Which refiners are least CO2 intensive, and which refiners are most CO2 intensive? This spreadsheet answers the question, by aggregating data from 130 US refineries, based on EPA regulatory disclosures.
The full databasecontains a granular breakdown, facility-by-facility, showing each refinery, its owner, its capacity, throughput, utilisation rate and CO2 emissions across six categories: combustion, refining, hydrogen, CoGen, methane emissions and NOx (chart below).
CO2 intensity of oil refineries could rise by 20% due to IMO 2020 regulations, according to the estimates in this data-file, if a refinery chooses to convert all its high-sulphur fuel oil into low-sulphur diesel.
The driversare an extra stage of cracking, plus higher-temperature hydrocracking and hydrotreating, which will also have the knock-on consequence of increasing hydrogen demands.
Higher CO2 intensity conflicts with the industry’s aim of lowering its net emissions, and a 20% increase would effectively undo 30-years of prior efficiency gains in the refining industry.
Catalysts matter for refinery energy and CO2 intensity, as is shown in this data-file: It tabulates temperature and pressure conditions, disclosed for different refinery units, based on over 50 patents from leading energy Majors.
The average refinery processtakes place at 450C. But variability is high. Hence our data-file explains the variations as a function of the different catalyst compositions, being pioneered by the different companies.
Combining all the best-in-class new catalystsin the datafile, we think the average refinery could save 5kg/bbl of CO2 intensity: across hydrocracking, FCCs, steam cracking, coking, dewaxing, hydrotreating, alkylation and reforming.
This model calculates the costs of post-combustion carbon captureat a world-scale refinery, using today’s commercially available CCS technologies. The aim is to see whether the process could be economically competitive, as oil refineries emit c1bn tons of CO2 per annum.
Carbon capture costs vary unit-by-unit, as a function of the unit’s size and the CO2-concentration in its flue gas. Hence we estimate that c10-20% of refinery emissions can be eliminated for $XX/ton, the “middle 50%” will cost c$XX-XX/ton, while the final 20% will cost $XX-XX/ton. Calculations can be flexed in the model, using alternative input assumptions.
Our estimatesare informed by an excellent technical paper from Shell, which is also summarised.
This data-file tabulates details of the c35 companies commercialising catalysts for the refining industry. Improved catalysts are aimed at better yields, efficiencies and energy intensities. This is the leading route we can find to lower refining sector CO2 emissions.
In particular, we find five early-stage companies are aiming to commercialise next-generation refining catalysts.
We also quantify which Majorshave recently filed the most patents to improve downstream catalysts.
If you would like us to expand the data-file, or provide further details on any specific companies, then please let us know…
What if it were possible to displace dieselfrom high-cost, high-carbon “island” electricity grids, by charging up large batteries with gas- and renewable power, then shipping the batteries?
This model assesses the relative economics and relative CO2 emissions of such a possibility. The model is sensitive to oil prices, battery prices, hurdle rates and alternative power prices.
Economics should improveas battery prices fall. But costs are already competitive for several island grids, while CO2 intensity can be halved. Our numbers have been informed by disclosures from Gridspan Energy, a leading company in this space.
This data-file breaks down global CO2 emissionsinto 35 distinct categories, based on prior publications, our own models and calculations.
The long tail illustrates the complexity of decarbonisation. The largest single component of global emissions is passenger vehicles, but this comprises just c14% of the total CO2e.
A further 30 line-itemsall account for at least 1% of the world’s total emissions including electricity, heating, cement, metals, plastics, food, fertilizers, paper, manufacturing, livestock, agriculture, military, oil refining, fossil fuel production and landfill.
This data-file breaks down the financial and carbon costsassociated with a typical US consumer’s purchasing habits. It covers container-ships, trucks, rail freight, cars and last-mile delivery vans; based on the ton-miles associated with each vehicle and its fuel economy.
We estimate the distribution chain for the typical US consumer costs 1.5bbls of fuel, 600kg of CO2 and $1,000 per annum.
The costs will increase 20-40% in the next decade, as the share of online retail doubles to c20%. New technologies are needed in last-mile delivery.
Please download the modelto for a full breakdown of the data, and its sensitivity to oil prices, consumption patterns, international trade and exciting new delivery technologies.