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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.
REFERENCES[1] FAO Regional Office for Asia and the Pacific. (1993) Policies and strategies for agro-industries in the Asia-Pacific region,
RAP Bulletin, 23.
[2] ADB, Key Indicators (2003) Education for Global Participation, Manila, 2003.
[3] United Nations Industrial Development Organization (30 October 1995), NGO Forum on Cleaner Industrial Production ID/WG.544/l, Vienna, Austria.
[4] ibid.
[5] A. Howgrave-graham and R. Van Berkel. (2007) Assessment of cleaner production uptake : method development and trial with small businesses in Western Australia, Journal of Cleaner Production, 15, pp.787-797.
[6] B. G. Hermann, C. Kroeze, and W. Jawjit. (2006) Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators, Journal of Cleaner Production, xx, pp. 1-10.
[7] Fijal T. (2006) An environmental assessment method for cleaner production technologies. doi:10.1016/ijclepro.2005.11.019.
[8] Chen W, Warren K A.(1999) Incorporating cleaner production analysis into environmental assessment, Environ Impact Assess REV, 19, pp. 457–476.
[9] Telukdarie A., Brouckaert, Yinlun Haung. (2006) A case study on Artificial intelligence based cleaner production evaluation system for surface treatment facilities, Journal of Cleaner production, 14, pp. 1622-1634.
[10] Peng W, Li C. (2012) Fuzzy-Soft Set in the Field of Cleaner Production Evaluation for Aviation Industry. 2:39–43. Communications in Information Science and Management Engineering, Dec. 2012, Vol. 2 Issue. 12, pp. 39-43
[11] Govindan K. A, Shankar M. (2013) Evaluation of Essential Drivers of Green Manufacturing Using Fuzzy Approach, integrating cleaner production into sustainability strategies, 4th International workshop on Cleaner Production, São Paulo, Brazil.
[12] Chia-Chi Sun. (2010) Expert Systems with Applications A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), pp. 7745–7754.
[13] Shao-lun Zeng and Yu-long Ren. (2010) Benchmarking Cleaner Production Performance of Coal-fired Power Plants Using Two-stage Super-efficiency Data Envelopment Analysis, World Academy of Science, Engineering and Technology, 42, pp. 1373–1379.
[14] Kachru R P. (2002) Agro-Processing Industries in India - Growth, Status and Prospects, status of farm mechanization in India, Indian Council of Agricultural Research, New Delhi, pp. 114-126.
Thank You