Plant SPI Inc. asset at risk, $ million Inc. prod. at risk, thousand bpd Inc. prod. at risk, $ million NA-P1 89% 10 30 3 NA-P2 90% 3 10 1 EU-P3 96% 0.1 1 0.1 ME-P4 90% 2 8 0.8 AP-P5 100% 0 0.5 0.05
FIG. 8. Example of a corporate dashboard.
0
Jan Feb Mar Apr MayJuneJuly Aug Sept Oct Nov Dec 20 40 60 80 100 Per ce nt
0Jan Feb Mar Apr MayJuneJuly Aug Sept Oct Nov Dec 20 40 60 80 100 Per ce nt 0
Jan Feb Mar Apr MayJuneJuly Aug Sept Oct Nov Dec 20 40 60 80 100 Per ce nt 0
Jan Feb Mar Apr MayJuneJuly Aug Sept Oct Nov Dec 20 40 60 80 100 Per ce nt 0
Jan Feb Mar Apr MayJuneJuly Aug Sept Oct Nov Dec 20 40 60 80 100 Per ce nt Leadership Competency
Plant value
at risk
Safety device management Incident reporting
Op. readiness Inc. rev. at risk $ 1 million Safety performance indicator 89% Inc. prod. at risk 5,000 Inc. asset at risk $ 10 million
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62MARCH 2013 | HydrocarbonProcessing.com
Safety Developments
Calculating the weighted KPI for a protection layer.
The KPI for a LOP can be calculated as:
KPI _ LOPj= e (wi×KPIi) i K j ∑ e∑iK(wi) where:
KPI_LOP = Weighted average KPI of a layer of protection w = Weight of a KPI7
KPI = Key performance indicator related to plant, process,
people (as applicable)
K = Number of KPIs for an LOP I = Index for counting number of KPIs J = Index for counting number of LOPs.
Calculating safety performance index for equipment.
Consider that a piece of equipment has a number of LOPs. From a safety perspective, the LOPs are of different impor- tance and risk levels. From the LOPA, each layer has an associ- ated risk-reduction factor. The weighted KPIs associated with the equipment can be aggregated and weighted, using the risk- reduction factor associated with the LOP:
SPI _EQUIPj= w _lopi× i Lj
∑
KPI _ LOPi wi i L∑
= rrfi×KPI _ LOPi i Lj∑
wi i L∑
where:L = Number of layers of protection
w_lop = Weight of a layer of protection (= RRF for the layer
of protection)
I = Index for counting LOP
J = Index for counting number of pieces of equipment. Calculating safety performance index for a facility.
Consider that a facility has a number of LOEs. From a safety perspective, LOEs are of different importance/risk levels. From the LOPA, each LOE has associated with it a total equip- ment risk. The SPIs for the LOEs can be aggregated using the total risk factor calculated from the LOPA:
SPI _ PLANT = 1 EQ _ RISKi i E
∑
×SPI _ EQUIPi 1 EQ _ RISKi i E∑
where:E = Number of pieces of equipment in a plant
I = Index used to count the pieces of equipment in the plant EQ_RISK = Total mitigated risk for a piece of equipment 8 SPI_PLANT = SPI for the plant
Estimated losses associated with LOE risk and plant.
Based on the SPI, a safety performance state can be calculated. For example, the SPI can have ranges such as good (> 95%), warning (90% to 95%) and bad (< 90%). Associated with each LOE is an asset impact. For example, the asset impact may be defined as S0 to S5, as shown in TABLE 2. Incremental
estimated asset value-at-risk is a safety performance adjusted metric (expected value) that can be calculated using the SPI, the safety performance state and the asset impact.
For example, the incremental asset value-at-risk can be es- timated as follows: 100% of the asset loss value-at-risk if the safety performance state is determined to be “bad”; 50% of the asset loss value-at-risk if the safety performance state is deter- mined to be “warning”; 0% of the asset loss value-at-risk if the safety performance state is determined to be “good”:
LOE: Estimated incremental asset value-at-risk:
=
0 if SPI > 9 5%
0.5 defined asset impact if SPI 90% and 95% 1 defined asset impact if SPI < 90%
The plant-level incremental asset value-at-risk can be estimated by adding the estimated incremental asset values-at-risk for the LOEs with the facility. The plant-level incremental production value-at-risk can be estimated by adding the incremental pro- duction values-at-risk for the underlying lines of equipment:
Plant: Estimated incremental asset value-at-risk: =
∑
LOE incremental asset value-at-riskPlant: Esimated incremental production capacity at risk:
=
0 if Plant SPI > 95%
0.5×defined production capacity if Plant SPI ≥90% and ≤95%
1×defined production capacity if Plant SPI < 90% ⎧ ⎨ ⎪⎪ ⎪⎪ ⎪ ⎩ ⎪⎪ ⎪⎪ ⎪
For a corporation with many plants, the incremental asset val- ues-at-risk and the product values-at-risk can be aggregated as:
Corporation: Estimated incremental Asset value- at- risk: = Plant incremental asset value at risk
Corporation: Estimated incremental production capacity at risk: = Plant incremental asset value- at- risk
Dashboards. To display the SPI and related incremental asset
value-at-risk and incremental production loss, dashboards can be used, as shown in FIGS. 8 and 9. The plant-level dashboard
could display the plant safety-performance data and provide drill-down capability to the underlying KPIs for analysis of the underlying causes of identified risks. Once identified, correc- tive action plans can be defined and implemented in a timely manner to avoid costly catastrophic safety events.
TABLE 2. Asset impact levels vs. asset value and production losses
Level Asset loss value Production loss, bbl
S0 $10,000 0 S1 $100,000 1,000 S2 $1,000,000 5,000 S3 $10,000,000 15,000 S4 $100,000,000 50,000 S5 $1,000,000,000 100,000
Hydrocarbon Processing | MARCH 2013 63
Safety Developments
Best practices and lessons learned. As proven with the
name of the American Fuel and Petrochemical Manufacturers’ (AFPM’s) safety conference, i.e., the National Occupational and Process Safety Conference, the refining and petrochemi- cal industries are clearly focused on PSM as a key component of their operational strategies. To support these operational strategies, there are nine steps or best practices to use when implementing and maintaining an effective process safety- performance management system:
Step 1. Establish the organizational arrangements/rela- tionships needed to implement indicators.
Step 2. Decide on the scope of the indicators.
Step 3. Identify the risk-control systems and decide on the outcomes.
Step 4. Identify critical elements of each risk-control system. Step 5. Establish the data collection and reporting system. Step 6. Review (benchmark against the IE PSM Frame- work or equivalent).
Step 7. Deploy the KPI model and SPI calculations. Step 8. Educate management on the importance of PSM. Step 9. Establish management roles and actions for review of KPIs, SPIs, estimated asset value-at-risk and estimated pro- duction value-at-risk.
LITERATURE CITED
1 “A Canadian Perspective of the History of Process Safety Management Legislation,”
8th International Symposium: Programmable Electronic System in Safety-Related Applications, Sept. 2–3, 2008, Cologne, Germany.
2 Center for Chemical Process Safety website: http://www.aiche.org/CCPS/
Students/GetSmart.aspx.
3 Center for Chemical Process Safety website: http://www.aiche.org/CCPS/
Students/GetSmart.aspx.
4 H. J. Toups, LSU Department of Chemical Engineering, with significant material
from SACHE 2003 Workshop.
5 Managing the Risks of Organizational Accidents, Ashgate Publishing Co., 1997. 6 Energy Institute, London, 1st Ed., December 2010.
7 A weight of 0 signifies that a KPI is not used.
8 This is equal to the sum of all the mitigated risks for an item of equipment.
MARTIN A. TURK, PhD is the director of Global Industry Solutions for the HPI for Invensys Operations Management at Houston, Texas. For most of his 40+ years of experience, Dr. Turk has been involved in engineering, consulting, sales and marketing activities related to process automation. These activities include process simulation, advanced control and information/automation system strategic planning. Dr. Turk is responsible for definition of industry-specific solutions for downstream petroleum refining and petrochemicals, participation in industry conferences and working with Invensys clients worldwide to identify and quantify automation opportunities in their manufacturing facilities that will provide them with significant returns on investments. He received his BS degree in chemical engineering from the University of Dayton and his PhD in chemical engineering from the University of Notre Dame. Also, he has published technical papers and made presentations at domestic and international seminars on a variety of subjects related to advanced automation solutions for the process industries.
AJAY MISHRA is the R&D program manager at Invensys. He helps define the detailed features and technology roadmaps for the Triconex branded safety & critical control products. Mr. Mishra holds a BSEE degree from the College of Engineering, Pune, India and an MBA from the UCLA Anderson School of Management. He has over 20 years of experience in safety and critical control systems in process control SIS, and railways systems including product development, project engineering, project management and product management. Mr. Mishra is a TÜV certified Functional Safety Engineer for hardware/software design (IEC 61508) and Safety Instrumented Systems (IEC 61511).
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