Distilling small volumes of crude oil

Autor(es)

Giordano, Gabriela F. ^{[1, 2]} ; Vieira, Luis C. S. ^[2] ; Gomes, Alexandre O. ^[3] ; de Carvalho, Rogerio M. ^[3] ; Kubota, Lauro T. ^[1] ; Fazzio, Adalberto ^{[4, 2]} ; Schleder, Gabriel R. ^{[4, 2]} ; Gobbi, Angelo L. ^[2] ; Lima, Renato S. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Chem, BR-13083970 Campinas, SP - Brazil

^[2] Brazilian Ctr Res Energy & Mat, Brazilian Nanotechnol Natl Lab, BR-13083970 Campinas, SP - Brazil

^[3] Leopoldo Amer Miguel Mello Res & Dev Ctr, BR-21941598 Rio De Janeiro, RJ - Brazil

^[4] Fed Univ ABC, BR-09210580 Santo Andre, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: FUEL; v. 285, FEB 1 2021.
• Citações Web of Science: 0

Resumo

We address for the first time a platform able to distil small volume of crude oil, providing the generation of oil fractions for succeeding composition analysis and accurate quantification of significant derivatives, i.e., naphtha, kerosene, and diesel, through true boiling point (TBP) curves and machine learning. While conventional systems are slow (2 to 3 days), sample-consuming (1 to 30 L), and require expensive equipment, simple and low-cost components such as thermocouples, fractionation column, external resistance on column region, and condenser were herein integrated into a glass piece to distillate 2 mL of oil in 6.7 h. In addition to assuring fractional distillation, a wire rope-packed column allowed the addition of samples without contaminating the inner glass walls. Systematic temperature programs were applied to oil and column, whereas the temperatures on the top of column were monitored to obtain TBP curves. The accuracy associated with the determination of oil derivatives was remarkably improved with the aid of a simple machine learning-modeled equation. By enabling diverse tasks such as definition of the type of petroleum, its market value, royalties, well throughput, and logistics for fuel transport, storage, and distribution, our distiller holds great potential for the petrochemical industry, in special during the drilling and prospecting of new exploratory wells when only small volumes of crude oil are commonly available. This platform also provides faster and safer analyses bearing lower energy demand and waste generation. (AU)

• Processo FAPESP: 17/02317-2 - Interfaces em materiais: propriedades eletrônicas, magnéticas, estruturais e de transporte
• Beneficiário: Adalberto Fazzio
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/18139-6 - Machine learning e Ciência de Materiais: descoberta e design de materiais 2D
• Beneficiário: Gabriel Ravanhani Schleder
• Modalidade de apoio: Bolsas no Brasil - Doutorado