Selection of factors affecting integrated municipal wastewater treatment and reuse network: an interpretive structural modelling (ISM) approach

Srivastava, Ria Ranjan; Singh, Prabhat Kumar

doi:10.1007/s10668-022-02428-x

Cited by 5 publications

(3 citation statements)

References 59 publications

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“…In comparison, unevenness and hairiness are in the middle position, Level 2. According to this methodology, the elements at the top position or level-1 are the most influential as they can influence the whole process, and the factor at the bottom level is the least influenced; moreover, it is influenced by others [11]. So according to the study, CSP is at the first rank.…”

Section: Description Of the Results Found From The Ism Methodologymentioning

confidence: 99%

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Prioritizing the quality parameters of cotton carded knit yarn by varying tensions of the bottom aprons of the ring frame

Raian¹,

Akter²,

Baral³

2023

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The forces exerted on fibres during drafting in the drafting zone with the upper and bottom aprons of the Ring Frame significantly impact the yarn quality. Aprons are crucial in manipulating fibres by facilitating movement and directing them toward the primary drafting area. From these two aprons, the bottom apron's tension is changed by changing tension spring bar groove hole positions. There is no scope for changing such settings in the case of top aprons. This research aims to achieve high-quality cotton carded knit yarn by selecting an appropriate bottom apron tension due to the significant role in proper drafting and twist insertion at the time of the manufacturing process directly affecting produced yarn's strength, evenness, imperfections, and hairiness properties. To fulfil this requirement, six yarn samples are produced each time by using 5 different tensions of the bottom drafting aprons. The samples underwent quality parameter testing, including Unevenness (U)%, Coefficient of Variation (CV)%, Imperfection Index (IPI), Hairiness (H), and Count Strength Product (CSP). The findings indicated that increasing the tension of the bottom apron while maintaining positions 1, 2, and 3 reduces the occurrence of neps, variations in thickness and thinness, and imperfections. In contrast, CSP is increased due to elevated stress on the fibres. In general, maintaining an appropriate tension enhanced the quality of the yarn, and the best quality was obtained for position 3, considering all the quality variables. Moreover, by using Interpretive Structural Modelling (ISM) methodology, the pairwise relationship between the quality variables is shown. Finally, after calculation, those quality variables are arranged in three levels. According to this level, the quality parameters are ranked where the top is the highest. It is observed that Count Strength Product is at the highest level, which indicates that CSP should be given more priority. Moreover, the end use of this yarn is to produce knit items, and for the knitting yarn, CSP is one of the most influential quality variables.

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Section: Description Of the Results Found From The Ism Methodologymentioning

confidence: 99%

“…The leading cause of using only the tension of the bottom apron, not the top apron, is that the tension of the bottom apron can be changed by changing the tension spring bar groove hole position, which setting is absent in the case of the top apron. So the tension of the top apron is fixed [10,11].…”

Section: Introductionmentioning

confidence: 99%

Prioritizing the quality parameters of cotton carded knit yarn by varying tensions of the bottom aprons of the ring frame

Raian¹,

Akter²,

Baral³

2023

Text Leather Rev

View full text Add to dashboard Cite

show abstract

“…Since the judgments of experts determine how the different elements are related, ISM becomes interpretive, taking into account the subjectivity of the decision-making process. Such adoption of the ISM-MICMAC approach has been demonstrated in a myriad of overarching applications for more than a decade, with recent reports in rural development [43], supply chains [37,44], project management [45][46][47], management of technology [48,49], disaster resilience [50], medical tourism [51], public safety [52], environmental studies [53], energy planning [54], e-commerce [55], among others. Literature surveys reported by Gardas et al [56] and Attri [57] expound on the breadth and depth of its applications.…”

Section: Introductionmentioning

confidence: 99%

Research Productivity for Augmenting the Innovation Potential of Higher Education Institutions: An Interpretive Structural Modeling Approach and MICMAC Analysis

Ocampo

Aro

Evangelista

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Current literature merely identifies the driving factors of research productivity in higher education institutions without directly examining their interrelationships that would offer some fundamental insights into the nature of these factors. Thus, this work intends to identify those driving factors and establish their structural relationships to determine those factors with crucial roles in advancing research productivity. Due to the subjectivity of the identified driving factors and the notion that the evaluation of their relationships reflects an expert judgment, an interpretive structural modeling (ISM) approach and the Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis were adopted. Results show that institutional support, reward system, research funding, mentoring, and electronic information resources are the most crucial factors influencing research productivity. When addressed, these driving factors would motivate other driving factors, contributing to higher research productivity. In particular, these findings encourage higher education institutions to (1) efficiently allocate research funds and design mentoring programs, (2) offer efficient research incentive schemes, (3) develop initiatives that would support promising research proposals beneficial to the institution, and (4) collaborate with external organizations to grant funding for research proposals. These results contribute significantly to the literature as it provides meaningful insights that aid decision-makers in higher education institutions in resource allocation decisions, policy-making, and the design of efficient initiatives for augmenting their innovation potential.

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Development and validation of an off-gas tool for oxygen transfer efficiency and N2O emission monitoring in wastewater treatment

Freches,

Freitas,

Marques

et al. 2024

Journal of Water Process Engineering

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Selection of factors affecting integrated municipal wastewater treatment and reuse network: an interpretive structural modelling (ISM) approach

Cited by 5 publications

References 59 publications

Prioritizing the quality parameters of cotton carded knit yarn by varying tensions of the bottom aprons of the ring frame

Prioritizing the quality parameters of cotton carded knit yarn by varying tensions of the bottom aprons of the ring frame

Research Productivity for Augmenting the Innovation Potential of Higher Education Institutions: An Interpretive Structural Modeling Approach and MICMAC Analysis

Development and validation of an off-gas tool for oxygen transfer efficiency and N2O emission monitoring in wastewater treatment

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