A Novel Automated Decision-Making Process for Analysis of Ions and Organic Materials in Drinking Water

Author(s): Javanbakht T.

Affiliation(s): Department of Computer Science, University of Quebec in Montreal, 201 President Kennedy St., Montreal, H2X 3Y7 Quebec, Canada

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 1 (2023)

Submitted: December 5, 2022
Received in revised form: February 6, 2023
Accepted for publication: February 20, 2023
Available online: February 24, 2023

Javanbakht T. (2023). A novel automated decision-making process for analysis of ions and organic materials in drinking water. Journal of Engineering Sciences, Vol. 10(1), pp. H1-H7, doi: 10.21272/jes.2023.10(1).h1

DOI: 10.21272/jes.2023.10(1).h1

Research Area:  CHEMICAL ENGINEERING: Environmental Protection

Abstract. This paper applies a novel automated decision-making process with TOPSIS to analyze ions and organic materials in drinking water. The hypothesis was that the modified TOPSIS algorithm with the Łukasiewicz fuzzy disjunction would be appropriate to optimize the drinking water samples. The maximum output values were set to one to apply the fuzzy disjunction. The concentrations of ions and organic materials in the drinking water samples were considered from the values for naturally occurring chemicals that would be of health significance. Materials with positive effects on the body were considered profit criteria, whereas other ones with negative impacts on human health were considered cost criteria. The analysis of samples with unmodified TOPSIS showed that profit criteria having high concentrations and cost criteria having low concentrations had the dominant effects on the candidates’ ranking. The modified TOPSIS showed that the candidates’ ranking in the second analysis series was the same as in the first. However, the value of 1.0 for the fourth candidate’s concentration of nitrite, which resulted from the fuzzy disjunction in the algorithm of the modified TOPSIS, was attributed to the confusion of the drinking water and undrinkable water categories. The optimization results for drinking water samples could be applied in science and engineering based on the concentrations of their ions and organic materials with the automated decision-making process for their distinction from undrinkable water.

Keywords: drinking water, automated decision-making process, health public, environment.


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