ASSESSMENT OF CLEANER PRODUCTION
AGRO BASED INDUSTRIES – A FUZZY LOGIC
J.N.N. College of Engineering, Shimoga, India
Dr. N. Nagesha
Industrial & Production Engineering,
University BDT College of Engineering, Davangere, India
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
It is achieved through
minimization of emission and
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).
Accurate Process data difficult to obtain.
Pollution load from individual agro-processing unit is
Input-output Flow of an Agro-processing Industry
Contributing factors to cleaner production
Resource consumption pattern,
Wastes and emissions generated,
Managing wastes and by products,
Attitude and Awareness
Input-Output audit, process study, waste and emission
Reduction in resource consumption and waste generation
by better practices/technology
Identification and evaluation of opportunities to effect
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 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
The evaluation of involved parameters in cleaner
production can be expressed linguistically based on
experience and knowledge of entrepreneurs of such
CASHEW PROCESSING AS AN
The various processing steps involved are:
sun drying of freshly harvested raw seed
steam cooking or roasting,
cooling and humidifying,
grading and packaging.
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,
on renewable energy and employment generation
Data from 22
MODEL DEVELOPED FOR THE ASSESSMENT OF
DEGREE OF CLEANER PRODUCTION IN THE
The measured values of the attributes of three criteria are
transformed into a linguistic variable assigned as low, medium
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
Output values of the second stage; aggregate criteria values
into a single value to represent overall cleaner production
The five linguistic variables defined are; very poor, poor,
average, good, very good and excellent.
FIS for aggregation of Sustainability
Control surface plot between Process Efficiency and Environmental burden with Overall CP level
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
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
The outcome of this study is useful in fine tuning policies of
promoting cleaner production in agro-based industries.
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