Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process | Journal of Engineering Sciences

Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process

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)

Dates:
Submitted: March 8, 2023
Received in revised form: May 6, 2023
Accepted for publication: May 15, 2023
Available online: May 18, 2023

Citation:
Javanbakht T. (2023). Optimization of graphene oxide’s characteristics with TOPSIS using an automated decision-making process. Journal of Engineering Sciences, Vol. 10(1), pp. E1-E7, doi: 10.21272/jes.2023.10(1).e1

DOI: 10.21272/jes.2023.10(1).e1

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. The present study focuses on a new application of TOPSIS to predict and optimize graphene oxide’s characteristics. Although this carbon-based material has been investigated previously, its optimization with this method using an automated decision-making process has not been performed yet. The major problem in the design and analysis of this nanomaterial is the lack of information on comparing its characteristics, which has led to the use of diverse methods that have not been appropriately compared. Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. Several characteristics of graphene oxide, such as its antibiofilm activity, hemocompatibility, activity with ferrous ions in hydrogen peroxide, rheological properties, and the cost of its preparation, have been considered in its analysis with TOPSIS. The results of this study revealed that the consideration of the criteria of this nanomaterial as profit or cost criteria would impact the distances of candidates from the alternatives. Moreover, the ranks of the candidates changed when the rheological properties were considered differently in the data analysis. This investigation can help improve the use of this nanomaterial in academic and industrial investigations.

Keywords: process innovation, energy optimization, prediction, TOPSIS, algorithm.

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