how to benchmark the state of a refinery's fuels blending

May 16, 2018 ©Dr. Suresh S Agrawal / Meena S Agrawal P a g e 1 | 15

How to benchmark the state of a refinery's fuels blending system? Part-I Methodology

An abridged version of this paper is published in “Benchmarking Fuel Blending Systems, Part-One”, Hydrocarbon

Engineering, March 2018, pp 101-108

How to Benchmark the State of A Refinery's Fuels Blending System?

Part-I Methodology

Dr. Suresh S. Agrawal Founder and CEO

Offsite Management Systems LLC Sugar Land, Texas, USA

Meena S. Agrawal

Co-founder and President Offsite Management Systems LLC

Sugar Land, Texas, USA

KEYWORDS

Fuels Blending, Benchmarking, Control & Optimization, Offsite Operations

ABSTRACT

A typical refinery produces about 75-80% of its crude throughput and produces its fuel products (gasoline, diesel, LPG, fuel oil) by blending 10-12 refinery products, which varies in both quality and monetary value. These fuel products have very strict specifications to meet and refineries use automated fuels blending control systems to optimize and control their properties. Any shortfalls in the delivered products specs, delays, and quality giveaways affect the enterprise bottom-line severely. Refinery management always looks for ways to identify and correct and enhance the shortcomings of their fuels blending systems. This paper discusses a methodology to benchmark the state of fuels blending systems in a refinery to compare with other refineries by using two indices, Automation Effectiveness (AE) and Operational Efficiency (OE). These two indices also gauge the budgetary investment required to either convert a manual system to an automated system or upgrade automated system to a state-of-the-art blending control system.

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 it available to end consumers. Figure-1 shows 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.

It is apparent in Figure-1 [1] that a refinery’s bottom-line of 7-8% profit margin is affected by an 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 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 100KB/day average refining capacity. Typically, savings in efficient fuels 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 giveaway by 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 giveaway be minimized? • Are our blends profitable? • Do we have adequate infrastructure for blending? • What is the payback for additional automation? • How does our fuels blending perform compare 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 option, 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].

Figure-1 Distribution of Crude Oil Products Figure-2 Economics of gasoline production



Traditional Method of Blending Benefits Analysis – 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 super impose the normal probability plot to analyze the distribution pattern of the blend quality giveaways for RON (Figure-3) and RVP (Figure-4).

Table-1 Resources to help improve blending Operations

No. Task and Resources Pros and 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

Figure-3 RON giveaway distribution for gasoline blends

0

5

10

15

20

25

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

No.

of B

lend

s

Blend Quality Spec- Final Blend Quality

Quality Giveaway for 82 Gasoline Blends over 3 months Data

Giveaway Quality Off-spec Quality

Actual Data for 82 blends for 90 daysSuperimposed Normal Distribution

Figure-4 RVP giveaway distribution for gasoline blends



Total Claimable Blending upgrade benefits can be calculated as follows:

MUS$/Year = 𝜎𝜎𝑐𝑐 * C * P …. (1)

Where

cσ = Standard deviation of final blend quality difference from blend quality spec limit C = Quality giveaway cost, US$/(bls*Unit Quality Giveaway) P = Yearly Fuels Production, MBLS/Year

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 Sulphur 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:

𝑪𝑪𝑹𝑹𝑹𝑹𝑹𝑹 = �𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑− 𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒓𝒓 �(𝑹𝑹𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑− 𝑹𝑹𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒓𝒓)

…………………(2)

= (2.429 – 1.779)/(93-87) = 0.10833 $/(gal gasoline* ON)

Where 𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑 = Retail Price, cpg of premium gasoline (April 2016)

𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒓𝒓 = Retail Price, cpg of regular gasoline (April 2016)

𝑹𝑹𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑 = Premium gasoline octane = 93

𝑹𝑹𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒓𝒓 = Regular gasoline octane = 87

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)

• RVP Giveaway cost is calculated using the spread between regular gasoline and butane price differential as follows:

𝑪𝑪𝑹𝑹𝑹𝑹𝑹𝑹 = (𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒓𝒓 − 𝑹𝑹𝑹𝑹𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝒑𝒑) …………………(2)

= (1.779 – 1.234) = 0.545 $/gal or $22.89/bl

Where 𝑹𝑹𝑹𝑹𝒑𝒑𝒑𝒑𝒓𝒓 = Retail Price, cpg of regular gasoline (April 2016)

𝑹𝑹𝑹𝑹𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝒑𝒑 = Retail Price, cpg of butane (Estimated for April 2016)



For a 100 KBD refinery producing 45% gasoline and standard deviation of RVP giveaway 0f .5 psi RVP, it will require 0.909% n-butane to be added to regular blend to increase from 9.0 to 9.5 psi RVP. This will require 909 bls/day (21.64 gals/day) to be added to the blend. Annual savings from RVP giveaway can be calculated as Annual Savings, 7.59 M$/year = 909 (bl butane/day) * 22.89 ($/bl Butane) * 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 additional profit per gallon of gasoline. While it’s an attractive proposition for a refinery to upgrade its fuels 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 problem?

• Is the blending infrastructure effective?

• Is the blending operation carried out efficiently?

• How do you ensure and measure the return on investment (ROI)?

• Is 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. 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 (AE)

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

1. Tank Farm 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

Figure-5 Automation Islands of a Fuel Blending System

Each of the automation islands have their 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 weighted importance of the fuel Blending Automation Islands. Each of these areas have following impact considerations:

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

Next, we assigned a percentage 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, tank gauging system and lab analysis would be the most manpower intensive. Similarly, DCS and APC would be the

Offline Blend Optimizer & Scheduling System

Online Blend Control & Optimization

Regulatory Blend Control

Tank Farm

Automatic Tank Gauging

System

Additive Control System

Field Equipment /

Instrumentation

Distributed Control System (DCS)

End ProductDispatch

Online Analyzers & Sampling

System

Sto

cks

Pro

duct

5

1

2

4

7

6

9

8Advanced

Control System

Pipelines

Rail Wagons

Trucks

Tankers

Tanks

Laboratory3

10BLEND

HEADER



most beneficial and automation effective compared to other areas. HSE would be impacted most from tank farm, tank gauging system, 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.

Table -2 Relative Ranking of Automation Areas

No Automation Island weight %

1 Tank Farm 10 2 Tank Gauging System 15 3 Laboratory 10 4 Field Equipment and 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 “Turns” and “Push” Buttons • Manual tank gauging • Lab analysis not available on a timely basis • No online analyzers • No DCS / PLC/ APC • Recipe based on algebraically linear models

Automation Effectiveness Index = 100 It relates to the following all fully automated state of the Blending infrastructure

• Optimum and adequate allocation of stock/product tanks



• All automatic / remote “Turns” and “Push” buttons • Automatic tank gauging with all required signals • Stock properties available online via 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 its attributes of the Automation Effectiveness Index is 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 refinery’s state of advanced blend control and optimization system based on abovementioned analysis.

Table-3 shows that the refinery has only 7.5% of the required automation level for the advanced blend control and optimization system. 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 (±5%) 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 slight variation due to the subjective nature of the assignment for the impact weights and significance ranking of the automation modules and sub-modules.

No Automation Island weight %Plant

Status, %

1 Offline Blend Optimizer 17 202 Online Blend Optimizer 11 103 Non-linear Blend Model 10 04 Multiperiod / Multi-blend offline Optimizer 15 05 Interface with offline and Online blend Control and Optimizers 6 56 Interface with online and Regulatory Blend Control 6 57 Feedback of blend performance 9 58 Online regression of blend parameters 11 09 Online blend bias Update 7 510 Historization of blend data 8 20

Over-all state of the automation Area and Infrastructure 100 7.5

Table -3 Status Ranking of Advanced Blend Control System



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 following attributes:

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

No Automation IslandWeighted Impotance

%

Refinery's Benchmarked

State %Comments Recommendation

1 Tank Farm 10 69.5 Adequate tankage, Expensive to improve, Low ROI

Stream Pooling

2 Tank Gauging System 15 85.0 Density Measurement Ignored Install density measurement3 Laboratory 10 55.5 Inadequate stock tank analyses

and frequency, Delays in results availability slows down the blending planning process and actual production rates

Analyze all required qualities in the lab, Install tanks qualities tracking System

4 Field Equipment and Instrumentation 10 12.5 High automation ROI and flexibility Automate "Turns" and "Pushes"

5 Additive Control System 5 75.0 Low ROI from automation Leave "Status Quo"6 Online analyzers and sampling system 10 25.0 High ROI from installation of

multiplexed integrated online analyzers such as NIR and other discrete analyzers such as RVP and Sulphur

Analyze required qualities online, install model based tank qualities measurement system

7 Distributed Control System (DCS) 15 85.0 No investment required in HW Interface RBC with Advanced Blend Control and Optimization System

8 Advanced Blend Control System 15 7.5 High ROI application System Install multi-blend/multi-periods offsline optimizer and online blend control and optimizers using the non-linear blend models and optimization algorithm

9 Blend Header 1 75.0 Component Connection order not optimized

Too Expensive to reorder

10 Product Dispatch System 10 70.0 Only tank to tank inline blender Inline certification required to pipeline/ships/barges blending

100 53.3

Table- 4 Typical Refinery Blending Automation Status versus Industry Benchmarks



Operation Efficiency Index = 100 The maximum blending operation efficiency is attributed to the presence of 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 requires 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.

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 normalize 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 study.

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 end 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.



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 shown in Figure-9 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-8. 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.

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 scope of automation and potential benefits. The colored symbols in Figure-10 only shows the refineries that can rank differently based on their blending indices and the directions for improvement in automation and efficiency.

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 upgrade project to gather enough historical blending operational data. This will justify the investment and document its validity.

Conclusion

This paper 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 index. It also details methods 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 benchmarks the state of 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”, Oil & Gas Journal,

Vol-103.7, pp 52-57, February 21, 2005 4. Agrawal, S.S., "Advanced Gasoline Blending-I”, 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”, , Hydrocarbon Asia, Vol-15, No-1, pp 36-47, January/February 2005

6. Agrawal, S.S., "Advanced Closed Loop Controls of Refinery Off-site Operations, Part-II”, 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 Off-site Operations, Part-I”, 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, “Implementation of Advanced On-line Blend Control, Optimization and Planning System in Mexican Refineries”, NPRA 96 Computer Conference, Atlanta, Georgia, November 9-12, 1996

9. Agrawal, S.S., "Integrate blending Control, Optimization and Planning”, Hydrocarbon Processing, Vol-74, No.8, pp 129-139, August 1995.



About the Authors

Dr. Suresh Agrawal is the founder and CEO of Offsite Management Systems LLC (OMS), Houston, Texas, USA. He graduated from Indian Institute of Technology, Mumbai, India with a Bachelor of Chemical Engineering. He then obtained Masters and PH.D. degree in Chemical Engineering from Illinois Institute of Technology, Chicago, USA.

Dr. Agrawal has 30+ years of experience at senior technical / management positions with international companies and he has

successfully managed many advanced refinery process control projects in numerous countries. Dr. Agrawal is a registered professional engineer in the state of Illinois, USA and is a member of American Institute of Chemical Engineers and Instrumentation Society of America. He has published and presented 20+ papers in international publications and conferences in the areas of advanced process control. He has also acted as a consultant to many refining and process industries worldwide, and delivers training seminars in the areas of his expertise.

Meena Agrawal is the co-founder and President of Offsite Management Systems LLC (OMS), Houston, Texas, USA. Meena is responsible for project management and execution as well as clients’ relations and business development. She graduated from Roosevelt University with a Bachelor’s degree in Chemistry and Associates degree in Computer Science from Morris County College, New Jersey.

Mrs. Agrawal has 20+ years of experience as technical lead / project management positions with major international companies such as Phillips 66, Exxon-Mobil, Apache Corp and handled projects for windows 7

migration, domain and system separation for ConocoPhillips and Phillips 66, Upstream (EU) application suite for supply and nomination, upstream gas/oil wells test and production data management, etc.

how to benchmark the state of a refinery's fuels blending

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