87 basappaji

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87 basappaji

  1. 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. 2. PRESENTATION LAYOUT  Cleaner Production  Agro-based Industries  Assessment  Methodology  Results & Conclusions
  3. 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. 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. 5. Input-output Flow of an Agro-processing Industry
  6. 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. 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. 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. 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. 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. 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. 12. MODEL DEVELOPED FOR THE ASSESSMENT OF DEGREE OF CLEANER PRODUCTION IN THE CURRENT STUDY.
  13. 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. 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. 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. 16. FIS for aggregation of Sustainability Control surface plot between Process Efficiency and Environmental burden with Overall CP level
  17. 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. 18. 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.
  19. 19. Thank You

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