A Proposed Fuzzy Logic Model for Waste Water Treatment Analysis

Srivastava, Kanishk; Handa, Chirag; Madhulika,

doi:10.1007/978-981-19-3089-8_39

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…Like other MLS that is binary true or false, the current methods use the degree of truth that can be accurate and considered a subset of the multiple-valued logic. The present analysis has high precision and accuracy and is widely employed in the water industry (Srivastava and Handa, 2022). The functional models (membership) used in the water industries are shown in Fig.…”

Section: Fuzzy Analysismentioning

confidence: 99%

“…During recent neurology studies, the brain discovered an electrical network of neurons for conducting pulses (Srivastava and Handa, 2022;Hameed et al, 2021). Alan Turing's computational theories revealed that any form of computation could be expressed digitally.…”

Section: Machine Learning For the Prediction Of The Wastewater Qualitymentioning

confidence: 99%

“…The term fuzzy represents ambiguity; in the current world, for real -time applications to face situations using the senses, it is impossible to tell whether a decision is true or false. In these situations, fuzzy logic provides a suitable solution with high probability and greater flexibility for good results (Srivastava and Handa, 2022). Thus, in recent considered MLS to avoid the uncertainties that might occur during accounting in any situation.…”

Section: Machine Learning For the Prediction Of The Wastewater Qualitymentioning

confidence: 99%

“…3). However, these methods have certain limitations and can't be considered accurate for treating water and wastewater (Srivastava and Handa, 2022).…”

Section: Anlnmentioning

confidence: 99%

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A proposed Fuzzy logic model for Electrospun nanofiber pressurised membranes for water treatment- A review

Rao¹,

Kumari²,

Karnena

2023

JANS

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Electrospun nanofiber membranes and nanocomposites for water treatment is an emerging concept growing faster in science, gaining more prominence among various scientists, and creating attention to improve water quality. The familiarity with fabricating these materials is increased, and researchers worldwide target more nanomaterial manufacturing and synthesis for several applications. The electrospinning process for nanofibers' preparation allows polymers to incorporate various functionalized materials. Scientists have recently proposed electrospun membranes using fluorinated compounds and polymers with hydrocarbons. The membranes prepared by the electrospun nanofiber to purify water created a primary research axis, and various laboratory experiments proved efficient. In recent years, the usage of fuzzy analysis in industries has increased, and these logics are applied in every unit process of the water industry. The present review primarily discusses the recent pressurized electrospun membrane technologies utilized in the water industries and machine learning methods that can be applied in the water industries. Further, it suggests a new novel way of integrating options to connect the fuzzy with water treatment to enhance the electrospun nanofiber prospects in the water industry. Implementing these fuzzy models in future by water industries might reduce maintenance costs and help in understanding the characteristics of the effluent quality in detail.

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Section: Fuzzy Analysismentioning

confidence: 99%

Section: Machine Learning For the Prediction Of The Wastewater Qualitymentioning

confidence: 99%

Section: Machine Learning For the Prediction Of The Wastewater Qualitymentioning

confidence: 99%

“…3). However, these methods have certain limitations and can't be considered accurate for treating water and wastewater (Srivastava and Handa, 2022).…”

Section: Anlnmentioning

confidence: 99%

See 2 more Smart Citations