Tipo: Artigo em Revista Científica Internacional
• Data: 2000

Autores

Fernando Gomes Martins (Autor) FEUP; J. J. C. Aparício (Autor) Outra; M. A. N. Coelho (Autor) Outra; Manuel Neto Coelho (Autor) FEUP; Carlos Costa (Autor) FEUP; M. A. S Jerónimo (Autor) Outra; C. Martins (Autor) Outra; A. S. Braga (Autor) Outra

Revista

Título: Petroleum Technology Quarterly

Vol. 5 Nº 4

Páginas: 111,113-115

ISSN: 1362-363X

Editora: Crambeth Allen Publishing

Indexação

ISI Web of Knowledge

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-008-NNF

Abstract (EN): A discussion covers how feedforward artificial neural networks (FANN) are utilized to model rotating disc contactors for deasphalting and for furfural extraction, a valuable approach in solving complex problems in the refining business; situations where the phenomenological models are practically unknown due to the process complexity and the difficulty in developing satisfactory thermodynamic models for the complex mixtures of pseudocomponents involved, i.e., propane deasphalting process and the furfural refining process; features of a FANN, e.g., interconnected non-linear processing elements called nodes; FANN created based on data corresponding to the operational conditions for three different lubricating oil cuts; important variables in the propane deasphalting process, i.e., feedstock quality, solvent, and deasphalting temperatures; and important variables for the solvent refining process, i.e., crude source, distillation cut, solvent temperature, solvent dosage, and extract recycle.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5