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

PRESENTATION LAYOUT

Cleaner ProductionAgro-based IndustriesAssessment MethodologyResults & Conclusions

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.

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.

Input-output Flow of an Agro-processing Industry

CLEANER PRODUCTION ASSESSMENT

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

Contributing factors to cleaner production Resource consumption pattern, Process efficiency, Wastes and emissions generated, Managing wastes and by

products, Attitude and Awareness

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.

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.

CASHEW PROCESSING AS AN EXAMPLEThe 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.

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.

CONTRIBUTING FACTORS CONSIDERED FOR ASSESSMENT OF DEGREE OF CLEANER PRODUCTION

METHODOLOGY

Data from 22 cashew

industries

Determine attribute values

Define membership

function

Design control algorithm

using fuzzy rules

Fuzzification of crisp data

FISMamdani

Max-Min Inference

DefuzzificationCentroid method

RESULT

MODEL DEVELOPED FOR THE ASSESSMENT OF DEGREE OF CLEANER PRODUCTION IN THE

CURRENT STUDY.

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.

Unit No.

ProcessCriteria Attributes

EnvironmentalCriteria Attributes

Sustainability Criteria Attributes

RCEC

MJ/kgWC

Ltrs/KgWWGLtrs/kg

GWPtons of CO2 eq.

SWD OREGC

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 0.230 10.56 0.670 0.570 0.0178 0.010 0.000 0.040 0.930

17 0.220 10.34 3.340 2.340 0.0023 0.010 0.240 0.060 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

Measured Value of Attributes

Unit No.PROCESSCRITERIA

SUSTAINAIBILITY CRITERIA

ENIRONMENTAL CRITERIA OVERALL CLEANER PRODUCTION

LEVEL

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

Crisp Values of Criteria and Overall Cleaner Production Level

Control surface plot between Process Efficiency and Environmental burden with Overall CP level

FIS for aggregation of Sustainability

CONCLUSIONSEven 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.

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[4] ibid.

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Thank You