Hybrid multi-criteria decision-making approach to select appropriate biomass resources for biofuel production

Firouzi, Saeed; Allahyari, Mohammad Sadegh; Isazadeh, Mohammadreza; Nikkhah, Amin; Haute, Sam Van

doi:10.1016/j.scitotenv.2020.144449

Cited by 56 publications

(18 citation statements)

References 52 publications

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“…They used the ELECTRE III method, and the results showed that the agro-energy regions with cereals and arable crops were better than those with fruit trees and other crops. Firouzi et al, 2021 [ 7 ], on the other hand, developed a hybrid method based on the combination of three MCDM methods (TOPSIS, ARAS, WASPAS) and three ranking aggregation methods (Borda, Copeland, and Rank Mean) to prioritize suitable biomass resources for biofuel production in Iran. In 2012, M. Van Dael et al, 2012 [ 24 ] proposed an AHP model to select potentially attractive locations to establish a biomass project in the Limburg province of Belgium.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bioenergy projects are usually very complex and must meet a set of, sometimes conflicting, requirements [ 6 ]. The selection of the appropriate biomass for a bioenergy project is a multi-criteria decision-making (MCDM) problem [ 7 , 8 ]. The choice of biomass can have an impact not only on the project's economic feasibility but also on environmental and social issues [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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A multicriteria approach for biomass availability assessment and selection for energy production in Burkina Faso: A hybrid AHP-TOPSIS approach

Zoma,

Sawadogo

2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multicriteria approach for biomass availability assessment and selection for energy production in Burkina Faso: A hybrid AHP-TOPSIS approach

Zoma,

Sawadogo

2023

Heliyon

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“…Biomass feedstock is a sustainable and environmentally friendly source of renewable energy that can replace fossil fuels 115. The biomass resources available in the world are 8 times greater than the current requirements of energy 116. The potential of biomass of 150 EJ year −1 is reported, which will be increased to 200–500 EJ year −1 by year 2050.…”

Section: Way Forward For Biofuels‐based On Renewability and Sustainab...mentioning

confidence: 99%

Suitability of Biofuels Production on Commercial Scale from Various Feedstocks: A Critical Review

et al. 2022

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The growing demand for energy resources and the increase in greenhouse gas (GHG) emissions has given biofuels a lot of attention. Liquid biofuels can be a significant alternative for the transport sector. Generally, biofuels are categorized into four generations based on the feedstock. This review reports the availability, economic feasibility, and potential of biofuel feedstock in global scenario. Feedstock for first-generation biofuel comprises edible resources impacting the food supply. Second generation biofuels are based on different residual materials and nonedible fuel crops. Cost and technological limita-tions for commercialization hinders this option. Microalgae are the feedstock for third generation biofuels providing the highest yield compared to the other two generations. A scale-up to commercial level is not possible as this requires further development. Genetically modified microorganisms are used as a feedstock for fourth generation biofuel production with the highest possible yield. Third and fourth generation biofuels have potential to replace fossil fuels. This review recommends certain suggestions for sustainable biofuel production.

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“…The deterioration model that obtained the largest number of predominance over the other deterioration model accomplished a win and was appended as a "Winner." On the other hand, the other deterioration model is known as the "Loser" [55,56]. A final sore of each deterioration model was yielded by subtracting the number of losses from the number of dominances.…”

Section: Copelandmentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

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Hybrid multi-criteria decision-making approach to select appropriate biomass resources for biofuel production

Cited by 56 publications

References 52 publications

A multicriteria approach for biomass availability assessment and selection for energy production in Burkina Faso: A hybrid AHP-TOPSIS approach

A multicriteria approach for biomass availability assessment and selection for energy production in Burkina Faso: A hybrid AHP-TOPSIS approach

Suitability of Biofuels Production on Commercial Scale from Various Feedstocks: A Critical Review

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

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