Assessment of the impact of a phosphatic fertilizer plant on the adjacent environment using fuzzy logic

The main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied.

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“…The fuzzy objective function (1) described in [19][20][21][22][23][24] appeared to be one of the best ways for the fuzzification of data matrix. …”

Section: Fuzzy Principal Component Analysis-first Component (Fpca-1)mentioning

confidence: 99%

“…Practically, the computation of the other fuzzy components is reduced to the computation of the first fuzzy component of a smaller-sized matrix. For a complete description of this robust PCA algorithm, two recent successful applications might be helpful [23,24].…”

Section: Fuzzy Principal Component Analysisorthogonal (Fpca-o)mentioning

confidence: 99%

Rime samples characterization and comparison using classical and fuzzy principal components analysis

et al. 2008

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“…These losses are most important for nitrogen, and cause ground water and soil pollution with nitrates, nitrosamines, or other harmful compounds [9,10]. These appear in foods, affecting health [11,12].…”

Section: Introductionmentioning

confidence: 99%

Synthesis Optimization and Characterization of Microencapsulated N-P-K Slow-Release Fertilizers

et al. 2015

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Slow-release microencapsulated N, P, and K fertilizer synthesis was optimized and the products exhaustively characterized. Four NPK formulations with nutrient mass ratios of 1:0:0, 1:1:0, 1:1:1, and 2:1:1 were characterized following European Council Regulation 2003/2003 for fertilizers as well as ICP-AES, 1 H-NMR, FT-IR, HPLC, elemental analysis, XPS, SEM, dynamic light scattering, thermogravimetry, differential thermogravimetry, activity index, and leaching rate. These fertilizers have good slow-release properties and decrease waste and crop contaminants, improving environmental protection. Compared to classic granulated fertilizers, plant nutrient availability was improved allowing reduced application. Nutrient leaching parameters describing four different kinetic models were evaluated.

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“…Enea and Salemi [8] developed an EIA procedure based on the extension principle by using parameters defined by means of fuzzy numbers, which was applied to assess the environmental impact of an incineration plant. De Siqueira and De Mello [9] developed a decision-making method to assess environmental impacts by means of fuzzy logic, which was applied to compare different options of a high-speed rail project in Santa Catalina (Brazil). Szczepaniak et al [10] assessed the environmental impact of a phosphatic fertilizer plant by means of fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Modeling Heterogeneous Experts’ Preference Ratings for Environmental Impact Assessment through a Fuzzy Decision Making System

Vrána

Aly

2011

IFIP Advances in Information and Communication Technology

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Currently, there is an increasing demand for more efficient and practical environmental impact assessment (EIA) tools due to the emerging climate change challenges and need to better evaluate and control impacts of industrial technologies and activities. However, due to the inherent uncertainties, vagueness's of assessment data, traditional EIA methods are unable to handle efficiently and properly such decision making process, and consequently more efficient method resorts to the opinions of group of relevant experts in order to enhance the reliability of the assessment decision. However, experts' assessments are usually in heterogeneous forms, multi-metric or multi-criterion and usually conflicting. This article presents a fuzzy decision making systems (FDMS) that enables heterogeneous experts' preference ratings assessment and provides for aggregation of those opinions over multi-metric scales. Experts can provide their opinion in form of crisp, linguistic or fuzzy values.

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Assessment of the impact of a phosphatic fertilizer plant on the adjacent environment using fuzzy logic

Cited by 10 publications

References 14 publications

Rime samples characterization and comparison using classical and fuzzy principal components analysis

Rime samples characterization and comparison using classical and fuzzy principal components analysis

Synthesis Optimization and Characterization of Microencapsulated N-P-K Slow-Release Fertilizers

Modeling Heterogeneous Experts’ Preference Ratings for Environmental Impact Assessment through a Fuzzy Decision Making System

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