Evaluation of waste water treatment technologies by combined analytical hierarchy process and grey relational analysis

Abstract

<p>Recycling of wastewater is an important issue as it is used for many important applications such as industries, agriculture, household applications and due to water scarcity across the different countries. Wastewater treatment is an important activity as it protects human being and the ecosystem from harmful and toxic elements present in wastewater. Performance evaluation of wastewater treatment technologies being complex Multi-criteria Decision Making (MCDM) problem that has contradictory evaluation factors. This paper is focused on the analysis of factors that affect evaluation of wastewater treatment technologies and their impact by developing a model which combine Analytical Hierarchy Process (AHP) and Grey Relational Analysis (GRA) for the assessment and ranking wastewater treatment technologies (WWTTs) respectively. The best WWTT is chosen based on data acquired through a case study. AHP is applied for computing criteria weights based on comparative significance while Grey Relational Analysis method is applied for ranking and selecting the wastewater treatment technologies. Sensitivity analysis is performed to show the impact of grey relational scores of alternatives in line with variations of distinguished coefficients.</p>

A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

“…(Table 5). Compared to the improved GRA algorithm in studies by literature (Sasikumar et 2022) and literature (Xu et al, 2020), the RMSE is smaller when selecting the characteristic variables with ACGRA. This indicates that ACGRA can make full use of the correlation between water quality indicators and can effectively improve the accuracy of river water quality assessment.…”

“…The results of water quality assessment using features obtained by weighted grey relational analysis are better compared to previous research literature ( Zhang et al., 2022b ) using features obtained by equal-weighted grey relational analysis for water quality assessment ( Table 5 ). Compared to the improved GRA algorithm in studies by literature ( Sasikumar et al., 2022 ) and literature ( Xu et al., 2020 ), the RMSE is smaller when selecting the characteristic variables with ACGRA. This indicates that ACGRA can make full use of the correlation between water quality indicators and can effectively improve the accuracy of river water quality assessment.…”

“…Based on data in Table 4 , ACGRA fully considers the effect of subjective and objective weights on the correlation between water quality and other water quality indicators compared to the single weighted grey correlation analysis used in the literature literature ( Sasikumar et al., 2022 ) and literature ( Xu et al., 2020 ).…”

“…Sutadian et al. proposed a subjectively empowered method for calculating the correlation of indicators ( Sasikumar et al., 2022 ). Miao, C et al.…”

“…GRA is a method for assessing the degree of interaction between indicators through geometric similarity between them (Zhang et al, 2021), and it takes a leveling treatment of the indicators. Sutadian et al proposed a subjectively empowered method for calculating the correlation of indicators (Sasikumar et al, 2022). Miao, C et al also introduced an objectively assigned indicator correlation calculation (Xu et al, 2020).…”