Identify and Prioritize Water Pollutants in Petroleum and Petrochemical Industries and Employing a Mathematical Model to Reduce Environmental Risk Emphasis
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Abstract: (404 Views) |
Water contamination is one of the important problems and challenges facing the world, including Iran. It is one of the main causes of death and mortality in the world. Surface and ground waters are both exposed to various contaminants. The contaminants have been classified into various categories, based on their nature, the source of contamination, and whether they are man-made or occur naturally. The main organic contaminants of water are agricultural, chemical, oil, and food contaminants. Each of these four groups has sub-criteria. In this research, investigation, identification, and accurate ranking of water contaminants in oil and petrochemical industries have been done using the factor analysis and analytical hierarchy process methods. The results of the factor analysis method suggest that the contaminants nitrate, sodium, and TDS have claimed the highest score from environmental experts as water contaminating agents. Further, the results of the ANP showed that agricultural and oil contaminants, with weights of 0.321 and 0.152, have been ranked first and fourth, respectively. In this research, following an investigation and ranking of water contaminants in oil and petrochemical industries, using factor analysis and ANP methods, a mathematical model was presented and then solved using goal programming with the aim of predicting and extracting the main contaminants and their levels in the oil and petrochemical industry and decreasing the environmental contaminants. The researchers hope that the presentation of the findings to relevant organizations, an effective step is taken to prevent environmental destruction by the contaminants of this industry. |
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Keywords: Water Contaminants, Analytical Network Process, Principal Components Analysis, Factor Analysis, Oil and Petrochemical Industry, Mathematical Modelling |
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Type of Study: Research |
Subject:
Special Accepted: 2017/12/12 | Published: 2017/12/12
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