National Seminar on

Awareness and Implementation of ISO 50001 in Oil &

Gas Sector

19th January, 2015

SCOPE Complex, New Delhi

Role of Data Analysis in implementation of ISO 50001

BY

R.K.Khilnani, Managing Director

Agenda to be covered in presentation

Why data analysis is more relevant for EnMS?

ISO 50001:2011 standard clauses requiring data analysis

Why baseline adjustments?

Tools used for data analysis

Different ways to analyze data

Flaws of specific energy consumption concept

Regression analysis

Why data analysis is more relevant for EnMS?

Because EnMS deals with Energy which is

measurable

Unlike other generic management systems; EnMS is unique due to one of its main requirements of continual improvement in energy performance (in addition to continual improvement in EnMS)

To show continual improvement in energy performance of organization, data analysis is required.

Analysis of data required for

Energy consumption based on measurement and other data (4.4.3a)

Identification of significant energy use areas (4.4.3b)

Identify relevant variables affecting significant energy use (4.4.3b)

Determine current energy performance (4.4.3b)

Estimate future energy consumption (4.4.3b)

Baseline establishment (4.4.4)

Analysis of data required for (Contd.)

Adjustment to baseline (4.4.4)

Identification of EnPIs (4.4.5)

Evaluation of actual versus expected energy consumption (4.6.1)

Review of energy performance and EnPIs (4.7.2c)

Extent to which energy objectives and targets are met (4.7.2e)

Why baseline adjustments?

Energy Baseline Definition

Quantitative reference(s) providing a basis for comparison of energy performance NOTE 1 - An energy baseline reflects a specified

period of time. NOTE 2 - An energy baseline can be

normalized using variables which affect energy use and/or consumption, e.g. production level, degree days (outdoor temperature), etc.

NOTE 3 - The energy baseline is also used for calculation of energy savings, as a reference before and after implementation of energy performance improvement actions.

Energy Baseline (clause no. 4.4.4)

Organization shall establish energy base line(s) in initial energy review

Decide baseline period

Changes in energy performance shall be measured against energy baseline(s)

Adjustments to the base line(s) shall be made in certain cases

Energy base line(s) shall be maintained and recorded

Energy Baseline (contd.)

The energy baseline is also used for calculation of energy savings as a reference before and after implementation of energy performance improvement actions

Savings = (Baseline Period Use – Reporting Period Use) ± Adjustments

A Notional Baseline

Why to make baseline adjustments?

Performance measurement requires an

“apples to apples” comparison.

We adjust baseline and reporting period energy use to the same set of conditions, for valid comparisons

Conditions keep on varying due to variables

Common Variables at Refineries Crude Intake of the refinery

Type of crude (high/low sulphur crude etc)

Secondary unit processing, unit load variations

Planned / Unplanned shutdown / start-up of units

Power failure frequency, Load shedding

Cold feed processing

Common Variables at LPG Bottling Plants

Bulk LPG Receipts through Pipeline

Bulk LPG Receipts through Wagons

Packed LPG Dispatch

Bottling of LPG

Bulk LPG Dispatch

Number of cylinders hydro tested

Quantity of water used

Availability and interruptions in grid supply

Why adjustments are required? - EnPIs no longer reflect organizational energy use

and consumption (and actual energy performance due to routinely changing variables), or

- There have been major changes (due to static factor which normally do not change) to the process, operational patterns or energy systems, or

- According to a predetermined method (e.g. if EnPI and/or baseline set by an organization require complying with legal requirements, the setting and the adjustment will follow its requirements)

Tools used for analysing the data

Graphs

Charts

Spreadsheets

Regression Analysis

Statistical models

CUSUM analysis

Data Analysis

There are an infinite number of valid ways to analyze any set of data.

Some are more appropriate than others.

Example Baseline Data

Let’s try

different

ways of

analyzing

these data.

Month Gas cons. In

units Days

Prod in tons

Jan 89000 30 220

Feb 83000 29 225

March 85000 31 215

April 79000 30 208

May 85000 31 250

June 105000 30 300

July 49000 30 23

Aug 60000 31 100

Sept 72000 30 190

Oct 85000 31 210

Nov 86000 30 221

Dec 75000 30 191

TOTAL 953000 363 2353

Analysis - Energy Per Day

Total annual gas use 953,000 units

Annual gas metering period 363 days

Energy Per Day = 953,000 / 363

= 2,625 units/day

Analysis - Energy Per Ton

Total annual gas use 953,000 units

Total annual production 2,353 Tons

So energy per ton = 953,000 / 2,353 (Specific Energy Consumption) = 405 units/ton

Energy/Day and Energy/ton may be overly simplistic.

Let’s look at a picture of the data.

Energy Per Ton - 1

-

20,000

40,000

60,000

80,000

100,000

120,000

- 50 100 150 200 250 300 350

Tonnes

Ga

s

Note: ‘baseload’ gas use is about 42,000 units

“A picture’s worth a thousand words!”

Energy Per Ton - 2

-

20,000

40,000

60,000

80,000

100,000

120,000

- 50 100 150 200 250 300 350

Tonnes

Ga

s

Find the real

production-sensitive

component look at

the high and low

points: At 23 Ton:

Gas use is 49,000

units At 300 Tons: Gas use

is 105,000 units

• So increasing Tons by 277 increases gas by 56,000 units

• Therefore Gas per Ton = 56,000 / 277 = 202 units/ton (the production sensitive element)

Energy Per Ton - 3

-

20,000

40,000

60,000

80,000

100,000

120,000

- 50 100 150 200 250 300 350

Tonnes

Gas

Now we can interpolate between points.

Draw a straight line through the data:

Energy Per Ton - 4

y = 194.55x + 41269

-

20,000

40,000

60,000

80,000

100,000

120,000

- 50 100 150 200 250 300 350

Tonnes

Ga

s

More precisely, using computer and regression analysis:

Energy Per Ton - 5

Performance Pattern:

Monthly Gas = (195 * Tonnes) + 41,269

This is a Mathematical Model of the baseline data

The “Flaw” of Averages

This mathematical model says the production-sensitive component is 195 units/ton (“marginal” gas per ton)

vs. The earlier simplified analysis (specific energy consumption) said the average gas is 405 units/ton.

The “Flaw” of Specific Energy Consumption

a. Specific energy consumption can be reduced even without taking any energy efficiency improvement measure simply by increasing production.

b. Even after making energy efficiency improvement, specific energy consumption may increase if production decreases in higher proportion as compared to reduction in energy consumption.

Example Base line data

Target of 5% reduction in SEC achieved through 10.6 % increase in prod

Month Gas cons. In

units Prod in tonnes SEC

Gas cons. In units

Prod in tonnes SEC

Jan 89000 220 404.5 88606.91 243.32 364.2

Feb 83000 225 368.9 89682.77 248.85 360.4

March 85000 215 395.3 87531.04 237.79 368.1

April 79000 208 379.8 86024.84 230.048 373.9

May 85000 250 340.0 95062.08 276.5 343.8

June 105000 300 350.0 105820.7 331.8 318.9

July 49000 23 2130.4 46217.96 25.438 1816.9

Aug 60000 100 600.0 62786.23 110.6 567.7

Sept 72000 190 378.9 82151.74 210.14 390.9

Oct 85000 210 404.8 86455.18 232.26 372.2

Nov 86000 221 389.1 88822.08 244.426 363.4

Dec 75000 191 392.7 82366.91 211.246 389.9

TOTAL 953000 2353 405.0 1001528 2602.418 384.8

CONCLUSION

The approximate production sensitive amount was 202 units/ton. As per regression analysis, it comes to 195 units/ton. SEC may not be the right parameter to find out energy efficiency improvement.

A marginal factor is usually more appropriate than an average factor for the adjustment. Unrecognized base load makes the difference. This is the “Flaw” of using simple averages.

Regression Analysis

Regression analysis is a mathematical technique that extracts parameters from a set of data to describe the correlation of measured independent variables and dependent variables (usually energy data).

Example of linear regression with one variable-

Y = ax+b

Where y is dependent variable (energy data)

X is independent variable

a & b are constants (regression coefficients)

How to deal with more variables ?

Apply established and proven effect of those variables on energy consumption and thus eliminating those. These could be obtained from handbooks, academic or trade or association journals, standards or typical operating procedures etc. However, the impact of these variables on energy consumption should be proven one and largely accepted by industries of that sector.

Use software applicable for multiple variables. Go for multiple variable regression analysis with

the help of a statistician.

Thanks!

Contact details M/S Energy Tech Consultants Pvt. Ltd.

A-204, Shaurya Apartments, Plot no. B-9/7B, Sector- 62

NOIDA – 201307 (UP) Phones: 09873827742, 0120-2401116/1117

E-mail: rkkhilnani@gmail.com Website: www.energytechconsultants.net