Prime mover selection in thermal power plant integrated with organic Rankine cycle for waste heat recovery using a novel multi criteria decision making approach

Khorasaninejad, Ehsan; Fetanat, Abdolvahhab; Hajabdollahi, Hassan

doi:10.1016/j.applthermaleng.2016.04.058

Cited by 27 publications

(5 citation statements)

References 51 publications

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“…Numerous studies have applied hybrid models combining the PROMETHEE method and other MCDM techniques. For example, Khorasaninejad et al [39] used a hybrid model to determine the best prime mover in a thermal power plant. The model combined fuzzy ANP-DEMATEL to assess criteria importance and relationships and PROMETHEE to rank alternatives.…”

Section: Mcdm Methodsmentioning

confidence: 99%

Ranking Startups Using DEMATEL-ANP-based Fuzzy PROMETHEE II

Nguyen¹,

Chu²

2023

Preprint

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Accelerators play a critical role in fostering innovative ecosystems and nurturing startups. The evaluation and selection of startups, particularly technology-based startups, for acceleration programs are essential in accelerator economy and management. Assessment of startups requires consideration of numerical and qualitative criteria such as sales, prior startup experience, demand validation, and product maturity. Startups must be ranked based on the varying importance of criteria, which can be identified as a fuzzy multi-criteria decision-making (MCDM) problem. MCDM methods have proven effective in managing complex problems. However, the use of MCDM techniques in startup selection and evaluation of criteria interrelationships from the accelerator perspective is yet to be researched. This study proposes a hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model to rank startups and examine the interrelationships between factors. The final preference values are fuzzy numbers, making a fuzzy ranking method necessary for decision-making. An extension of ranking fuzzy numbers using a spread area-based relative maximizing and minimizing set is suggested to improve the flexibility of existing ranking MCDM methods. Algorithms and formulas are derived and a comparison demonstrates the merits of the proposed method. Finally, a numerical experiment is designed to address the viability of the hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model.

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Section: Mcdm Methodsmentioning

confidence: 99%

Ranking Startups Using DEMATEL-ANP-based Fuzzy PROMETHEE II

Nguyen¹,

Chu²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Numerous studies have applied hybrid models combining the PROMETHEE method and other MCDM techniques. Khorasaninejad et al [41] used a hybrid model to determine the best prime mover in a thermal power plant. The model combined fuzzy ANP-DEMATEL to assess criteria importance and relationships and PROMETHEE to rank alternatives.…”

Section: Fuzzy Promethee IImentioning

confidence: 99%

Ranking Startups Using DEMATEL-ANP-Based Fuzzy PROMETHEE II

Nguyen

Chu

2023

Axioms

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In entrepreneurship management, the evaluation and selection of startups for acceleration programs, especially technology-based startups, are crucial. This process involves considering numerical and qualitative criteria such as sales, prior startup experience, demand validation, and product maturity. To effectively rank startups based on the varying importance of these criteria, a fuzzy multi-criteria decision-making (MCDM) approach is needed. Although MCDM methods have been successful in handling complex problems, their application in startup selection and evaluating criteria interrelationships from the accelerator perspective is underexplored. To address this gap, a hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model is proposed in this study, facilitating startup ranking and examining interrelationships among factors. The resulting preference values are fuzzy numbers, necessitating a fuzzy ranking method for decision-making. An extension of ranking fuzzy numbers using a spread area-based relative maximizing and minimizing set is suggested to enhance the flexibility of existing ranking MCDM methods. Algorithms, formulas, and a comparative analysis validate the proposed method, while a numerical experiment verifies the viability of the hybrid model. The final ranking of four startup projects is A4<A1<A3<A2 which indicates that startup project A2 has the highest comprehensive potential, followed by startup project A3.

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“…Several earlier MCDA studies have concerned different aspects of power systems. These include site selection for hydropower (Omitaomu et al 2012, Vučijak et al 2013, solar power (Mamlook et al 2001, Cavallaro 2009), wind power (Sánchez-Lozano et al 2016), production technology evaluations (Pilavachi et al 2011), selection of prime mover for organic Rankine cycle (Khorasaninejad et al 2016), community level power solutions (Cherni et al 2007, Grujić et al 2014, Rahman et al 2013, and large scale power system analyses (Ribeiro et al 2013, Matteson 2014, Maxim 2014, Santoyo-Castelazo & Azapagic 2014, Stein 2013. Mixed heat and power system analyses include definition of the general sustainability index to measure the sustainability of an urban energy system by Jovanovic et al (2010), MCDA model for evaluating renewable energy technologies for the island of Crete by Tsoutsos et al (2009), sustainability ranking of renewable power and heat generation technologies by Dombi et al (2014), MCDA evaluation of multi-source energy systems by Catalina et al (2011).…”

Section: Literature Reviewmentioning

confidence: 99%