Oil price variations impact the selection process for refinery crude slates. When considering crude and product prices, product demand, refinery configurations and other constraints, the evaluation process can be exhaustive. Software tools are comprehensive and time-consuming to screen and rank crude oils on a regular basis.
To simplify this process, a quick methodology was developed to screen a crude oil basket. The differential in crude oil price and processing costs are the most influential factors that determine crude selection. Typically, the differential in crude oil prices is in relation to the benchmark crudes like Brent price and the quality. The differential in processing cost is primarily associated with hydroprocessing and residue evacuation at lower prices. The sensitivity analysis should include crude, hydrogen and residue prices.
The crude selection model presented in this study is an Excel-based tool; it requires minimum data input to determine crude oil potential and rankings. With this method, refiners, crude-oil traders, supply-chain optimizers and crude schedulers can make quick and accurate business decisions.
Crude oil processing
Worldwide trends. At present, the refining industry is processing opportunity crude oils, as benchmark crude prices surpassed $100/bbl. These crude oils are available at cheaper prices due to inferior qualities.1 Also, fuel specifications have become more stringent with stricter environmental regulations. Result: Producing cleaner fuels is more difficult. Processing poorer quality crude oils to yield higher-quality products may add exorbitant costs through hydroprocessing.2 In this scenario, processing opportunity crude oils and meeting tighter product specifications is the real challenge while achieving higher refinery margins. We must ask the question: Is processing opportunity crude oils always a profitable option for refiners throughout the world?
It can be yes, if a refiner is a lower-cost producer. However, there are many uncontrollable factors, such as crude oil prices, economic conditions and environmental regulations for fuel specifications; all affect refinery economics and profitability. Benefiting from the least expensive crude oils in the market, such as high-acid crudes (HACs), heavy-sour crudes and extra-heavy oil produced from oil sands, refiners can keep margins high and stay ahead of the competition.3
The answer can be no. Not every refiner can handle crude oils with a high corrosion propensity and a disproportionate amount of heavy residues. Crude cost is the single most important determining factor for profitability. The differential between a particular crude oil and benchmark crude oil can be great (more than 20%) and it widens as market oil prices rise.
Several methods have been used to relate these differentials to the qualities of crude oils.4,5 For refineries that have more freedom in crude oil choices, selecting the optimum crude oils is vital. These crude oils are available at discounted prices with compromised qualities such as API, sulfur (S), total acid number (TAN) and many other impurities.6 Assessing the refining processes for such crude oils is crucial, as the differential margin offered by opportunity crude oils could justify modifications or expansions to facilitate efficient processing of such feedstocks.7, 8 These crude oils produce inferior quality product streams including diesel, vacuum gasoils (VGO) and more residues that require additional processing and/or evacuation at lower cost. In hydroprocessing, such streams consume large quantities of hydrogen; there is an exorbitant additional cost due to high hydrogen prices.
The cost of crude oil has the larger influence on refining business; it involves 80%90% of the total product cost. Cheaper crude oils, however, require additional processing to meet critical product specifications of Euro III, IV and V grades. More capital and operating costs, likewise, squeeze profits. Opportunity crude oils not only influence the processing cost due to hydrogen deficiencies, but they also produce more byproducts and residuals that must be upgraded or blended off with cutter stocks such as kerosine/diesel or as lower-cost fuel oils (FOs). Accordingly, refiners have the flexibility to re-evaluate options when processing opportunity crude oils for profitability.2 The presented study discusses how to investigate more accurately discounted, poorer-quality crude oils against processing costs and profitability. This study is supportive for quick decisions on selecting crude oils and ranking them on a regular basis.
Detailed crude evaluation and selection
The classical evaluation method to identify the potential of crude oils is done through laboratory experiments and analyses, which are lengthy processes. Typically, crude oils are classified in nine different categories by qualitiesAPI (light/medium/high) and sulfur (sweet/medium/sour), as shown in Fig. 1. This is an indication of preliminary qualities for the crude oils; it can be used to judge the characteristics and performance of the crude oil and the processing measures needed.
Fig. 1. Typical classification of crude oils by
sulfur content and API.
On the basis of these two qualities, a discount is offered. It is one of the factors considered during crude oil trading.9 However, it is not completely accurate for refining business decisions. Thus, a detailed crude oil evaluation is done at the laboratory to obtain more important information, which is very useful for refinery operations and processing of the crude oils. The detailed crude oil evaluation provides the valuable database to counter the processing difficulties and assist in planning the processing of the opportunity crude.
To achieve optimal crude oil selection and processing decisions, the refiner always needs detailed evaluations that refer to the quality of the crude oil. This includes true boiling point (TBP) distillation yield profile data for the atmospheric and vacuum distillation units to meet product demand and refinery capacity utilization. The detailed crude oil evaluation also covers the characterization of the crude oil and cut-wise analyses for all important fractions including liquefied petroleum gas (LPG) potential, naphtha, kerosine, diesel, vacuum gasoils (VGOs), atmospheric residue (AR) and vacuum residue (VR).
This detailed information is obtained through standard laboratory test methods, normally API and/or ASTM.10 This extensive laboratory analysis of the crude oil is a costly and a time-consuming process. Such analysis is expensive, and, generally, it takes four to six weeks to complete. However, the detailed information is typically unavailable during the selection of crude oils. It does not consider the processing costs involved, which are very important parameters for decision making. Even during actual refining operations where multiple feeds and blends are processed, the refining process decisions are not accurate for hydrogen consumption, residue evacuation and intermediate distillate routings for maximum values.11
The detailed crude oil evaluation is vital when considering the process based on crude oil qualities (API, S), detailed crude assay and simulation/linear programming (LP) tools. Laboratory evaluation and historical crude oil data are used in synergy and its compatibility with the refinery configuration.12, 13 Accurate crude oil quality data are essential for precise scheduling and planning inside the refinery. This can be achieved by commercially available rigorous software tools that are comprehensive, time-consuming and complex. Rigorous models are not always suitable when solving complex problems over refinery margins, which are more conceptual and economic than scientific and technical. Rigorous models are not designed for fine-tuning actual refinery operations. They do not use available information; instead, they require tremendous input data that may not always be available during the selection process.
It is even difficult to obtain economically relevant results to operate the crude distillation unit.14 Integration of best practices in refinery planning and scheduling, along with crude oil selection based on economics, is essential to make better business decisions.15 Therefore, a quick estimate of relative crude oil potential, accounting for real-life scenarios with minimum inputs becomes a necessity. The procedure is accurate, tunable to the existing reference operation and flexible for any new proposed refinery operation and crude data. More importantly, this evaluation is easily understood by both the trader and refinery economist. To achieve these objectives, a simplistic approach was proposed; it considers Brent crude price variations, the differential of the crude oil price due to its qualities, hydroprocessing cost, resid-evacuation cost, crude and product prices, product demand, refinery configurations and constraints. This is a simple and swift Excel-based tool, and it can be used to determine crude oil potential and their rankings quickly. This tool can be part of a comprehensive simulation software. Crude oil processing can be monitored on a regular basis for benefits.
Refinery processing costs (RPCs) mainly include the hydroprocessing costs for diesel and VGO streams and residue evacuation. In this study, a heavy-sour crude oil processing train with a diesel hydrotreater and VGO hydrocracker as secondary processing units are considered while evaluating crude oils under different scenarios. VR is evacuated as FO by adding distillates as cutter stocks.
Diesel and VGO derived from the heavy-sour crude oils are sent to hydroprocessing where sizable hydrogen consumption occurs. Primarily, the poor quality of the diesel and VGO is the main reason for high hydrogen consumption. Hydrogen is used to remove sulfur from the diesel-product streams via the diesel hydrodesulfurization (DHDS) unit, as shown in Fig. 2. When qualities of the diesel (density and sulfur) are poor (high-density and high-sulfur), large quantities of hydrogen are necessary. From Fig. 2, the lines in the nomogram are for constant hydrogen consumption (expressed as wt% of feed) with varying feed quality. This nomogram is also helpful in hydrogen management of DHDS unit vs. diesel qualities.
Fig. 2. Hydrogen consumption as wt% of
feed to the hydrotreater and as a function of
DHDS feed properties.
Hydrogen supply and demand. When VGO is sent to the hydrocracker unit (HCU), the characteristics of VGO such as aromatic content, sulfur and other impurities (V, Ni, basic N2, etc.) are the main culprits for maximum hydrogen consumption. The characteristics of VGO are mainly influenced by the vacuum tower operations, endpoints and the crude oils processed.16 These properties also affect the HCU catalyst and run length. In addition, they restrict throughput capacity of the hydroprocessing units as hydrogen capacity is a limiting factor. It is essential to select crude oils and/or blends that minimize hydrogen consumption. The study would also be helpful in hydrogen management in both DHDS and HCU for feed qualities vs. hydrogen availability within refinery premises.
To reduce hydrogen consumption, superior quality of VGO must be sourced, which may be obtained from more expensive crude oils.2 However, it would be a challenging task to meet both the requirements of processing opportunity crude oils and minimizing hydrogen consumption. The random selection of the crude oil basket for hydrogen savings may not be a feasible economical option. There are many implicit factors involved in crude oil trading such as oil production, scheduling, planning, price fluctuations and availability, etc. The approach to select a crude basket for hydrogen savings would not have meaning unless it is provided several benefits. In this scenario, it is essential to carry out detailed analyses for hydrogen savings and total processing benefit.
The contribution of residue evacuation on refinery processing costs should also be considered. Recently, residue is the key focus for refinery profitability. Crude oils are traded at high cost, and typically up to a 25 wt% portion is the residue content, which has very low value. Without residue-upgrading facilities, such as delayed coker, solvent de-waxing, visbreaker, etc., the residues are evacuated at the lowest cost.17 It is either used to produce FO/low-sulfur heavy stock (LSHS) where vacuum residues are blended with cutter stock (kerosine/diesel) or bitumen production. However, not all crude oils are suitable for bitumen production. Thus, VR is a burden to refineries, where up to 20 wt%25 wt% portion of the costly crude oils are diverted to low-cost products. It is very essential to understand residue yields and the evacuation process during the selection process.
Modeling approach for crude oil selection
High API and low-sulfur crude oils yield superior quality of diesel and VGOs. These crudes are more suitable for hydrogen savings and they also yield low residual products. From the total optimization perspective, several criteria should be examined closely and major issues include:
Price difference between crude oils
Hydroprocessing costs and hydrogen capacity limits
Residual generation and disposal.
Price. Crude price is mainly a function of Brent crude oil price and its quality is given as:9
Crude price = Brent price [1-m1 3 (∆API) m2 3 (∆ sulfur)]
Where m1 and m2 are constants.
Hydroprocessing and resid-evacuation costs and unit constraints are given as:
RPC = (DieselPC)DHDS + (VGOPC)HCU + VR evacuation
(DieselPC) DHDS = Diesel processing cost at DHDS
(VGOPC) HCU = VGO processing cost at HCU
(VR evacuation)PC = VR evacuation processing cost.
The model considers two elements for crude selection:
Differential with Brent crude oil price, D1
Differential of refinery processing cost with Brent crude processing cost, D2.
The net differential of D1 and D2 decides the position of crude oil for their rankings and selection based on net margins.
In this modeling approach, the balance of carbon (C), hydrogen (H) and impurities (I) are considered across the system to determine hydrogen consumption via hydroprocessing operations.1820 Correlation models were developed for crude and refinery processing costs. The RPC consists of the processing cost of hydrogen at the hydrotreater, hydrocracker and evacuation of VR with additional cutter as FO/LSHS.
This study was conducted with crude oils, which are processed globally. The additional costs through hydrogen consumptions are observed for diesel and VGO to upgrade product quality and to meet product specifications. Also, an additional cost for evacuation of residues to FO/LSHS and/or bitumen production is included in the model. The cutter requirements for disposal of VRs are categorized as low-sulfur VR (LSVR) and high-sulfur VR (HSVR). The cutter requirement for HSVR and LSVR are assumed to be 50 wt% and 30 wt%, respectively, as per refinery practices. The sensitive analyses were done with regard to hydrogen and FO prices to check the profiles for RPC. Selection of crude oils from the basket and/or selection of new crude oils and/or blends is possible through the model. In the current case, due to lack of major residue upgrading facilities, the RPC contribution is 5%15% of the crude oil cost. The differentials of LS and HS crude oils do have a major impact on crude selection. The proposed crude selection model considers theses assumptions:
The cut-points of diesel and VGO are 240°C360°C and 360°C565°C, respectively
All components other than C and H are considered as impurities for the calculations
Price of hydrogen is five times the Brent crude price
Price of FO is 50% of Brent crude price.
Cutter requirement for high S (Scrude > 0.5 wt%) VR is 50 wt%
Cutter requirement for low S (Scrude < 0.5 wt%) VR is 30 wt%
Sensitive analyses with variations in hydrogen, FO and Brent price have been considered
The listed inputs can easily be modified into the tool with varying price scenarios.
Crude selection model equations:
Crude price = Pricebrent 3 [1 + A 3 (APIcrudeAPIbrent) + B 3 (ScrudeSbrent)]; (1)
error < ±3%
FO cost ($/kg) = X % of Brent cost ($/kg)
VR evacuation cost ($/bbl) = [VR (kg/bbl) + Cutter (kg/bbl)] 3 [(Crude cost FO cost) ($/kg)] (2)
(RPC)Brent = C + D 3 Brent crude price (3)
(RPC)Crude = (RPC)Brent 3 (1+ E 3 (Crude API Brent API)
F 3 (Crude S Brent S) + G 3 (Crude VR) (Brent VR); error < ±5% (4)
D1 = Crude price differential ($/bbl) = (Brent crude price Crude price) (5)
D2 = RPC differential ($/bbl) = Refinery processing cost (Brent Crude) (6)
(Crude)cost = Cost of crude oils ($/bbl)
RPC ($/bbl) = f (Crude properties, hydroprocessing cost, VR evacuation cost)
Results of RPC
The existing crude oil basket was analyzed to estimate the RPC. The crude oils were categorized as LS (S < 1 wt%) and HS (S > 1 wt%). Fig. 3 depicts the RPC for LS crude oils; it was observed that the calculated and model-predicted RPC values are in good agreement. The LS crude oils are arranged by increasing the order of VR yields, which indicates the influence of VR yields content on RPC. Fig. 4 depicts the RPC values for HS crude oils. Again, the calculated and model-predicted RPC values are in good agreement. The HS crudes are arranged in the plot by decreasing API value to study the influence on RPC. Due to varying sulfur content and VR yields, the influence of API was not distinct. Thus, API is not the only factor influencing refinery processing. There are other factors to consider as part of the selection process. The RPC for Seria Ex Light (source: Brunei) and Arab Heavy (source: Saudi Arabia) crude oils are found to be lowest and highest, respectively. This data, however, are not for the final decisions as it must be compared with the crude oil price, which has a major contribution.
Fig. 3. Refinery processing costs for LS
crudes (S < 1 wt%).
Fig. 4. Refinery processing costs for HS
crudes (S > 1 wt%).
The RPC includes cost of hydrogen consumed by the diesel hydrotreater and by the VGO in the HCU and VR evacuation. The percentage contribution of hydrogen consumption and VR evacuation on RPC is shown here. The crude oil basket is categorized as LS and HS for this study. Figs. 5 and 6 show the RPC contribution for LS and HS crude oils. The RPC of LS crude oils is lower than HS crude oils of similar API. As LS crude oils yield higher amounts of distillates, thus lowering the quantity of the VR evacuation, it has a low RPC value.
The cost of diesel processing is the lowest for all the crude oils. Brega and Saharan blend crude oils have minimum hydroprocessing costs. Among hydroprocessing and VR evacuation, the contribution of VR evacuation is up to 80% for HS crude oils, as shown in Fig. 6. However, the hydroprocessing cost is higher than VR evacuation for some LS crude oils, e.g., Seria Export Light and Labuan, etc., as shown in Fig. 5. It depends on the qualities of the crude oils and residue yields.
Fig. 5. Contribution of RPC for LS crudes
(S < 1 wt%).
Fig. 6. Contribution of RPC for HS crudes
(S >1 wt%).
All of these influencing parameters were incorporated in the model to improve accuracy. In this scenario, the minimum input data of crude oils would be required for quick decisions. The calculated and model-predicted values are in agreement. Thus, the model equations can be used for any unknown crude oils/blends and/or synthetic crude oils to estimate RPCs. This approach would be helpful in optimization of crudes and blends to minimize hydrogen consumption and overall RPCs. The net result is improved margins.
Sensitivity analysis for RPC
Under the present refining environment, naphtha is facing significant competition from natural gas (NG) with regard to hydrogen production. In this scenario, growing NG usage including liquefied NG (LNG) and compressed NG (CNG) may be the cheaper option for hydrogen production. Thus, the variation in hydrogen prices can influence RPC. Varying hydrogen prices, up to ±50%, were studied. The variation in RPC for LS and HS crude oils with varying hydrogen price was analyzed and reported in Fig. 7. The variation in FO price on RPC was also studied for LS and HS crude oils, as shown in Fig. 8.
Fig. 7. Sensitivity analysis for RPC with
regard to hydrogen price.
Fig. 8. Sensitivity analysis for RPC with
regard to FO price.
From the results, the effects of hydrogen and FO prices are significant on processing trends (RPC). Processing LS crude oils (premium crude oils) has a lower RPC vs. hydrogen and FO price scenarios. Processing opportunity (poorer quality) crude oils has a high RPC. The variation in differential for RPC of LS and HS crude oils range up to $15/bbl and for FO price variations up to ±50 %. Similarly, the variation in differential of RPC of LS and HS crude oils range up to $7/bbl$15/bbl for hydrogen price variations up to ±50%.
The sensitive analyses of RPCs indicate that processing opportunity crude oils may not be a prudential option unless the differentials of crude oil prices (LS/HS) are large compared with RPC. Conversely, processing LS crude oils may be the better choice when the differential price of LS/HS becomes low at the prevailing prices of crude oils, hydrogen, FO and RPC.
Crude potential with varying Brent prices
The differential in crude oil prices vs. RPC continue to impact crude oil selection. Implicitly, these factors are influenced by crude and product prices, product demand, refinery configurations and constraints, and hydrogen. In the modeling approach, these parameters are included and simplified to facilitate the crude selection process with minimum inputs.
In the presented study, processing opportunity crude oils and hydrogen savings is a contradiction. Thus, processing crude oils, such as low API and HS, may not always be beneficial unless the differential costs of LS/HS crude oils are large enough. However, residual upgrading options, which can evacuate the residues at higher price than crude oil prices would be the probable solution for processing HS and low-API crude oils. Thus, a detailed evaluation of the selected crude oils is essential to fully address suitability of the refinery configurations and breakeven points. Conversely, high-API and LS crude oils are more suitable for hydrogen savings and minimizing RPCs. However, these crude oils are available at higher prices.
The crude selection model is based on a simplistic approach where the differential in crude oil prices is compared with differential in RPC for net margins. The differential in crude oil prices (D1) is f (Brent prices and qualities) and the differential in RPC (D2) is f (crude qualities, hydroprocessing cost, VR evacuation cost). Using the model equations, the differential in crude oil prices vs. Brent crude oil prices was studied and reported in Fig. 9. From this figure, the differential in crude oil prices for HS crude oils such as AM and Kuwait, are increasing steadily. Thus, processing poorer quality crude oils can be beneficial at higher Brent crude oil prices. However, the variations in premium-grade LS crude oils are less with Brent crude oil prices. Thus, processing LS crude oils such as BH, Saharan Blend and Brega may be the better option at lower Brent crude oil prices.
Fig. 9. Variation in crude differential (D1)
with Brent crude oil price.
The differential in D1 and D2 represents the net margins offered by a particular crude. The net margin (D1 + D2) was studied with varying Brent crude oil prices and reported in Fig. 10.The crossover between HS (Kuwait) and LS (Saharan) blends occur when Brent crude oil price is $90/bbl. It implies that up to $90/bbl of Brent crude oil price, Saharan Blend crude oil is a better option over Kuwait for net margins. Beyond $90/bbl of Brent crude oil price, Kuwait processing is beneficial.
Fig. 10. Variation in crude differential
(D1 + D2) with Brent crude oil price.
The model is generic to capture various scenarios of prevailing prices for Brent crude oils, crude oil qualities, refinery processing and constraints for selection. The present modeling approach facilitates quick pre-screening of crude oil baskets. This would be a useful tool for crude oil trading, where the ranking of crude oils is possible in a short time.
The study of crude oil for the refining business was reviewed, and many intrinsic details were discussed with traders, scheduler and planning, supply-chain optimization, technologists and refiners to develop a swift tool for the pre-screening of crude oils. The study highlights are:
Premium (high API, LS) crude oils are more suitable for hydrogen savings and minimizing processing costs. But these crudes may not be economical under all scenarios because they are available at higher costs. The price differential of benchmark and premium crude oils is less at lower crude oil prices. Thus, processing premium crudes may be economical at lower crude oil prices.
Poorer-quality (low API, HS) crude oils are available at comparatively low costs. But processing them is more difficult and it entails higher costs. This option may be economical when higher discounts are offered on these crude oils. However, the suitable refinery configurations may support reducing the RPC and making them suitable (optional).
To make the economical decisions, comprehensive tools are available, which required a detailed database. Such databases are not always available, especially for new crude oils/blends.
A user-friendly tool with minimum input data information for quick business decisions is sought by the refining industry. Such a tool would facilitate pre-screening of crude oils under different scenarios.
A simple and accurate tool can provide crude oil selection under real-life scenarios with prevailing price variations of benchmark crude oils, products, hydrogen and refining processes. The tool is also useful for hydrogen management within refinery premises on a regular basis. There is no alternative to a detailed crude oil evaluation done through rigorous experimentation following API/ASTM methods; however, the crude selection model is a step forward for unlocking the true value of crude oils for quick business decisions. HP
The authors express their sincere thanks to Mr. K. V. Seshadri, ED (R&D) for constant support and permission for publication. Many thanks to Mr. Ravitej and Mr. Sandip Agarwal from Scheduling and Blending department of the Mumbai Refinery for their inputs and validation of the crude selection model with actual refining processing data.
1 Goldhammer, B., et al., Future of opportunity crudes processing, Petroleum Technology Quarterly, Winter 2011.
2 Kumar, R., P. Parihar and R. K. Voolapalli New crude oil basket for hydrogen savings, Petroleum Technology Quarterly, Spring 2012.
3 Kumar, R., et al., Processing opportunity crude oilsA catalytic process for high-acid crudes, Hydrocarbon World, Vol. 4, No. 2, pp. 6468, 2009.
4 Bacon, R. and S. Tordo, Crude oil price differentials and differences in oil Qualities: a statistical analysis, Energy sector management assistance programme technical paper 081, 2005.
5 Fattouh, B., The dynamics of crude oil price differentials, Centre for Financial and Management Studies, SOAS and Oxford Institute for Energy Studies, January 2008.
6 Keamer, L., Crude oil and quality variations, Petroleum Technology Quarterly, Autumn 2004.
7 Blume, A. M. and T. Y. Yeung, Analysing economic viability of opportunity crudes, Petroleum Technology Quarterly, Autumn 2008.
8 Yeung, T. W., Evaluating opportunity crude processing, Petroleum Technology Quarterly, Winter 2006.
9 The World Bank, Crude oil prices, predicting price differential based on quality, note No. 275, October 2004.
10 HandBook on Crude Oil Evaluations, Vols. 1 and 2,Corporate R&D Centre, Bharat Petroleum Corp. Ltd., 2007.
11 Parihar, P., et al., Routing of intermediate distillate streams for refinery marginsOptimization, Hydrocarbon Processing, February 2012.
12 Lambert, D., Determination of crude properties, Petroleum Technology Quarterly, Spring 2007.
13 Hartmann J. C. M., Crude valuation for crude selection, Petroleum Technology Quarterly, Winter 2002.
14 Swafford, P. and M. McCarthy, Improving crude oil selection, Petroleum Technology Quarterly, Autumn 2008.
15 Stommel, J. and B. Snell, Consider better practices for refining operations, Hydrocarbon Processing, October 2007.
16 Rajeev Kumar, R. et al.,Maximization of VGO through deep-cut distillation, Petroleum Technology Quarterly, Spring 2011 pp. 8791, 2011.
17 Kumar, R., et al., Diverting low-sulphur heavy stocks for fuel oil production, Summer, Petroleum Technology Quarterly, pp. 4347, 2011.
18 Scherzer, J, and A. J. Gruia, Hydrocracking Science and Technology, Marcel Dekker New York, 1996.
19 Ancheyta, J., S. Sánchez and M. A., Kinetic modeling of hydrocracking of heavy oil fractions: a review, Catalysis Today, Vol. 109, pp, 14, 7692, 2005.
20 US Bureau of Standards, Miscellaneous Publication No. 97 (9.11.1929).
|The authors |
||Rajeev Kumar is deputy manager (R&D) with Bharat Petroleum Corp.,Ltd., India. His areas of interest are crude oil processing, refining processes, modeling, simulation and optimization. He also has research interest in process development for biodiesel and biolubricants. Mr. Kumar holds an MS degree in chemical engineering from the Indian Institute of Technology, Kanpur, India. |
||Prashant Parihar is deputy manager (R&D) with Bharat Petroleum Corp., Ltd., India. He has more than six years of research experience in hydroprocessing and optimization of refining processes. He holds an MS degree in chemical engineering from the Institute of Chemical Technology, Mumbai. |
||Ravi K. Voolapalli is chief manager at Corporate R&D Centre, Bharat Petroleum Corporation Ltd., India. He has 22 years of research experience. His areas of interest are refinery processes, coal-to-liquid technologies, modeling, scale-up and optimization. Dr. Voolapalli holds a BTech degree in chemical engineering from Andhra University, Visakhapatnam, an MTech degree in chemical engineering from the Indian Institute of Technology, Kanpur, and a PhD in chemical engineering from Imperial College of Science Technology and Medicine, London. |