A model-based approach for process monitoring in oil production industry

Irisarri, Edurne; García, Marcelo V.; Pérez, Federico; Estévez, Elisabet; Marcos, Marga

doi:10.1109/etfa.2016.7733633

Cited by 15 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, even among the publications with industrial evaluation, we found contributions introducing novel metamodels (cf. [71]) or extensions of established ontologies (cf. [113]).…”

Section: Resultsmentioning

confidence: 99%

“…Out of the 408 included publications, only 23 (5.64 %) reported such applications. The industrial domains include automotive [39,68,79], avionics manufacturing [121,127], packaging [155], production of white goods [9], oil production [71,133], and production of windows and doors [7]. The next section presents our main findings along the four classification dimensions.…”

Section: Umlmentioning

confidence: 96%

See 1 more Smart Citation

Modeling languages in Industry 4.0: an extended systematic mapping study

et al. 2019

View full text Add to dashboard Cite

Industry 4.0 integrates Cyber-Physical Systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. Objective: We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Method: Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0 [159] to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Results: Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for In

show abstract

“…Nonetheless, even among the publications with industrial evaluation, we found contributions introducing novel metamodels (cf. [71]) or extensions of established ontologies (cf. [113]).…”

Section: Resultsmentioning

confidence: 99%

Section: Umlmentioning

confidence: 96%

Modeling languages in Industry 4.0: an extended systematic mapping study

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The creation of predictive models in the oil industry is of the utmost importance. For example, models have been developed for oil production (Li and Horne, 2003;Irisarri et al, 2016;Hutahaean et al, 2017) and oil transformation (Farrusseng et al, 2003;Wang et al, 2019). These models, however, have not been entirely successful (Cai et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Bokhimi

2021

Front. Chem. Eng.

View full text Add to dashboard Cite

We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

show abstract

“…Qatar, an important producer of oil and gas, is in an arid area with harsh soils and weather. Oil is discharged to the environment as waste during anthropogenic activities and through spillages, and oil in the environment is subjected to weathering processes, particularly under extreme conditions especially in the summer season (mean temperature 34.9 °C, relative humidity of 58 %, daily sunshine of 10.6 h, solar radiation of 584.7 mW h/sq.cm and wind speed of 26 km/h) [ 1 , 2 ]. In a recent study it was found that oil pollution has occurred in limited specific areas in Qatar but extensive weathering caused by the harsh weather in Qatar has led to oil in these areas being at different stages of oxidation [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Potential for native hydrocarbon-degrading bacteria to remediate highly weathered oil-polluted soils in Qatar through self-purification and bioaugmentation in biopiles

Al-Kaabi

Al-Ghouti

Jaoua

et al. 2020

Biotechnology Reports

View full text Add to dashboard Cite

Highlights Highly adapted hydrocarbon-degrading bacteria were isolated from weathered soils. High diversity of bacterial metabolism was shown although from the same soil. Biostimulation improves removal of weathered hydrocarbons by indigenous bacteria. Combination of stimulation to augmentation requires selection of indigenous strain. Without selection of strains, negative effect may be registered on bioremediation.

show abstract

A model-based approach for process monitoring in oil production industry

Cited by 15 publications

References 3 publications

Modeling languages in Industry 4.0: an extended systematic mapping study

Modeling languages in Industry 4.0: an extended systematic mapping study

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Potential for native hydrocarbon-degrading bacteria to remediate highly weathered oil-polluted soils in Qatar through self-purification and bioaugmentation in biopiles

Contact Info

Product

Resources

About