1. ASSESSMENT OF CLEANER PRODUCTION
LEVEL IN
AGRO BASED INDUSTRIES – A FUZZY LOGIC
APPROACH
Basappaji K.M.
Mechanical Engineering,
J.N.N. College of Engineering, Shimoga, India
Dr. N. Nagesha
Industrial & Production Engineering,
University BDT College of Engineering, Davangere, India

2. PRESENTATION LAYOUT
 Cleaner
Production
 Agro-based Industries
 Assessment
 Methodology
 Results & Conclusions

3. CLEANER PRODUCTION
Integrated preventive strategy applied to processes and
products in order to increase efficiency and reduce
risks to human beings and the environment by
continuously taking actions to prevent pollution in
every activity relating to processes, products and
services.
It is achieved through
Input modification
Product modification
Process modification
resource conservation,
energy conservation,
minimization of emission and
recycling.

4. AGRO-BASED INDUSTRIES
Add value to agricultural raw materials through their
processing into marketable, usable or edible products.
Regarded as an extended arm of agriculture.
Generally the agro based industries are micro, small and
medium sized enterprises (MSMEs).
Technically unsophisticated.
Accurate Process data difficult to obtain.
Pollution load from individual agro-processing unit is
relatively low.

5. Input-output Flow of an Agro-processing Industry

6. ASSESSMENT
Contributing factors to cleaner production
Resource consumption pattern,
Process efficiency,
Wastes and emissions generated,
Managing wastes and by products,
Attitude and Awareness
Consists
Input-Output audit, process study, waste and emission
audit.
& Explore
Reduction in resource consumption and waste generation
by better practices/technology
Facilitates
Identification and evaluation of opportunities to effect
their implementation.
Benchmarking

7. Attempt to quantify them lead to
oversimplification and loses the significance of
one or the other factor.
As individual attributes vary independently and
essentially their contribution is to be captured.
Fuzzy logic approach may be a better method as it
mimics human control logic, as exact values of
these data are usually not critical.

8. FUZZY APPROACH
Fuzzy logic was developed by Dr. Lotfi Zadeh of the
University of California at Berkeley in the 1960s.
Zadeh reasoned that people do not require precise,
numerical information input, but they provide a simple
way to arrive at a definite conclusion based upon
vague, ambiguous, imprecise, noisy, or missing input
information.
The evaluation of involved parameters in cleaner
production can be expressed linguistically based on
experience and knowledge of entrepreneurs of such
industries.

9. CASHEW PROCESSING AS AN
EXAMPLE
The various processing steps involved are:
sun drying of freshly harvested raw seed
steam cooking or roasting,
shelling,
kernel drying,
cooling and humidifying,
peeling,
grading and packaging.

10. TRIBUTING FACTORS CONSIDERED FOR
SSMENT OF DEGREE OF CLEANER PRODUC
evaluation of process efficiency -- the raw material
conversion efficiency, quantity of energy consumed, and
the amount of water consumed.
environmental burden it causes -- waste water generation,
emission caused by combustion and onsite recyclability
of wastes generated
sustainability of the process -- onsite recyclability,
dependence
on renewable energy and employment generation
capability.

11. METHODOLOGY
Data from 22
cashew
industries
Determine
attribute values
Define
membership
function
Design control
algorithm using
fuzzy rules
Fuzzification of
crisp data
FIS
Mamdani
Max-Min Inference
RESULT
Defuzzification
Centroid method

12. MODEL DEVELOPED FOR THE ASSESSMENT OF
DEGREE OF CLEANER PRODUCTION IN THE
CURRENT STUDY.

13. 
The measured values of the attributes of three criteria are
transformed into a linguistic variable assigned as low, medium
and high

The rules are framed by treating all the attributes as equal
contributors to cleaner production realization.

In the first stage, fuzzy inference system returns crisp values
for the three aforementioned criteria.

Output values of the first stage; crisp values of the three
criteria

Output values of the second stage; aggregate criteria values
into a single value to represent overall cleaner production
level.

The five linguistic variables defined are; very poor, poor,
average, good, very good and excellent.

14. Measured Value of Attributes
Process
Criteria Attributes
Unit No.
Environmental
Criteria Attributes
Sustainability
Criteria Attributes
RC
EC
MJ/kg
WC
Ltrs/Kg
WWG
Ltrs/kg
GWP
tons of CO2
eq.
SWD
OR
EGC
Labor/kg
REU
1
0.270
5.280
0.490
0.370
0.0178
0.010
0.110
0.050
0.740
2
0.240
2.750
3.000
2.500
0.0049
0.010
0.100
0.050
0.480
3
0.230
1.510
0.900
0.810
0.0026
0.010
0.030
0.050
0.320
4
0.750
2.840
1.000
0.750
0.0071
0.010
0.150
0.040
0.670
5
0.300
3.550
0.250
0.200
0.0115
0.010
0.130
0.030
0.780
6
0.300
2.810
1.000
0.900
0.0071
0.010
0.100
0.060
0.530
7
0.290
2.340
0.340
0.170
0.0085
0.010
0.120
0.030
0.650
8
0.250
3.020
1.000
0.940
0.0053
0.010
0.150
0.040
0.630
9
0.240
3.130
0.340
0.170
0.0057
0.010
0.160
0.050
0.650
10
0.240
6.360
1.340
0.270
0.0035
0.010
0.390
0.060
0.800
11
0.260
2.670
0.220
0.150
0.0053
0.010
0.160
0.030
0.770
12
0.260
3.420
2.230
2.060
0.0049
0.010
0.100
0.030
0.760
13
0.300
3.100
0.800
0.670
0.0035
0.010
0.200
0.030
0.820
14
0.240
1.360
3.340
3.170
0.0013
0.040
0.030
0.040
0.330
15
0.250
2.010
0.750
0.730
0.0044
0.010
0.000
0.040
0.460
16
17
0.230
0.220
10.56
10.34
0.670
3.340
0.570
2.340
0.0178
0.0023
0.010
0.010
0.000
0.240
0.040
0.060
0.930
0.690
18
0.280
1.820
0.170
0.110
0.0142
0.010
0.000
0.040
0.540
19
0.220
6.190
0.800
0.540
0.0071
0.010
0.310
0.080
0.660
20
0.290
3.450
0.790
0.470
0.0012
0.010
0.160
0.070
0.610
21
0.250
1.910
0.190
0.160
0.0195
0.010
0.090
0.040
0.590
22
0.300
4.240
0.840
0.750
0.0026
0.010
0.180
0.090
0.560

15. Crisp Values of Criteria and Overall Cleaner Production Level
Unit No.
PROCESS
CRITERIA
SUSTAINAIBILITY
CRITERIA
ENIRONMENTAL
CRITERIA
1
48.3
42.2
55.0
47.7
2
40.0
38.2
62.5
40.8
3
47.9
32.3
87.5
45.9
4
80.3
41.7
79.9
64.3
5
55.0
44.1
62.5
54.4
6
55.0
43.0
77.2
53.9
7
55.0
37.6
79.9
51.7
8
50.1
40.8
74.8
49.0
9
51.5
48.6
79.9
55.1
10
38.4
79.9
87.5
62.7
11
55.0
44.3
81.6
54.6
12
40.8
40.0
62.5
41.2
13
55.0
44.1
87.5
54.4
14
40.0
25.0
55.0
33.6
15
49.6
87.5
50.0
54.5
16
40.0
55.0
50.0
48.6
17
15.3
70.7
61.9
47.2
18
55.0
50.0
62.5
59.7
19
42.4
81.5
79.9
67.7
20
54.2
55.3
87.5
66.8
21
55.0
33.6
55.0
50.4
22
54.7
55.7
87.5
67.4
OVERALL CLEANER PRODUCTION
LEVEL

16. FIS for aggregation of Sustainability
Control surface plot between Process Efficiency and Environmental burden with Overall CP level

17. CONCLUSIONS
Even though industries performing better in any criteria will
not reflect the overall.
The use of fuzzy logic provides a simple but robust approach
for the quantification of degree of cleaner production of the
considered industry.
This helps in status assessment and can visualize areas
where improvement is required.
The present work underscores importance of the awareness of
cleaner production level in improving the industrial
activity to achieve financial, environmental and social
sustainability.
The outcome of this study is useful in fine tuning policies of
promoting cleaner production in agro-based industries.

18. REFERENCES
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19. Thank You