Fuzzy Rule-based Framework for Effective Control of Profitability in a Paper Recycling Plant
Keywords:
qualitative, computational, representation, possibility
Abstract
The rapid and constant growth of urban population has led to a dramatic increase in urban solid waste production, with a crucial socio-economic and environmental impact. As the demand for materials continues to grow and the supply of natural resources continues to dwindle, recycling of materials has become more important in order to ensure sustainability. Recycling is one of the best ways for citizens to make a direct impact on the environment. Recycling reduces greenhouse gas emissions that may lead to global warming. Recycling also conserves the natural resources on Earth like plants, animals, minerals, fresh air and fresh water. Recycling saves space in the landfills for future generations of people. A sustainable future requires a high degree of recycling. Recycling industries face serious economic problems that increase the cost of recycling. This highlights the need of applying fuzzy logic models as one of the best techniques for effective control of profitability in paper recycling production to ensure profit maximization despite varying cost of production upon which ultimately profit, in an industry depend. Fuzzy logic has emerged as a tool to deal with uncertain, imprecise, partial truth or qualitative decision-making problems to achieve robustness, tractability, and low cost. In order to achieve our objective, a study of a knowledge based system for effective control of profitability in paper recycling is carried out. The root sum square of drawing inference is found to be the most suitable technique to infer data from the rules developed. This resulted in the establishment of some degrees of influence on the output. To reinforce the proposed approach, we apply it to a case study performed on Paper recycling industry in Nigeria. A computer simulation using the Matlab/Simulink and its Fuzzy Logic Tool Box is designed to assist the experimental decision for the best control action. The obtained simulation and implementation results are investigated and discu
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Published
2010-07-15
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This work is licensed under a Creative Commons Attribution 4.0 International License.