CHAPTER 5

ANALYSIS OF ENERGY CONSUMPTION AND ENVIRONMENTAL IMPACT

5.1 INTRODUCTION

The obstacles in improving environmental performance of the MSME sector are mainly

the lack of knowledge and information concerning environmental issues. MSMEs have a

perception that they have little or no impact on the environment due to their smaller scale

of operation. Any pro-environmental attempt by these organizations are hindered due to

lack of; financial support, expertise, and technology, etc. In such situations CP seems to

be suitable approach to contain the environmental damage occurring due to activities of

MSMEs. CP aims at reducing the negative environmental impacts in the production

process by advocating the efficient use of resources, thus helping industries to perform

economically better apart fi-om improving their environmental performance. Agro-based

industries are resource intensive industries in which the energy requirement, among

others, constitutes the major portion of input resources. The prime environmental impact

is associated with air pollution due to energy consumption. In the previous chapter,

production processes followed in the considered three agro-based industry clusters were

presented. It was understood that energy requirement in these sectors consists three types

of energy forms viz., electrical, labour and thermal energy. Study of energy consumption

pattern and associated environmental impacts facilitate finding disparities existing among

the enterprises within the cluster. Subsequently, estimation of energy conservation

potential and also associated reduction in environmental degradation can be estimated.

5.2 ENERGY AND ENVIRONMENT IN MSMEs - A CAPSULE OF

LITERATURE

It is widely recognized fact that MSMEs play a significant role in the economic

development of a nation. However, they also exert considerable pressure on the

environment, if not at the individual level but collectively at the cluster level. Many

researchers have demonstrated that this problem can be dealt with approaches that are

specifically relevant to the concerned region and the industry. But, the common goal is to

first understand the situation and then device methods to reduce the resource

consumption.

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Johannes (2004) observes the core of CP implementation lies in identifying the flows of

mass and energy in a company for evaluating the efficiency of the use of materials, water

and energy. MSMEs have potential to create jobs and because they can be dynamic

vehicles for irmovation, must be given easy access to available advanced energy systems.

In this context, Scarpellini and Romeo (1999) opined that renewable energies represent

the most adequate resoiirce to MSMEs assuring continuity in energy supply without

dependence on fluctuating energy market. According to Viswanathan and Kumar (1999)

one of the issues associated with CP implementation is to significantly reduce GHG

emission and increase energy usage efficiencies. Ramachandra (1998) studied the rural

energy utilisation in Kamataka and found that energy is a vital input to rural industries

including agro-processing industries. It was suggested in the study that decentralised way

of meeting the energy requirements of industries would be the most appropriate way of

handling the energy situation in a region.

Study of energy consumption pattern and their associated environmental burden are

cornerstones for effective implementation of CP. Many studies have been conducted in

the agro-based food industries. Kaiman and Boie (2002) studied the implications of

energy management in German bakeries. They found that it was possible to reduce energy

consumption by about 6.5% of the then prevailing energy consumption level. They also

estimated Specific Energy Consumption (SEC) of 1.37 kWh per kg of processed flour

(4.93 MJ/kg). Bamgboye and Jekayinfa (2006a) have carried out energy consumption

pattern in palm kernel oil processing. They considered thermal, electrical and manual

energy consumption and brought out the total energy consumed for the operation. Further,

the energy use of different operations in the production was also estimated. Jekayinfa and

Bamgboye (2006b) also estimated the energy consumption in cashew processing in

Nigeria and estimated that the total energy intensity in the cashew nut mills varied fi-om

0.21 to 1.161 MJ/kg. Different studies on energy requirement for rice processing revealed

that there was no agreement on the energy requirement. This is attributable to the fact that

there is considerable variation amongst different mills in terms of paddy quality, type of

milling, and parboiling methods adopted. In a study EC - Asean Cogeneration program

(1998) in Thailand, white rice-mill processing required 30 kWh/ton (108 MJ/ton), while

parboiled rice milling required up to about 60 kWh/ton (216 MJ/ton). The other study

conducted by Kapur et al. (1999) in India, reported parboiling as the most energy

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intensive process consuming about 241 to 425 MJ/tonne of paddy and milling required

79.2 to 164 MJ/tonne of paddy.

Atul et al. (2011) undertook a study on sources of environmental pollution from cashew

processing. According to the study, even though the pollutant emission from single unit

is low, collective emissions load of all the units in the cluster causes considerable

environmental degradation. The cashew nut processing by cooking i.e., steam roasting

process is relatively less pollution intensive. It is a better alternative process for roasting

and hence may be adopted to reduce the environmental impact. Natarajan, et al. (1998)

considered rice husk generated as a by-product during rice milling process and used

this as a renewable energy source in the production process. It was found that the husk

was less polluting due to its low sulphur and heavy metal content. Mamasaray et al.

(1999) concluded that converting rice husks into heat, steam, gas or liquid fuel would

benefit countries that have limited or no conventional energy resources. Promoting the

use of rice husk by the energy sector would curb local environmental problems, such as

rice husk dumping, open burning and can mitigate Green House Gas (GHG) emissions.

In this literature backdrop it is very clear that the energy use analysis followed by

environmental impact assessment not only helps in creating a sound case for

implementation of CP, but also assists in improving the economic performance of

MSMEs.

5.3 ENERGY CONSUMPTION PATTERN IN AGRO-BASED INDUSTRIES

Energy consumption pattern of the three agro-based industry clusters selected in this study

are presented in the following sections. In this energy analysis, thermal, electrical and

manual energies are considered. A researcher administered structured questionnaire was

used in all the clusters to fetch the details of the type of energy carrier, and quantum of

energy used. Using the primary data collected from the MSME units in each of the three

clusters, SEC, and total energy used are determined and projection of energy requirement

for the whole cluster was made.

5,3.1 Bakery Cluster (Shimoga)

In bakeries the thermal energy is mainly required for baking operation. Thermal energy in

the cluster was used to be obtained conventionally by burning the wood. However, with

the development of electrical ovens, bakeries shifted to use electricity for thermal energy.

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Later with the availability of Liquefied Petroleum Gas (LPG), LPG ovens were also used

which is considered to be one of the clean fuel available for this sector. Owing to cost

aspects, bakery operators once again started shifting from LPG fired ovens to diesel fired

ovens. At present diesel cost is considered as the lowest with 3-4 years of pay back period

for installing new diesel ovens. Apart from thermal energy, bakeries require electrical

energy as well to operate various bakery equipments and for front-shop operation. Manual

energy is the other energy source considered in the CP assessment of this industry. Bakery

cluster when compared to other agro-based industrial clusters in this study, does not use

any sort of biomass these days.

Table 5.1 provides 'energy consumption pattern' in bakeries based on the sampled units in

the cluster. The number of bakeries in the cluster is not exactly known as there are no

authentic data available. In consultation with the bakery operators, it was approximated at

around 175 units processing about 4500 tons of flour annually out of which about 1000

ton of flour is processed in the 40 sampled units. A total energy of 6.93 TJ in the sampled

units and 30.08 TJ in the entire cluster are consumed annually.

Table 5,1: Average Annual Energy Requirement in Bakeries

Type of Energy Fossil Fuel

Electricity

Manual

Total

Total Annual Energy 5.25

1.55

0.134

6.93

Projected Annual 22.78

6.70

0.60

30.08

% Share of 75.7

22.3

2.0 100

Fossil Fuel • Electricity IVIanual

2%

Figure 5.1: Energy Consumption Pattern in Bakery Industry

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5.3.2 Cashew Processing Cluster (Dakshina Kannada and Udupi)

Cashew processing requires energy in different forms. This industry is labour intensive

and requires skilled labourers for shelling and peeling. Women workers are best suited for

these operations. Other processes like raw nut drying, grading and packing may employ

male workers. Apart from the labour energy, it essentially requires thermal energy for

roasting and drying processes. Thermal energy is derived mainly from the cashew cake

which is a by-product of this industry. Fire-wood is also used as thermal energy source in

those locations where it is available easily. In few instances, electrical energy is also used

for drying purposes. However, electrical energy is mainly required to operate shelling

machines, peeling machines, and packing machines. Fossil fuel (petrol/diesel) is

occasionally used during power failures to operate machines/generate power.

Table 5.2: Average Annual Energy Requirement for Cashew Processing

Type of energy carrier

Biomass

Electricity

Fossil Fuel

Manual

Total

Total Annual Energy Used in

Sampled MSMEs TJ

100.19

9.53

13.17

40.26

163.15

Projected Annual Energy Required the Cluster TJ

341.34

32.50

44.8

137.15

555.79

• Biomass • Electricity

8% J

6%

^ M

% share of total energy

61.41

5.84

8.06

24.60

100

Fossil Fuel « Manual

• 1 ^ ^ ^ ^ ^

Figure 5.2: Energy Consumption Pattern in Cashew Processing Industry

Total number of cashew processing units in the cluster is about 160, which processes 1.5

Million tons of raw cashew nuts annually out of which 44,000 tons of cashew is estimated

to be processed annually in the 40 sampled units. Total annual consumption of energy is

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162.9 TJ in the sampled units and it amounts to 555.6 TJ in the cluster. Table 5.2 provides

the estimation of average annual consumption of energy in surveyed units and its

projection to the entire cluster.

Thermal energy constitutes about 61.4% and labour energy accounts for 24.6% in the

total energy consumed. The labour energy and electricity have a share of 5.84% and

8.06% as shown in figure 5.2. The average annual energy use per enterprise in the cashew

cluster is 4.00 TJ and the SEC is 3.73 MJ per Kg of raw cashew nut processed.

5.3.3 Rice MiU Cluster (Gangavati)

Rice processing is highly energy intensive requiring thermal and electrical energy for its

processing. In the present research work only par boiled rice processing is considered

because it is having high pollution intensity. Par boiling is an optional processing that

requires steam and hot water derived from burning rice husk generated from rice milling

process. After parboiling, the paddy is milled in a series of machinery which operate using

electrical energy. To obtain high quality cleaned rice, milling in modem rice mills are

done with latest machineries which are energy intensive thus increasing the energy

demand of rice-mills.

Table 5.3: Average Annual Energy Requirement for Rice Mills

Type of energy carrier

Biomass Electricity Fossil Fuel

Manual

Total

Total Annual Energy Used in

Sampled MSMEs TJ

1024.93 104.70

13.99 0.83

1144.45

Projected Annual Energy Required the

Cluster TJ

3711.50 361.51 48.31 2.88

4124.22

% share of total energy

89.5 % 9.14% 1.22%

0.072 %

100 %

In the present work total 40 units were sampled randomly out of 145 processing units in

the MSME cluster under study. The total annual quantity of paddy processed in the

cluster was about 11 Million tons out of which 3.2 Million tons was estimated to be

processed annually in the sampled units. A total of 1144.45 TJ of energy in the sampled

units and 4124.22 TJ in the cluster are consumed annually. Table 5.3 provides the

estimation of average annual consumption of energy in the sampled units and its

projection to the entire cluster.

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Thermal energy requirement is predominent in rice milling constituting almost 90% of the

total energy. Requirement of electrical energy to operate machines is about 9%, with

fossil fuel accoumting for about 1.25%. The remaining feeble share goes to manual

energy as shown in figure 5.3. The average annual energy required per enterprise in this

cluster is estimated at 25.5 TJ while the SEC is around 3.75 MJ per kg of paddy processed

in this industry.

• Biomass " Electricity

1%

^ 9%

Fossil Fuel

0%

Manual

.^-2980

Figure 5.3: Energy Consumption Pattern in Rice-Milling Industry

5.4 ENVIRONMENTAL IMPACT OF AGRO-BASED INDUSTRIAL CLUSTERS

The measurement of environmental implications provides an insight that allows

organisations to focus on achieving the most meaningful reductions. In agro-based

industries consumption of energy, water and generation of waste is the source for

environmental pollution. Water is being used in the agro-based industries for different

purposes. In bakeries, it is used as an ingredient and also for cleaning purposes. In the

cashew processing industries it is used for steam generation and for cleaning purposes.

The rice mills use water for steam generation and for soaking purpose in parboiled rice

processing. The quantity of water required in bakeries and cashew processing may be

controlled by adopting conservation measures and are not of much concern from an

environmental perspective. Rice mills use huge amount of water to process paddy without

the use of chemicals. It contains organic matter which when discharged repeatedly in huge

quantities may cause stagnation and putrefies causing pollution.

Kuvempu University Library Jnana Satiyadri, Shankaraghatta

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Other soiirce of pollution is through generation of solid wastes. In bakeries, the solid

waste generated is from spill overs and leftovers. In cashew processing and rice mill

industries, the solid wastes are cashew nut shell and rice husk respectively along with the

ash generated from burning bio-mass. Cashew nut shell and rice husk have now found

altemative uses. Considering the above facts and localized nature of pollution, the

pollution caused by water use and solid wastes generated are not serious and hence not

included for a detailed study in the present research work. The major environmental

impact in agro-based industries is the air pollution caused due to energy consumption in

different forms. Thus estimation of air pollution, which not only has the local effect but

can have the global impact as well, is carried out in a greater detail in this research work.

5.4.1 Emission Estimation Procedure

Air pollution due to energy use is expressed in terms of generation of Green House Gases

(GHGs) and other pollutants. For stationery combustion, they include Carbon Dioxide

(CO2), and five major non-COa GHGs: Methane (CH4), Nifrous Oxide (N2O), Carbon

Monoxide (CO), and Nitrogen Oxides (NOx), and Non-Methane Volatile Organic

Compounds (NMVOC). The ftiels also contain sulphur and emit Sulphur Dioxide (S02),

which is not a GHG. In this study major GHGs are considered and their emissions due to

fiiel combustion are estimated using Intergovernmental Panel on Climate Change (IPCC)

guidelines. Estimation of CO2 emission is relatively simple and more accurate due to the

fact that factors used are the ftinction of ftiel properties.

The emission of each GHG from stationery sources are calculated by adopting the

procedure outlined in IPCC (2006) Guidelines for National Greenhouse Gas Inventories.

The procedure involves determining the fuel consumption in mass units first and

converting it into energy content of the fuel to be expressed in Tera Joules. Next, the

energy consumption thus calculated is multiplied with the default emission factor for each

GHG of the fuel to get the emission quantity. Basically, carbon emissions are classified

into Direct and Indirect emissions. The Greenhouse Gas Protocol (GHG Protocol) defines;

• Scope 1: Direct GHG emissions

Direct GHG emissions are emissions from sources that are owned or controlled by the

reporting entity. For example, emissions from combustion in owned or controlled boilers,

fumaces, vehicles, etc.

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• Scope 2: Indirect GHG emissions

These are emissions that are a consequence of the activities of the reporting entity, but

occur at sources owned or controlled by another entity, i.e. emissions due to purchased

electricity, heat or steam.

• Scope 3: Other indirect emissions

Other activities are included here such as the extraction and production of purchased

materials and fuels, transport related activities in vehicles that are not owned or controlled

by the reporting entity, electricity-related activities like transmission and distribution

losses not covered in Scope 2, outsourced activities, waste disposal, etc.

Table 5.4 provides the emission factors used in the estimation of GHGs in the three

MSME clusters for various energy carriers. Since emission factor for rice husk and

cashew cake are not directly available in the IPCC guidelines, a common emission factor

given for the biomass is used.

SI. No.

1

2

3

4

5

Table 5.4: Emission Factors (Source: IPCC, 2006)

Name of the

Pollutant CO2

CH4

N2O

CO

NOx

Emission Factor for Diesel

Kgsof C02/tonne

3171.48

0.128

0.025

0.65

8.67

Emission Factor for LPG

KgsofCOi/ Tonne 3098.72

0.046

0.0046

0.26

6.93

Emission Factor for Biomass

1890

0.441

0.0588

75.60

1.89

Emission Factor for

Wood 1646.4

0.441

0.0588

29.40

1.470

5.4.2 Environmental Impact assessment in the Sampled Clusters

This section provides the air pollution caused by the selected agro-based MSME clusters

due to energy consumption. Various GHGs are estimated in all the three clusters and it is

based on the computation of average GHGs emitted from the sampled units in the

respective clusters and its annual projection for the entire cluster. The emission intensities

are specified for one ton of raw material processed. Table 5.5 illustrate the GHG

emissions calculations for bakery cluster. Pollution intensity caused by this sector is

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highest when compared to other two sectors due to its dependence on fossil fuel;

conversely the total pollution caused by the cluster is lowest owing to annual processing

quantity.

Table 5.5: GHG Emissions - Bakery Cluster

Scope 1 Emissions due to fuel consumption

Emissions in kg per ton of wheat flour processed CO2 380.6

CH4 1

Scope 2 CO2 emissions due to purchased electricity is 354.6 kg/ton of wheat flour processed Projected Annual GHG Emission of the Cluster in tons of GHG

2153.24

D.OlO N2O

0.00173 CO

0.053 NOx 0.938

Global Warming Potential (GWP) rScoDe 1 & 2)

735.94 kg C02eq.

0.058 0.010 0.312 5.36

Cashew processing is relatively less polluting among the considered clusters. In table 5.6

air emissions calculations for the cashew processing cluster. This cluster depends on the

biomass as energy source for its thermal energy requirement.

Table 5.6: GHG Emissions - Cashew Processing Cluster

Scope 1 Emissions due to fuel consumption

Emissions in kg per ton of raw cashew nut processed CO2

249.68 CH4

0.0554

Scope 2 CO2 emissions due to purchased electricity is 57.23 kg/ton of raw cashew nut processed Projected Annual GHG Emission of the Cluster in tons of GHG

37452

Glol

8.31

N2O 0.00746

CO 8.269

NOx 0.282

>al Warming Potential (GWP) (ScoDe 1 & 2)

310.37 kg CO2 eq.

1.12 1240.35 42.3

Table 5.7: GHG Emissions

Scope 1 Emissions due to fuel consumption

- Rice MiU Cluster

Emissions in kg per ton of paddy processed CO2 221.5

CH4 0.110

Scope 2 CO2 emissions due to purchased electricity is 87.10 kg/ton of paddy processed Projected Annual GHG Emission of the Cluster in tons of GHG

243650

N2O 0.0147

CO 3.51

Global Warming Potentia (Scope 1 & 2)

315.46 kg of C02eq

121 16.17 3861

NOx 0.578

(GWP)

635.8

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As rice mill cluster also depends on biomass energy, the air pollution intensity for rice

mill cluster is similar to cashew processing cluster. Owing to huge quantity of paddy

processed in this cluster, annual GHG emission of the cluster is highest as illustrated in

table 5.7.

5.5 SUMMARY

The study of quantity of energy consumed and energy consumption pattern revealed that

the thermal energy is predominant in the agro-based industries. Further, a wide variation

in the energy consumption is observed amongst the MSME units in a product cluster as

observed in Figure 5.4.

• SEC • Coefficient of Variation

Bakery Rice Mill Cashew

Figure 5.4: Variation in SEC amongst the Three Product Clusters.

This could be attributed to difference amongst units in a cluster in terms of lack of

knowledge, expertise, and financial ability to adopt effective energy management

techniques. Another reason might be that they operate on short term profit goal, and do

not give attention to conserve energy which may yield results on long term basis.

The results of the study reveal that there is wide disparity in energy consumption amongst

the three MSME clusters. The environmental pollution associated with energy

consumption is also estimated in terms of GWP (Global Warming Potential) using IPCC

guidelines. The difference in SEC values is understandable because the products are

different despite they come under agro-based food industry classification. The production

processes are quite different and so also is the energy requirement thus resulting in

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different SEC values for each of the clusters. Further, different operating conditions

prevailing in the three geographical locations, the attitude of entrepreneurs and labourers,

quality of energy carriers used, technological aspects, etc., have also contributed to this

variation.

Looking at Table 5.8, it may be observed that SEC values and emissions of cashew

processing and rice mills are comparable as biomass constitutes major energy source.

Bakery cluster deviates fi-om other two and proves to be highest energy consumer and also

has high GWP per unit of raw material processed.

Table 5.8: Estimated SEC and GWP in the Three Agro-Based Industries

Agro-Based Cluster

Bakery

Cashew Processing

Rice Mills

Energy Carrier

Fossil Fuel Electricity

Manual Biomass

Electricity Fossil Fuel

Manual Biomass

Electricity Fossil Fuel

Manual

Specific Energy Consumption

MJ/kg 5.110 1.450 0.140 2.280 0.230 0.310 0.910 3.375 0.328 0.044 0.003

SEC of the Product MJ/kg

6.7

3.73

3.75

GWP gms of

CO2 eq. /kg

735.94

310.37

315.46

With the understanding of the energy consumption pattern, energy efficiency (SEC) and

its variation amongst the three product clusters, the next chapter looks at the assessment of

CP in the three MSME clusters.

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