Scholarly article on topic 'Assessment of Cleaner Production Level in Agro based Industries – A Fuzzy Logic Approach'

Assessment of Cleaner Production Level in Agro based Industries – A Fuzzy Logic Approach Academic research paper on "Agriculture, forestry, and fisheries"

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Abstract of research paper on Agriculture, forestry, and fisheries, author of scientific article — K.M. Basappaji, N. Nagesha

Abstract Cleaner Production (CP) is a preventive environmental strategy and across the globe there are plenty of success stories of its initiatives which have not successfully transcended to realize the fruits. In this context, there is a need to promote CP initiative to help the industry, society and environment. Hence, present level of CP is to be assessed which facilitates exploring the potential for further CP. This underscores the need for developing a methodology to estimate the CP level. Generalizing the evaluation of assessment of CP is difficult due to heterogeneous nature of operations in industries. The application of fuzzy logic assists in addressing this complexity by offering a methodology that can be adapted to any type of organization by identifying the appropriate variables. This paper presents a model developed to assess the CP status and implementation of this model on 22 cashew processing units. In the assessment of CP level, contributing parameters viz., process efficiency, environmental degradation, and sustainability aspects are considered. Various dimensions of each contributing parameter is identified and measured in the overall estimation of CP level. The result of the study has useful policy implications for catalyzing the CP initiatives.

Academic research paper on topic "Assessment of Cleaner Production Level in Agro based Industries – A Fuzzy Logic Approach"

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Energy Procedía 54 (2014) 127 - 134

4th International Conference on Advances in Energy Research 2013, ICAER 2013

Assessment of cleaner production level in agro based industries - a

fuzzy logic approach

Basappaji K Ma*, N Nageshab

a Mechanical Engineering, J.N.N. College of Engineering, Shimoga, India b Industrial & Production Engineering, University BDT College of Engineering, Davangere, India

Abstract

Cleaner Production (CP) is a preventive environmental strategy and across the globe there are plenty of success stories of its initiatives which have not successfully transcended to realize the fruits. In this context, there is a need to promote CP initiative to help the industry, society and environment. Hence, present level of CP is to be assessed which facilitates exploring the potential for further CP. This underscores the need for developing a methodology to estimate the CP level. Generalizing the evaluation of assessment of CP is difficult due to heterogeneous nature of operations in industries. The application of fuzzy logic assists in addressing this complexity by offering a methodology that can be adapted to any type of organization by identifying the appropriate variables. This paper presents a model developed to assess the CP status and implementation of this model on 22 cashew processing units. In the assessment of CP level, contributing parameters viz., process efficiency, environmental degradation, and sustainability aspects are considered. Various dimensions of each contributing parameter is identified and measured in the overall estimation of CP level. The result of the study has useful policy implications for catalyzing the CP initiatives.

© 2014K.M. Basappaji.Published by ElsevierLtd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/3.0/).

Selection and peer-review under responsibility of Organizing Committee of ICAER 2013 Keywords: Cleaner production; Sustainability; Fuzzy logic; Agro-based industries; cashew processing.

1. Introduction

According to United Nations Environment Programme (UNEP), Cleaner Production (CP) is an integrated preventive strategy applied to processes and products in order to increase efficiency and reduce risks to human beings and environment by continuously taking actions to prevent pollution in every activity relating to

* Corresponding author. Tel.: +91-8182-98861-39971 E-mail address: bas_km@yahoo.co.in

1876-6102 © 2014 K.M. Basappaji. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Selection and peer-review under responsibility of Organizing Committee of ICAER 2013 doi: 10.1016/j.egypro.2014.07.255

processes, products and services. It is achieved through reducing the use of resources, judicial use of energy, reducing emission and wastes, recycling, etc. CP itself is not a solution to all environmental problems, but it reduces the dependence on end-of-pipe solutions, generates less harmful wastes. Products that are designed and produced with CP in mind are often less harmful for consumers to use, and their residuals are normally less of a burden to waste streams. Apart from the environmental benefits, CP practice also offers the organization with financial gains. CP strategies are assuming significance in most industries including the agro based industries.

Agro based industries are those that add value to agricultural raw materials through their processing into marketable, usable or edible products, while enhancing the income and profitability of the producers [1]. Generally the agro based industries are micro, small and medium sized enterprises (MSMEs) and regarded as an extended arm of agriculture. They constitutes an important and vital part of the manufacturing sector in developing countries as they alleviate rural poverty on the one hand and may earn foreign exchange through the export of agricultural and agro-industrial products on the other [2]. Pollution problems in agro-based industry are caused mainly due to the energy consumption; wastewater and solid wastes generated during their manufacturing processes and air pollution problems are caused by combustion processes.

Assessment of CP enables to recognise and examine the CP opportunities which can make possible their implementation in industries. The assessment consists of identification of sources of wastes and emissions through an audit to be conducted on resource consumption; impact on environment. Further, comparing with best performing unit provides an opportunity to understand the shortcomings and get new ideas to improve and perform better.

The contribution of the CP initiatives is to ensure efficient resource utilization, less generation of wastes and pollutants. This would be possible in several ways such as, product modification, technology or process modification, improving awareness and developing positive attitude and support from government and R&D institutions. To assess the CP status and potential, the above aspects are required to be characterised quantitatively and this is a complex task. The evaluation of parameters involved in CP can be expressed linguistically based on experience and knowledge of entrepreneurs of such industries. But, these linguistic expressions require the translation into a meaningful quantity. Fuzzy logic is a useful approach to deal with such situations. 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. In the current paper an attempt is made to evaluate CP status of a cashew processing unit using fuzzy logic approach.

2. Literature Review

In the present era, the thrust is shifting towards environmental friendly technologies for sustainable industrial production. CP is a strategy towards achieving sustainable production, reducing wastes and emissions at the source and is distinct from end-of-pipe technologies. Despite the advantages of CP, there are some barriers that are active making its worldwide implementation difficult. Major barriers to the implementation of CP are lack of knowledge of the appropriate technology, lack of technical expertise in the methodology of process assessment and diagnosis, lack of financial resources, lack of awareness, government policies and regulations, incentives for investment in end-of-pipe technologies and resistance to new ideas [4]. A critical step towards implementing CP is environmental impact assessments (EIA), which requires an evaluation of process operation and management in a systematic way. This results in identifying the specific needs for improving operational efficiency and waste reduction. CP assessment enables identification and evaluation of CP opportunities. Howgrave-graham et al, suggested generic semiquantitative proxy indicators for estimating the level of CP assessment in small to medium-sized enterprises on the basis of awareness; management support; innovations and operational improvements [5]. An analytical tool which can be used to provide detailed information on the overall environmental impact of a business was developed by integrating parts of tools such as life cycle assessment (LCA), multi-criteria analysis and environmental performance indicators [6] which combine complementary aspects of these three tools. A quantitative environmental impact assessment scheme [7] for CP technologies is presented in a methodology by Fijal. This is based on material and energy flows and uses a set of profile indices, including raw material, energy, waste, product and packaging, and the related indices are used as a basis for determining an integrated index for overall environmental assessment of CP technologies. Though EIA were developed and improved significantly, they fail to bring the small industrial

pollution problems under their structure, since they encourage reliance on end of pipe treatment, which is financially encumbering than the CP approaches. A study [8] in China contends that CP assessments are to be fully incorporated into EIA so that the enterprises may derive economic benefits from its adoption. Fuzzy logic approach often coupled with analytic hierarchy process (AHP) is also used as evaluation tools. A case study [9] on artificial intelligence (IA) based CP evaluation system for surface treatment facilities use a fuzzy logic operator based evaluation system that outputs the CP status of the company. To reflect the CP condition of an enterprise, a fuzzy-soft comprehensive evaluation model is built to effectively evaluate CP for aviation enterprise quantitatively and accurately [10]. The analysis of drivers to green manufacturing is carried out with fuzzy approach [11] in which the drivers are compared over one another and concludes with the priority among common drivers. An evaluation model [12] based on the fuzzy AHP and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, was developed to help the performance evaluation in a fuzzy environment. Here, the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers to help decision analysts to better understand the evaluation process and provide a more accurate, effective, and systematic decision support tool. A benchmarking method using two-stage super-efficiency data envelopment analysis (DEA) for coal-fired power plants is proposed [13] to improve the CP performance of DEA-inefficient or weakly DEA efficient plants and then to select the benchmark from performance improved power plants. Thus, based on the literature available it appears that fuzzy logic application in CP assessment can be effectively employed to produce useful dividends.

3. Agro based industries

Agro based industries are characterized by the use of agricultural production as their inputs mainly for processing and to convert it to edible or non-edible products. Agro processing is now regarded as the emerging sector of the Indian economy in view of its large potential for growth and socio economic impact, specifically on employment and income generation. Despite this, it remains rudimentary, lacks institutional, technical and financial support. Majority of the industries are run as family business without much expertise and knowledge of latest developments. In India, only about 3% of total workforce is engaged in agro processing as against about 14% in the developed countries revealing its underdeveloped state and untapped potential for being a major player at the global level [14].

Fig. 1. Input-Output flow of an agro-processing industry

Even though the pollution load from individual unit is relatively low, the magnitude of pollution problem in the cluster of such units is significantly high. To be competitive, the agro based industries need to be operated at high efficiency levels which requires a holistic approach to improve the present situation. Typical input and output flow for an agro based industry is shown in Fig.1. Many of the agro processing are energy intensive and require plenty of water for processing and generate large amounts of solid, liquid and gaseous wastes. The wastes generated are organic matter with water and air pollution seemingly more serious problems than the solid waste. Solid wastes can either be recycled or used for making by-product. Thus, the CP in agro based industries should encompass energy and water conservation, efficiency in raw material processing leading to less waste generation and increased productivity, onsite recycling and further usage of wastes as resources.

4. Problem formulation

Often, MSMEs are technically unsophisticated and do not have ready access to the process data. Measuring the degree of cleaner production in such enterprise requires a special approach, as contributing factors are multiple in natures. Attempt to quantify them lead to oversimplification and loses the significance of one or the other factor. Even though the quantitative values of attributes are available, it cannot directly be used for assessment, because individual attributes vary independently and thus, their contribution can only be captured by the fuzzy logic approach. For this reason fuzzy logic was conceived as a better method for sorting and handling data since it mimics human control logic, as exact values of these data are usually not critical.

In the present work, to evaluate the CP status of an agro based industrial cluster, cashew processing units are considered. The various processing steps involved in chronological order of a cashew processing unit are: sun drying of freshly harvested raw seed for storage, steam cooking or roasting, shelling, kernel drying, cooling and humidifying, peeling, grading and packaging. Among the above processes, steam cooking and kernel drying are energy intensive, mainly consuming thermal energy. Shelling and peeling are labour intensive processes but gradually getting mechanized these days due to lack of availability of labour.

CP assessment is a systematic approach to identify and evaluate CP opportunities. In the assessment, data about production process has to be collected to evaluate environmental performance and production efficiency of the unit. CP thrusts on using fewer resources and generate less pollution apart from being sustainable. The contributing factors considered for the assessment of degree of CP are: evaluation of process efficiency, environmental burden it causes, and sustainability of the process. The process efficiency is evaluated by knowing the raw material conversion efficiency, quantity of energy consumed, and the amount of water consumed. The burden on environment caused by the production activity is assessed by waste water generation, emission caused by combustion and solid waste disposed. The sustainability is measured by onsite recyclability, dependence on renewable energy and employment generation capability.

5. Methodology

To illustrate the fuzzy logic approach to assess the degree of CP, the data from 22 cashew processing units located at Mangalore in the coastal region of Karnataka, India, are collected through a researcher administered structured questionnaire. With fuzzy logic, the first step is to understand and characterize the system behavior by using one's knowledge and experience. Then, design the control algorithm using fuzzy rules which describe the principles of the controller's regulation. Finally, if the results are not satisfactory, some fuzzy rules need to be modified and rechecked. The fig. 2 depicts the model developed in the current study for the assessment of degree of CP.

To carry out the fuzzy analysis, fuzzy logic toolbox functions built on the MATLAB numeric computing environment is used. The fuzzy logic toolbox allows creating and editing fuzzy inference systems. Fuzzy inference is mapping from an input to an output using fuzzy logic which is a source to make decisions. In the current work, the Mamdani's max-min inference method is used to model human expert knowledge which is most commonly used fuzzy inference system. The fuzzy inference system comprises membership functions, fuzzy logic operators, and if-then rules. The membership function represents the degree of truth and a triangular membership functions are

assigned in this work. The membership range to the input and output variables are determined through the values of the attributes by considering the best performing and unpleasant cases. The membership functions assigned converts the crisp data to a fuzzy data. The decision the fuzzy inference system makes is derived from the set of rules framed.

Fig. 2. Cleaner Production (CP) Assessment Model for Agro Based Industry

Fuzzy operators are the subjects and verbs of fuzzy logic to frame if-then rules. Finally, the fuzzy output is defuzzified to get the crisp output of process, environmental and sustainable criteria. Centroid method of defuzzification is selected, because the values lie approximately in the centre and poses less ambiguity.

6. Results

To allow aggregation and to facilitate fuzzy computations, the values of attributes are derived from the data collected from the cashew processing units. The measured values of the attributes of three criteria are given in the table 1. The fuzzification module transforms these values into a linguistic variable assigned as low, medium and high in order to make it compatible with the rule base. The measured values for attributes are assigned with a linguistic variable. The criterion for allocating the linguistic value is based on the measured value. If CP level is to be high, the resource conversion efficiency (RC), onsite recyclability (OR), renewable energy use (REU) and employment generation capability (EGC) has to be more and the remaining attributes have to be low. Thus they are accordingly given the linguistic variable as low, medium and high based on the measured value. Table 2 presents the norms for assigning attribute values to linguistic variables. A linguistic value is represented by a fuzzy set using triangular membership function that associates with each minimum and maximum value from the range of values of each attribute. The rules are framed by treating all the attributes as equal contributors to CP realization.

In the first stage, fuzzy inference system returns crisp values for the three aforementioned criteria based on the attributes. Output values of the first stage; crisp values of the three criteria considered for the fuzzy analysis are now being aggregated into a single value to represent overall CP level of the processing units. The five linguistic variables defined (along with assigned values) are; very poor (0-33.4), poor (16.7-50.0), average (33.4-66.7), good

Table 1. Measured Value of Attributes

Process Environmental Sustainability

Criteria Attributes Criteria Attributes Criteria Attributes

EC WC WWG GWP EGC

RC MJ/kg Ltrs/Kg Ltrs/kg tons of CO2 eq. SWD OR 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

Table 2. Values assigned to linguistic variables

Goal Criteria Attribute Measured Values Attribute Assigned Linguistic Variable

Min Max Low Medium High

Process Criteria RC 0.22 0.80 0.0- 0.4 0.2 - 0.6 0.4 - 0.8

EC 1.36 11.0 5.5-11.0 2.75-8.25 0.0 - 5.5

Overall Cleaner Production (CP) Level WC 0.17 3.50 1.75-3.5 0.87-2.62 0.0 ■1.75

Environmental Criteria WWG GWP SWD 0.11 0.0012 0.010 3.50 0.02 0.40 1.75-3.5 0.01-0.02 0.2-0.4 0.87-2.62 0.0-1.75 0.005-0.015 0.0-0.01 0.1-0.3 0.0-0.20

Sustainability OR REU 0.0 0.030 0.40 1.00 0.0-0.2 0.0-0.5 0.1-0.3 0.25-0.75 0.2-0.40 0.5-1.0

EGC 0.320 0.10 0.0-0.05 0.025-0.075 0.05-0.10

(50.0-83.4) and excellent (66.7-100). Figure 3 illustrates the fuzzy membership function for the output overall CP index and the estimated values of the three criteria and the overall CP values are given in table 3.

0 10 20 30 40 50 60 70 80 90 100

output variable "CPLevel"

Fig. 3. Fuzzy membership function for the output overall cleaner production level

Table 3. Crisp Values of Criteria and Overall Cleaner Production Level

Unit No. Process Criteria Sustainability Criteria Environmental 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

7. Conclusions

Assessment of CP level is a prerequisite for understanding and further infusing this strategy in an organization. The use of fuzzy logic provides a simple but robust approach for the quantification of degree of CP of the considered industry. This helps the industries to assess their status of CP involving various criteria; and can identify areas where they lack and compare with the best performers to facilitate further improvement. The results reveal that despite some industries performing better on a few criteria, their overall performance comprising the entire gamut of aspects is not that encouraging. The present work underscores importance of the awareness of CP level in improving the industrial activity to achieve financial, environmental and social sustainability. The outcome of this study has useful implications in fine tuning policies of promoting CP in agro-based industries.

References

[1] FAO Regional Office for Asia and the Pacific, "Policies and strategies for agro-industries in the Asia-Pacific region",, RAP Bulletin, 1993.

[2] ADB, "Key Indicators Education for Global Participation", 2003, Manila.

[3] United Nations Industrial Development Organization, ID/WG.544/l, NGO Forum on Cleaner Industrial Production,, Vienna, Austria, October 1995

[4] ibid.

[5] A. Howgrave-graham and R. Van Berkel, "Assessment of cleaner production uptake : method development and trial with small businesses in Western Australia", Journal of Cleaner Production, 15, 2007, 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, 2006, pp. 1-10.

[7] Fijal T. (2006) "An environmental assessment method for cleaner production technologies", doi:10.1016/j.jclepro.2005.11.019.

[8] Chen W, Warren K A," Incorporating cleaner production analysis into environmental assessment", Environ Impact Assess REV, 19, 1999, pp. 457-476.

[9] Telukdarie A., Brouckaert, Yinlun Haung, "A case study on Artificial intelligence based cleaner production evaluation system for surface treatment facilities", Journal of Cleaner production, 14, 2006, pp. 1622-1634.

[10] Peng W, Li C. (2012) "Fuzzy-Soft Set in the Field of Cleaner Production Evaluation for Aviation Industry", Communications in Information Science and Management Engineering, Vol. 2 Issue. 12, Dec 2012, pp. 39-43

[11] Govindan K. A, Shankar M, "Evaluation of Essential Drivers of Green Manufacturing Using Fuzzy Approach, integrating cleaner production into sustainability strategies", in 4th International workshop on Cleaner Production, May 22nd to 24th 2013, Sao Paulo,

Brazil.

[12] Chia-Chi Sun, "Expert Systems with Applications A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods". Expert Systems with Applications, 37(12), 2010, pp. 7745-7754.

[13] Shao-lun Zeng and Yu-long Ren, "Benchmarking Cleaner Production Performance of Coal-fired Power Plants Using Two-stage Super- efficiency Data Envelopment Analysis", World Academy oof Science, Engineering and Technology, 42, 2010, pp. 13731379.

[14] Kachru R P, "Agro-Processing Industries in India - Growth, Status and Prospects, status of farm mechanization in India", Indian Council of Agricultural Research, New Delhi, 2002, pp. 114-126.