A conceptual model for evaluating eco-efficiency of thermal spraying processes

• Published in: Heliyon Vol. 10; no. 12; p. e32414
• Main Authors: Xavier Belem, Maria Julia, Junior, Milton Vieira, Mummolo, Giovanni, Facchini, Francesco
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 30.06.2024; Elsevier
• Subjects: Assessment model; Eco-efficiency; Environmental indicators; Thermal spray; Assessment model; Eco-efficiency; Thermal spray; Environmental indicators
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e32414

Thermal spraying (TS) is one of the main processes for obtaining surfaces with the desired protective properties in various industrial applications. TS is an energy-intensive treatment required to heat the application material and consumes different resources. To assess the environmental impact of TS, it becomes necessary to integrate an approach that jointly analyses and evaluates the economic and environmental variables influencing the system. The concept of eco-efficiency (EE) added to the TS process allows for assessing the environmental and economic condition through the survey and application of eco-indicators. The lack of an EE evaluation model for TS processes was identified based on literature searches. Thus, the overall objective of this work is to propose a conceptual model to evaluate the EE of TS treatment, selecting environmental and economic indicators considered more impactful in the process. The model developed consists of three main steps: (i) the input and output indicators (environmental and economic) are identified by applying the Analytic Hierarchy Process (AHP) method; (ii) the structure to be employed in the model is defined; and (iii) the Data Envelopment Analysis (DEA) model is applied to define the EE evaluation form. The proposed model consists of clear and easy-to-follow steps for evaluating the EE of spraying processes, filling the gap found in the literature. The use of DEA allowed the integration of the environmental and economic indicators obtained from the TS processes to generate important insights for evaluating EE. The results prove the model's effectiveness in identifying the EE results for each analysed unit of the TS process. The model has provided an evaluation consistent with the existing studies, and the EE scores were assessed according to twenty-one decision-making units (DMUs) allowing the identification of the most eco-efficient DMUs concerning TS processes.