Normalization Techniques & Forecasting Models for Textile Sector- Spinning

E-Cube Energy Trading Private Limited

#PAT #EPO #Textile #EnView #Benchmarking #Scorecard #EnPI

Introduction

In the PAT scheme each of the designated consumers has been given Specific Energy Consumption

“SEC” reduction targets, and not energy reduction targets! While people may argue that both

essentially mean the same, but in reality we have seen that there are a

lot of factors which can increase or decrease the SEC without having a

considerable effect on the net energy consumption.

Each industry has certain drivers/parameters that define the production

quality and in turn the production cycle and energy consumption

pattern. So for the same amount of production the parameters may be

different and hence the SEC.

Consider the example in the table given below. In case 1 & 2 product

types A & B have different energy consumption for the same production

quantity and In Case 1 & 3, product types A&C consume same energy

for different production quantity. In all the cases we have different

SEC, so how does a verifier account for the impact of energy saving

measures on the SEC?

Production in Tons

Product Type

Energy Consumption in MTOE

SEC in TOE/TON

Case 1 1 A 10 10 Case 2 1 B 12 12

Case 3 2 C 10 5

Table No.1

In this case study we take the case of Spinning Process (Textile Sector)

and look at identify a process in which variation in SEC could be

accounted to some of the parameters, which in turn will help identify

the effect of energy saving measures on the GTG SEC.

Verification& SEC Assessment is

going to be one of the biggest

challenges in the PAT Scheme.

DENA has to have access to

data, tools to be able to identify

and differentiate the impact of

energy saving measures on GTG

SEC as compared to the

baseline level.

DENA also has to ensure that a

DC does not get undue

advantage or disadvantage

owing to change in product mix

or raw material shift has

compared to the baseline level.

Need for Normalisation for Textile Sector (Spinning)

Factor Impact on GTG SEC How to normalize? Product Mix Energy consumption for each

product type varies, and hence a change in product mix can impact the GTG SEC either positively or negatively.

Statistical Analysis, Forecasting Model based on the data for the compliance cycle can help us “forecast” the impact of product mix and hence normalize the actual SEC wrt to Product Mix.

Count Finer count consumes more energy as compared to coarser count material. The conversion of avg count of a DC to 40s count based on Sitra’s conversion factors makes the impact of change of count even more significant. For example conversion of 10s count to 40s count is not the same as converting 20s count to 40s count

Statistical analysis can help us identify the impact of count variation and hence normalize the change in SEC wrt to count change. It is important to measure the change in SEC in respect of the actual production and not in respect of the normalized production.

TFO and Doubling A lot of units have doubled yarn as major product which consumes more energy than Single Yarn. Also a lot of units do doubling on Job work or for other group units, so the electricity consumed for doubling is essentially not accounted for their own production.

Most units do not have separate metering for TFOs and Doubling, either a specific Metering Requirement has to be specified or we have to rely on statistical analysis that can help us identify the impact of count variation and hence normalize the change in SEC wrt to count change. Either it would be required to count the doubling production as part of the overall production, or credit for the electricity consumed for doubling is to be provided while calculating the GTG SEC.

Need for Normalisation for Textile Sector (Spinning)

Factor Impact on GTG SEC How to normalize? Energy Efficiency Measures

Any measureable energy efficiency project reduces GTG SEC (should be visible). However because of the process variation in the textile sector, it is not always visible.

It is important to provide M&V protocols clearly outlining the energy consumption and production scenario pre and post implementation of all energy efficiency projects. This is very important, as while formulating the forecasting model, we would have to deduct the expected energy saving for target and grace period based on the M&V data maintained by the plant.

Quality Enhancement Equipment

Quality of similar material may be different and can vary as per customers demand. Additional energy consumed does not change the production but the GTG SEC.

Units should have separate meters installed for all the Quality Enhancement Equipments, so that the credit for the same could be provided at the time of verification.

Power Mix Based on the source of the electricity, energy consumption in terms of MTOE would vary.

It is proposed to take the ratio of electricity consumption as in baseline, and convert the electricity consumption for each of the target years in the same ratio. This will effectively normalize any change in the power mix.

Automisation Automisation can either reduce or increase energy consumption. If Automisation is done in process, it would be difficult to consider the impact of the same on GTG SEC.

Should not be considered for Normalization

Import & Export of Intermediary Products

Intermediary products do get counted as the major production and hence if the energy consumption is considered for GTG SEC calculations, it will increase the GTG SEC.

Statistical Analysis, Forecasting Model based on the data for the compliance cycle can help us “forecast” the impact of Intermediary Products and hence normalize the actual SEC wrt to Intermediary Products.

Data Maintenance Production and Energy consumption data sets are crucial in analyzing the energy consumption trend.

It is imperative the units be asked to maintain daily production and energy consumption data, which can be utilized for statistical analysis.

Case Study of a Spinning Unit- Use of Forecasting Models

Here we consider a spinning unit which has undertaken multiple energy saving measures, however the GTG SEC calculations show that the SEC for one of the target year has increased in comparison to the baseline year. In this case study we will see how access to data and its analysis can help us identify the impact of various factors on the GTG SEC.

1. Factors applicable for the unit under consideration for the case study:

- Product Mix - Count Variation - Power Mix - Count Normalization to 40s count using SITRA’s Method

2. Key Facts- Before and After Normalization!1

- Normalised GTG SEC shows a decrease of 29% without normalisation for power mix.

- After normalisation for Power Mix and based on 40s converted production (As per SITRA

document) GTG SEC shows an increase by 14% whereas GTG SEC on normalized production

basis shows a reduction of SEC by 16%----(A)

- Actual Production has increased by 93% since baseline year as compared to an increase of

84% in the normalised production2. Electricity consumption in the target year increased by

93% as compared to the baseline year.

- On statistical analysis it is found that the three product lines A, B and C have the following

UKG Index 3.8, 3.2 and 2.4 respectively. Actual Production (Average Count) for product type

A has reduced by 20%, whereas production for product type C has increased by 23%,

resulting in lowering of the overall UKG Index by 8%.

- At 40s conversion index of 0.5, the actual UKG is expected to reduce by 16%. ----(B)

- At 95% confidence level, count variation does not seem to be factor for change in UKG;

however the change in normalised production is less than the change in actual production,

causing the normalised SEC to increase in comparison to the baseline situation.3

1 As per the statistical analysis carried out by Team EETPL

2 Normalized as per SITRA conversion factor provided by BEE

3 Normalization factor as provided in the SITRA document are not continuous and hence for slight variation of

count, the factors change considerably.

- If the increase in normalised production is considered to be the same as the increase in

actual production, the GTG SEC shows an increase of 1% over baseline SEC.---(C)

- As per the M&V records for the energy saving measures implemented during the target

year, expected reduction in UKG was 0.5%---(D)

- Normalised change in SEC in account of inefficiency=C+D=1.5%

How can EnView help?

We have seen how statistical analysis can help identify the correct impact of energy saving measure

(or other parameters) on energy consumption and SEC. However to be able to develop a model

which is reliable and full proof we need access to

- High Quality Data which has to be maintained via Faceless Information Management System

- Minimum of 500 sets of data to account for any other variation.

- Daily energy consumption details based on Key Performance Indicators

EnView provides users access to processed energy and production data. Each set of data whether

entered for the first time or modified have a time stamp and can easily be verified by auditors or

DENA

Some of the other features of EnView are:-

Note

This case study is an independent analysis of E-Cube Energy Trading Pvt Ltd and has suggestions on how to normalize factors that can influence the GTG SEC calculations and is just for reference and understanding of the broader issues. Final application of such methodology and tools would depend on the M&V protocol developed and prescribed by relevant authorities.