How to Benchmark a Refinery’s Fuels Blending System? Part- 1 Concept

Introduction

The downstream crude oil refining business is correctly synonymous with the production of fuels such as gasoline, diesel, fuel oil, LPG, etc.  A typical refinery produces 75-80% of blended fuel products, made from mixing 10-12 intermittent products from process units.  The goals of process unit operations, also known as the onsite operations, are to produce these intermittent products cheaply, safely, efficiently, with the best quality and finalize the final fuel products to make them available to end consumers. Figure-1 shows the distribution of products made from a barrel of crude oil in the refinery and shows 80% of blended products versus 20% of non-blended products. 

crude oil

Figure 1. Distribution of Crude Oil Product

gas production

Figure 2. Economics of gasoline production

It is apparent in Figure-1 [1] that a refinery’s bottom-line of 7-8% profit margin is affected by the efficient and economical production of fuel products.  Compare this to the cost and profit of gasoline production in a 100KB/day refinery as shown in Figure-2 [2].  If effective and efficient production of gasoline fuel can save 2% of the retail price or 3.50 cents/gallon of gasoline, it translates to 24.28 M$/year additional net profit for a 100KB/day refinery.  It further amounts to about 242 M$/year in additional profit for a corporation with 10 refineries with 300KB/day average refining capacity.  Typically, savings for efficient fuel blending is about 3-4 cents/gallon of gasoline. We shall later discuss how a refinery can achieve these savings by minimizing Octane and RVP giveaways by a mere 33% (one sigma deviation of the giveaway distribution).

Blending operation concerns 

Since the refinery cannot sell sub-spec fuel products to end-users, their specifications are always met and certified.  However, refinery management is always looking for answers to the following concerns:   

  • Can re-blends and quality giveaways be minimized?¬†
  • Are our blends profitable?¬†
  • Do we have adequate infrastructure for blending?¬†
  • What is the payback for additional automation?¬†
  • How does our fuel blending perform compared to other refineries?¬†

Hence, refinery management is always looking for ways to improve their blending operations to maximize their profitability.  They usually resort to one of the resources as shown in Table-1 to guide them in their pursuit of improving blending operations. However, these resources range in cost, scope, comparative options, and post-study follow-ups.  Most of these resources (1 thru 4) use one-time analysis for one individual plant and do not perform comparative studies across corporate-wide refineries [3,4,5,6,7,8,9]. 

Table-1 Resources to Help Improve Blending Operations

No. Tasks & Resources Pros & Cons
1 Master Plan Study by an external consulting company  Very expensive (100k$+), time-consuming and requires extensive participation from refinery engineers and planner 
2 Technical Come? Feasibility Blending Study by consulting company  Moderately expensive (75K$+), time-consuming, and requires extensive participation from refinery engineers and planner 
3 In-house Blending Automation Study by plant blend engineer and planner  Less expensive (50K$+), time-consuming and requires extensive participation from refinery engineers and planner, may not have adequate in-house expertise  
4 Participation in Third Party APC and Automation Surveys  Relatively inexpensive (25K$+)/survey, comparative study, and surveys but covers very little of fuels blending 

Traditional Method of Blending Benefits Analysis ‚Äď One of the traditional ways the analysis of calculating blending benefits by any of the resources listed in Table-1 is conducted is by taking 3 months (preferably a year) worth of blend data and calculating the quality (RON, RVP and Sulphur) giveaways (Blend Spec-final blend quality) and graphically representing them on frequency plots as shown in Figure-3 and Figure-4. Using the average and standard deviation of blend data we can calculate and superimpose the normal probability plot to analyze the distribution pattern of the blend quality giveaways for RON (Figure-3) and RVP (Figure-4).

RON giveaway

Figure 3. RON giveaway distribution for
gasoline blend

RVP

Figure 4. RVP giveaway distribution for gasoline blends

Total Claimable Blending upgrade benefits can be calculated as follows:

equation

Next, we will discuss how the giveaway cost, C in the above equation (1) is calculated for Octane (RON), Reid Vapor Pressure (RVP) for gasoline blends, and Sulfur for Diesel and Fuel Oil Blends.

Octane Giveaway cost is calculated using the spread between premium grade (RON=93) and Regular Grade (RON=87) as follows:

CRON

For a 100 KBD refinery producing 45% gasoline and a standard deviation of RON giveaway 0f .225 octane, the annual savings from Octane giveaway can be calculated as

Annual Savings, 15.52 M$/year = 100,000 (bl/d) *.45 ( bl gas/bl oil) *42 (gals/bl) * 0.225 ON * .10833 ($/(gal gasoline* ON)) *

365 (days/year) ……………. .. (3)

Therefore, we have estimated that an upgraded gasoline blending system will save a refinery around 23.18M$/year from Octane and RVP giveaways alone. This represents about 3.35 cents of additional profit per gallon of gasoline. While it’s an attractive proposition for a refinery to upgrade its fuel blending system, the unanswered question that remains is how to achieve this additional increase in profit from gasoline blending. Refinery management often wonders:

  • What causes the quality giveaways source of the problem?
  • Is the blending infrastructure effective?
  • Is the blending operation carried out efficiently?
  • How do you ensure and measure the return on investment (ROI)?
  • Are the investment and returns scalable to other refineries?
  • How do other corporate refineries compare with each other and other refineries in the world?

The above and many other refinery gasoline blending concerns can be handled by two indices namely, Automation Effectiveness (AE) and Operational Efficiency (OE) developed by the authors of this paper and its POC (Proof-of-Concept) was documented during a case study by the authors for a South Texas 300kB/day refinery. The automation Effectiveness index analyzes the blending infrastructure whereas the operational efficiency focuses on the execution of blends using the infrastructure. We will next discuss these indices in detail.

Blending automation effectiveness index

This index analyzes the following automation islands of blending infrastructure and Figure-5 shows their interrelation and integration.

  1. Tank Farms
  2. Tank Gauging System
  3. Laboratory
  4. Field Equipment and Instrumentation
  5. Additive Control System
  6. Online analyzers and sampling system
  7. Distributed Control System (DCS)
  8. Advanced Blend Control System
  9. Blend Header
  10. Product Dispatch System
fuel blending system

Figure 5. Automation Islands of a Fuel Blending System

Each of the automation islands has its own degree of relative importance in the overall schema of the blending control system and therefore are assigned weighted percentages to enable us to benchmark the importance of these areas in a refinery’s fuels blending system.

We have adopted the following methodology to establish the weighted importance of the fuel Blending Automation Islands. Each of these areas has the following impact of considerations:

  • Manpower
  • Automation
  • HSE (Health, Safety, and Environment)
  • Benefits

Next, we assigned a percentage of weighted impact to each of the above impact areas. We then assigned a ranking from 1 to 10 (10 indicating the most impacted and 1 being the least) to each of the automation islands for each of the impact areas. For example, a tank gauging system and lab analysis would be the most manpower-intensive. Similarly, DCS and APC would be the most beneficial and automation-effective compared to other areas. HSE would be impacted most from tank farms, tank gauging systems, and field equipment to avoid fire, explosion, spillage, contamination, and outrages, etc. Table-2 shows generated weighted rankings of the fuels blending areas after using the weighted impact and its relative rankings.

No. Automation Island Weight %
1 Tank Farm 10
2 Tank Gauging System 15
3 Laboratory 10
4 Field Equipment & Instrumentation 10
5 Additive Control System  5
6 Online Analyzers and sampling system 10
7 Distributed Control System (DCS) 15
8 Advanced Blend Control System 15
9 Blend Header 0
10 Product Dispatch System 10

Next, we must define criteria for benchmarking a refinery’s state of each of the automation islands and we do so by enlisting the attributes of the limits, 0 and 100, of the automation index. These attributes of blending operation automation are defined as follows:

Automation Effectiveness Index = 0

It relates to the following all manual state of the blending infrastructure

  • Inadequately shared or non-dedicated stock/product tankage
  • Field equipment with manual ‚ÄúTurns‚ÄĚ and ‚ÄúPush‚ÄĚ Buttons
  • Manual tank gauging
  • Lab analysis not available on a timely basis
  • No online analyzers
  • No DCS / PLC/ APC
  • Recipe based on an algebraically linear model

Automation Effectiveness Index = 100

It relates to the following all fully automated state of the blending infrastructure:

  • Optimum and adequate allocation of stock /product tank All automatic/remote ‚ÄúTurns‚ÄĚ and ‚ÄúPush‚ÄĚ buttons
  • Automatic tank gauging with all required signals
  • Stock properties available online via a model-based quality tracking system
  • Multiplexed integrated NIR online analyzers with optimum sampling locations
  • DCS and advanced three-tiered blend control and optimization system
  • Recipes based on non-linear models and optimizer recipe

Once the boundaries and attributes of the Automation Effectiveness Index are defined, we analyze each automation area and its sub-modules and sub-components to the deepest level possible to estimate the refinery’s state of blending infrastructure. Table-3 shows an example of a refinery’s state of advanced blend control and optimization system based on the abovementioned analysis.

table of blend control

Table 3. Status Ranking of an Advanced Blend Control System

Table-3 shows that the refinery has only 7.5% of the required automation level for the advanced blend control and optimization system. A similar analysis of all blending automation areas and infrastructure produces an overall estimate of weighted Automation Effective Index (AE,). Table-4 shows the weighted automation area of importance, benchmarked state, observations, and recommendations to increase the refinery’s automation effectiveness of its blending infrastructure.

It should be noted here that the final automation effectiveness index as shown in Table-4 has some degree (¬Ī5%) sensitivity depending upon the relative weights assigned to impact of manpower, automation, HSE, and benefits to all automation areas, modules, and infrastructure. It generates a relative comparative index rather than an absolute one. The reproducibility of this index by different analysts may have a slight variation due to the subjective nature of the assignment for the impact weights and significance ranking of the automation modules and sub-modules.

table of refinery blending

Table 4. A Typical Refinery Blending Automation Status Versus Industry Benchmarks

Blending Operation Efficiency Index (OE) Earlier, we defined and discussed how to benchmark the refinery’s state of automation of blending infrastructure. Next, we discuss how well the refinery executes its blending operations and how can we benchmark it also. Just like the automation effectiveness index, we can again scale the blend operation efficiency between 0 and 100 based on its execution resources and methodology as follows:

Operation Efficiency Index = 0

It relates to the following all manual operation of the blending operation using primitive technology with the following attributes :

  • Batch blending
  • Changing stock qualities
  • Linear blend models
  • Linear optimization, if any
  • No stock usage economics
  • Linear models based recipe

Automation Effectiveness Index = 100

The maximum blending operation efficiency is attributed to the presence of the following blending execution features.

  • Inline blending
  • Online and real-time stock qualities
  • Non-Linear blend models
  • Non-Linear optimization
  • Stock usage and inventory economics
  • Multi-period and multi-blends planning
  • Automatic models tuning and reconciliation of bias and productions

Calculations of blending operation efficiency index

Now, the next step is to calculate the operation efficiency and it is far more complex than the automation effectiveness index. The analysis and estimation of the operation index require the expertise of a blending consultant who is very skilled in the data analysis and optimization of the blending recipe.

    1. Phase-1 of this task is to collect historical data of blending for a 3-12-month period depending upon the detailed analysis required by the refinery as shown in Figure-6. The format of this data is very specific to a refinery and requires its transformation to suit the data input structure of an optimizer employed by the consultant. The optimizer for the purpose must have certain features and will be discussed in part II (Case Study) of this series.
      blending operation efficiency

      Figure 6. Data Collection for Blending Operation Efficiency Calculations

    2. Phase – 2 of this task is to use the offline blending optimizer and perform a 3 – part data analysis to reoptimize historical blend recipes using linear and non – linear blend models to create a normalized scale of tangible benefits using linear and non – linear blending models. The next step would be to use nonlinear blend models to identify the benefits due to stock optimization, quality giveaway minimization, and inventory minimization which can be done only by using the non – linear blend optimization. Figure-7 shows the steps to calculate the blending operation efficiency index for a specific refinery in the study.
      Blending Operation efficiency

      Figure 7. Three Steps Analysis to Calculate the Blending Operation efficiency

Applications of the blending benchmarking methodology

Now, we have calculated two indices for the refinery’s blending infrastructure in terms of its automation effectiveness index and its blending execution methodology using the blending operation efficiency index. We list the following applications of the methodology to benchmark a refinery’s blending system.

    1. Absolute Benchmarks of State of Refinery’s Blending System
      Since both indices have attributes at both ends of the index scale, the refinery’s state of blending system can be shown as in Figure-8. It is rare, but not impossible, that a refinery can achieve the benchmarks of 100 for both indices as it all depends on initial refinery configuration, revamp and upgrade projects, and the management’s goal to improve the bottom line.
      blending system

      Figure 8. Absolute benchmarking indices for the blending system

    2. Estimating Budgetary Capital Investment and ROI for the Upgrade of the Blending System 
      The absolute blending benchmarking indices can estimate both capital investment to upgrade the blending system and its Return on Investment (ROI). The cost and benefits are shown in Figure-9 and are based on the authors’ experience with 10+ refineries in the blending upgrade projects and represent average values but can be much higher than indicated in Figure Р8. Please note that the investment does not include any upgrade of tankage as it usually is a basic refinery configuration and is not considered in a blending upgrade
      ROI

      Figure 9. Estimation of Investment and ROI Using the Blending Indices

    3. Relative Comparison of Blending System Across Refineries
      The benchmarked blending indices can be used to compare the state of the blending system across refineries of the same corporation or with other refineries in the world. These indices then can rank the refineries and indicate the impact of blending upgrade projects as shown in Figure-10. Figure-10 shows three categories of the blending states of the ranked refineries in terms of the scope of automation and potential benefits. The colored symbols in Figure-10 only show the refineries that can rank differently based on their blending indices and the directions for improvement in automation and efficiency.
      typical cases

      Figure 10. Typical Cases

    4. Tracking Blending KPI (Key Performance Indicator)
      The blending indices can also be used to document KPI from the investment by reevaluating the operation efficiency index one year after the completion of the upgrade project to gather enough historical blending operational data. This will justify the investment and document its validity.

Conclusion

This blog presents a methodology to benchmark a gasoline blending system in a refinery by evaluating its blending infrastructure to assess its automation effectiveness in terms of the index. It also details method s to analyze how the blending operations are executed in terms of deployed technology and modus operandi of daily blending operation, again by assigning an index. These two indices combined benchmark the state of the blending system in a refinery. It can be used to estimate budgetary capital investment and its return on investment (ROI). Blending systems in refineries across different corporations in the world or within a corporation can be ranked using these two indices to get a sense of their state of blending system. Further, the blending KPI can be estimated and tracked post-upgrade project implementation. Last but not the least, this methodology offers a cost and time-wise effective way to benchmark and estimate budget and benefits for a blending upgrade project. Part – II of this paper series will discuss a case study executed by the authors of this paper to outline all steps and techniques used for a 300 KB/day Texas, USA refinery.

References

  1. http://www.eia.gov/petroleum/gasdiesel/gaspump_hist.cfm
  2. http://www.indexmundi.com/commodities/?commodity=crude-oil
  3. Agrawal, S.S., M. J. Naughton, “Advanced Gasoline Blending – II‚ÄĚ, Oil & Gas Journal, Vol – 103.7, pp 52 – 57, February 21, 2005
  4. Agrawal, S.S., “Advanced Gasoline Blending – I‚ÄĚ, Co-author: M. J. Naughton, Oil & Gas Journal, Vol – 103.7, pp 50 – 53, February 14, 2005
  5. Agrawal, S.S., Leong K.M., Wee L.H., ECT James CTJ, “Implementation and Benefits of Online Tanks Quality Tracking System in a Singapore Refinery‚ÄĚ, Hydrocarbon Asia, Vol – 15, No – 1, pp 36 – 47, January/February 2005
  6. Agrawal, S.S., “Advanced Closed Loop Controls of Refinery Offsite Operations, Part – II‚ÄĚ, The International Journal of Hydrocarbon Engineering, Vol – 3, No – 4, pp 29 – 34, September 1997
  7. Agrawal, S.S., “Advanced Closed Loop Controls of Refinery Offsite Operations, Part – I‚ÄĚ, The International Journal of Hydrocarbon Engineering, Vol – 2, No – 4, pp 28 – 33, July/August 1997
  8. Agrawal, S.S., W.M. Beach, G.T. Rendon, R.O. Olevera, ‚Äú Implementation of Advanced Online Blend Control, Optimization, and Planning System in Mexican Refineries ‚ÄĚ, NPRA 96 Computer Conference, Atlanta, Georgia, November 9 – 12, 1996
  9. Agrawal, S.S., “Integrate blending Control, Optimization, and Planning‚ÄĚ, Hydrocarbon Processing, Vol – 74, No.8, pp 129 – 139, August 1995.
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