Fuzzy-based wastewater quality indices for pollution classification: a case study in the United Arab Emirates

Kizhisseri, Mohamed I.; Mohamed, Mohamed M.

doi:10.1007/s10669-015-9579-9

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Let us add that modern research focused on processing fuzzy information uses the following route: "raw datafuzzificationanalysisdefuzzification -result". At the same time, they formulate variables, including linguistic ones, and, using conventional methods of fuzzing terms, go to FS and further, as for example, in [23] analyze the resulting data model in a mathematical package [24]. Also fuzzy components are explored using intelligent systems.…”

Section: Layered Pcmsmentioning

confidence: 99%

Analysis of Fuzzy Data for the Selection of Ingredients of the Polymer Composition by Technical Functions in Quality Management

Гермашев¹,

Mashihina²,

Derbisher³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The task of fuzzy mathematical modeling of the composition of a polymer composition is singled out and formulated, as one of the types of chemical-technological systems for creating the theoretical foundations for managing the physicochemical properties of the material produced from this composition. Fuzzy data on the technical functions and concentration of individual ingredients that included in the composition in the form of fuzzy numbers are used as the initial formalized information at modeling the polymer composition. As a result, a parametric space is formed that describes the multicomponent system under study. The index of compliance of each parameter of the polymer composition with the requirements for the physicochemical properties and quality of the polymer material is calculated. This allows you to go to the relative dimensionless real values. Weighted voting procedure is used as an aggregate function. The obtained numerical value reflects the integral measure of the compliance of the chemical-technological system with the requirements for the polymer composition. An example of the choice of ingredients of the polymer composition by calculating using fuzzy numbers is presented. In addition to the formulation of the problem, the study develops the mathematical software for the intellectual system supporting the research and production activities of the chemical technologist to substantiate the decision to choose a polymer composition.

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Section: Layered Pcmsmentioning

confidence: 99%

Analysis of Fuzzy Data for the Selection of Ingredients of the Polymer Composition by Technical Functions in Quality Management

Гермашев¹,

Mashihina²,

Derbisher³

et al. 2019

Adv. sci. technol. eng. syst. j.

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“…There is a need for a simple output from the set of complex input variables. Kizhisseri and Mohamed (2016) applied successfully the fuzzy logic concept-based wastewater quality indices for pollution classification. The objective of present study is to characterize the KWW samples to know the hidden characteristics.…”

Section: Introductionmentioning

confidence: 99%

Assessment of kitchen wastewater quality for irrigation

Parwin

Paul

2020

Appl Water Sci

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In this study, the potential reuse of kitchen wastewater (KWW) in irrigation was analyzed to reduce the present freshwater demand. To know the suitability of KWW for irrigation, the samples were first collected from an educational institute in India and then characterized according to its physical, chemical, and bacteriological properties. The characterized data were then compared with the standard limit for irrigation Food and Agriculture Organization (FAO in Water quality for agriculture. Irrigation and drainage paper 29, M56, 1994) and the US Salinity Laboratory (USSL). Apart from the above irrigation standards, the characterized data were also compared with sodium adsorption ratio (SAR), residual sodium carbonate, sodium percentage (Na%), magnesium hazard (MH), Kelly’s ratio (KR), and permeability index to get better clarity. From the characterization, it was found that carbonate, fluoride, chromium, and Escherichia coli were absent, whereas parameters like pH, chloride, iron, copper, magnesium, lead, nickel, sodium, calcium, zinc, aluminum, and sodium adsorption ratio were within the permissible limit. The result obtained from the USSL classification system suggested that 30.77% of KWW samples are safe for irrigation. Moreover, its quality was found to be safe for irrigation based on SAR, Na%, KR, and MH. For better decision making of KWW reuse in irrigation, the output of Mamdani fuzzy inference system (MFIS) was compared with the USSL classification system. The overall agreement between USSL and MFIS was found to be 55.6% for KWW.

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“…Earlier in 1983, Bhargava et al, defined water quality index and then after various indices were established all over the world to determine the overall water quality within a particular area. National Sanitation Foundation's Water Quality Index (NSFWQI), Canadian Council of Ministers of Environment Water Quality Index (CCMEWQI), British Columbia Water Quality Index and Oregon Water Quality Index are some of the examples, Said et al, 3 Abhishek et al, 4 and Ibrahim et al 5 A multi-parameter comprehensive index was calculated for wastewater (WWCI) and sludge feedstock (SCI) by considering nine influent and eight feedstock characteristics respectively. 6 The biogas prediction approaches were reviewed which primarily based on characterization which is extensively laborious and time consuming.…”

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confidence: 99%

Nomograms as a graphical model to predict biogas at municipal wastewater treatment plant using fuzzy indices

Anaokar¹,

Khambete

Christian

2022

Env Prog and Sustain Energy

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The strength of municipal wastewater and sludge considering their potential for biogasification can be expressed in terms of multiple parameter comprehensive indices (MPCI), namely, Wastewater Comprehensive Index (WWCI) and Sludge Comprehensive Index (SCI), respectively. Both of these indices can be calculated using fuzzy multiple criteria decisions making (FMCDM). Simple linear equations are established to express the relation between these indices, suspended solids concentration and biogas. The linear equation is developed to predict biogas from multiple parameter comprehensive indices, which indicate the linear relation between strength of wastewater, sludge and biogas. Nomogram was developed to get immediate quantification of biogas by linear scaling of suspended solid concentration and multiple parameter comprehensive indices (MPCI). Using a linear equation obtained from regression analysis; a suitable scale was developed to construct the nomogram. The biogas was found 96% precise with reference to measured one when estimated using nomograms. A single parameter, that is, suspended solids concentration will be monitored at municipal wastewater treatment plant, by digital device; to save chemicals, time, power and human resource. While taking efforts toward smart cities; digitalization and resource conservation will be creditable.

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Fuzzy-based wastewater quality indices for pollution classification: a case study in the United Arab Emirates

Cited by 7 publications

References 21 publications

Analysis of Fuzzy Data for the Selection of Ingredients of the Polymer Composition by Technical Functions in Quality Management

Analysis of Fuzzy Data for the Selection of Ingredients of the Polymer Composition by Technical Functions in Quality Management

Assessment of kitchen wastewater quality for irrigation

Nomograms as a graphical model to predict biogas at municipal wastewater treatment plant using fuzzy indices

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