The dataset is a data relevant to chemical or process engineering. It includes data on percentage.yield, gravity, vapour.pressure, ten.percent.distillation.point, and fraction.end.point, which are likely used to analyze the efficiency and characteristics of various substances under different conditions in a distillation or similar process. This dataset can be useful for optimizing processes and studying the physical properties of materials.

Data on petroleum inputs, production, yield, and capacity. Weekly, monthly and annual data available. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The Pile -- NIHExPorter (refined by Data-Juicer)

A refined version of NIHExPorter dataset in The Pile by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 2.0G).

  Dataset Information

Number of samples: 858,492 (Keep ~91.36% from the original dataset)

  Refining… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/the-pile-nih-refined-by-data-juicer.

Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on Oil Refineries.

This dataset contains information about world's oil regional refining margins. Data from BP. Follow datasource.kapsarc.org for timely data to advance energy economics research. Note: The refining margins presented are benchmark margins for three major global refining centres. US Gulf Coast (USGC), North West Europe (NWE - Rotterdam) and Singapore

In each case they are based on a single crude oil appropriate for that region and have optimized product yields based on a generic refinery configuration (cracking, hydrocracking or coking), again appropriate for that region.

The margins are on a semi-variable basis, ie the margin after all vari

Facilities that separate and convert crude oil or other feedstock into liquid petroleum products, including upgraders and asphalt refineries. Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States. The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.

RedPajama -- ArXiv (refined by Data-Juicer)

A refined version of ArXiv dataset in RedPajama by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 85GB).

  Dataset Information

Number of samples: 1,655,259 (Keep ~95.99% from the original dataset)

  Refining Recipe… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/redpajama-arxiv-refined-by-data-juicer.

Production: Petroleum Refinery Product: Barrel: Recap data was reported at 1,421,396.423 Barrel in Jun 2019. This records a decrease from the previous number of 1,499,603.921 Barrel for May 2019. Production: Petroleum Refinery Product: Barrel: Recap data is updated monthly, averaging 1,416,068.954 Barrel from Jan 2000 (Median) to Jun 2019, with 234 observations. The data reached an all-time high of 1,889,326.838 Barrel in Jul 2014 and a record low of 0.000 Barrel in Aug 2015. Production: Petroleum Refinery Product: Barrel: Recap data remains active status in CEIC and is reported by National Petroleum Agency. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBD011: Production: Petroleum Refinery Product: Barrel: by Source: by Plant. Petroleum Refinery Product refers to volume of petroleum processed in national refineries.

This worksheet displays the results of mineral abundance estimates based on Rietveld refinement of X-ray diffraction (XRD) analyses of mill tailings and other ore processing materials from worldwide localities. Data are also provided to show variation in mineral abundance estimates for subsplits in individual samples. Samples were analyzed using a PANalytical X'Pert Pro diffractometer using Cu radiation and the results interpreted using Highscore Plus v.4.7.

United States PADD II: Refinery Yield: Gain or Loss data was reported at -5.800 % in Apr 2018. This records an increase from the previous number of -5.900 % for Mar 2018. United States PADD II: Refinery Yield: Gain or Loss data is updated monthly, averaging -5.500 % from Jan 1993 (Median) to Apr 2018, with 304 observations. The data reached an all-time high of -3.600 % in Jul 1994 and a record low of -6.900 % in Feb 2006. United States PADD II: Refinery Yield: Gain or Loss data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.RB061: Petroleum Refinery Yield.

Production: Petroleum Refinery Product: Barrel: Univen: Domestic data was reported at 0.000 Barrel in Jun 2019. This stayed constant from the previous number of 0.000 Barrel for May 2019. Production: Petroleum Refinery Product: Barrel: Univen: Domestic data is updated monthly, averaging 0.000 Barrel from Jan 2000 (Median) to Jun 2019, with 234 observations. The data reached an all-time high of 63,854.044 Barrel in Sep 2009 and a record low of 0.000 Barrel in Jun 2019. Production: Petroleum Refinery Product: Barrel: Univen: Domestic data remains active status in CEIC and is reported by National Petroleum Agency. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBD011: Production: Petroleum Refinery Product: Barrel: by Source: by Plant. Petroleum: Refinery Product: National refers to volume of petroleum processed in national refineries, where the origin of petroleum is domestic.

Dataset

This dataset was created by Mohamed Mahmoud Ragab

Contents

Alpaca-CoT -- EN (refined by Data-Juicer)

A refined English version of Alpaca-CoT dataset by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to fine-tune a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 226GB).

  Dataset Information

Number of samples: 72,855,345 (Keep ~54.48% from the original dataset)

  Refining Recipe… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/alpaca-cot-en-refined-by-data-juicer.

Refinery Crude Runs in the United States decreased to 211 Thousand Barrels in November 21 from 259 Thousand Barrels in the previous week. This dataset provides - United States Refinery Crude Runs- actual values, historical data, forecast, chart, statistics, economic calendar and news.

qPCR detection results for Topeka Shiner eDNA surveys in MN and IA Oxbows. This data was used to determine the optimal eDNA sampling methods for this species in oxbow habitats. The methods used to collect this data and the summary and interpretation of the results can be found in our final report entitled: Refining the use of environmental DNA (eDNA) as a method to detect presence of the endangered Topeka Shiner (Notropis topeka).

This statistic shows selected data about the Dangote Oil Refinery in Lagos, Nigeria as of 2017. The Dangote Oil Refinery, which is expected to open in 2019, will be the world's largest oil refinery when it is completed. The project's production capacity is expected to be approximately ******* barrels of oil per day.

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Oil Refining Market Size 2025-2029

The oil refining market size is forecast to increase by USD 111.2 billion at a CAGR of 1.3% between 2024 and 2029.

The market is driven by the surging demand for refined fuel, with increasing global mobility and industrialization fueling this trend. The adoption of modular mini refineries is another key driver, as these facilities offer cost-effective and efficient solutions to meet local fuel demands in regions with limited infrastructure. However, the market faces significant challenges, including the costly and time-consuming nature of oil refinery maintenance operations. The oil refining market is essential for producing various transportation fuels, including fuel oils, gasoil, and liquefied petroleum gas (LPG).
These complex processes require substantial resources and planning, making it essential for companies to optimize their maintenance strategies to minimize downtime and maximize productivity. Effective implementation of predictive maintenance technologies and strategic partnerships can help refineries navigate these challenges and capitalize on the market's growth opportunities. Process control instrumentation and energy conservation measures are essential components in maintaining profitability and sustainability in the oil refining industry. The demand is driven by sectors such as transportation and power generation, with developing countries in Asia, including India and China, being key contributors.

What will be the Size of the Oil Refining Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, process optimization techniques and stream analysis play a pivotal role in enhancing efficiency and reducing costs. Heavy oil upgrading, a significant segment, employs advanced methods like reactor temperature control and catalyst selection to maximize output. Petroleum coke production, a byproduct of refining, is subject to stringent safety management systems and environmental impact assessments. Distillation tower design and heat exchanger efficiency are crucial in optimizing energy consumption. The market is a critical component of oil and gas downstream, focusing on the processing and refining of crude oil into valuable products. Hydrogen production methods, integral to various refining processes, are undergoing innovation to minimize costs and improve yields. Fractionator control systems ensure consistent product quality, while pipeline integrity management and pressure control systems maintain safety and reliability.

Environmental considerations are driving the adoption of waste minimization strategies and desalting process control. Thermal cracking methods, a key refining technology, continue to evolve, with process simulation software aiding in optimizing operations. Reactor temperature control, reactor catalyst selection, and paraffin wax production are areas of ongoing research for improved performance and reduced emissions. Asphalt production methods and bitumen processing are also undergoing technological advancements to meet evolving market demands. Additionally, the growing aviation industry significantly contributes to market expansion, as it requires a substantial supply of jet fuel to support increasing air travel and cargo transportation.

How is this Oil Refining Industry segmented?

The oil refining industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Light distillates
  Middle distillates
  Fuel oil
  Others


Fuel Type

  Gasoline
  Kerosene
  LPG
  Others


End-user

  Transportation
  Petrochemicals
  Residential and commercial heating
  Power generation
  Others


Capacity

  Large-scale refineries
  Medium-scale refineries
  Small-scale refineries


Geography

  North America

    US
    Canada


  Europe

    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Product Insights

The Light distillates segment is estimated to witness significant growth during the forecast period. The market is driven by the demand for light distillates, particularly gasoline, from the transportation sector. Light distillates, which include petrol or gasoline, accounted for the largest market share in 2024. Light crude oil, the primary feedstock for producing light distillates, contains a higher proportion of hydrocarbons and is easier to refine compared to heavier variants. This results in a greater yield of gasoline and diesel from light crude oil. The transportation industry's reliance on gasoline as a fuel source further increases its demand. Crude oil distillation is a crucial

These environmental DNA data and corresponding water quality data were collected and analyzed by the Fish and Wildlife Service in 2017. The samples were collected from 4 sites in pools 17 and 18 in the Upper Mississippi River on 3 sampling trips. The data was used to study occupancy modeling of eDNA data and determine optimal sampling effort required for reliable detection of invasive Bighead Carp and Silver Carp in streams with similar attributes at the Mississippi River.

This report summarizes revisions to the design basis for the linear programing refining model that is being used in the Refining and End Use Study of Coal Liquids. This revision primarily reflects the addition of data for the upgrading of direct coal liquids.

TechnicalRemarks: This repository contains the supplementary data to our contribution "Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry" to the 2022 Measurement Science and Technology special issue on the topic “Machine Learning and Data Assimilation techniques for fluid flow measurements”. This data includes annotated images used for the training of neural networks for particle detection on DPTV recordings as well as unannotated particle images used for training of the image-to-image translation networks for the generation of refined synthetic training data, as presented in the manuscript. The neural networks for particle detection trained on the aforementioned data are contained in this repository as well. An explanation on the use of this data and the trained neural networks, containing an example script can be found on GitHub (https://github.com/MaxDreisbach/DPTV_ML_Particle_detection)