Framework for Optimized Analysis of Waste Bioenergy Projects

Eliana M. A. Guerreiro, Maicon Silva, Marcio Guerreiro, Taís Carvalho, Attilio Converti (), Hugo Valadares Siqueira and Cassiano Moro Piekarski
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Eliana M. A. Guerreiro: Sustainable Production Systems Laboratory (LESP), Graduate Program in Industrial Engineering (PPGEP), Universidade Tecnológica Federal do Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Maicon Silva: Sustainable Production Systems Laboratory (LESP), Graduate Program in Urban Environmental Sustainbility (PPGSAU), Universidade Tecnologica Federal do Parana (UTFPR), Rua Deputado Heitor Alencar Furtado, 500, Curitiba 81280-340, Brazil
Marcio Guerreiro: Computational Intelligence and Advanced Control Laboratory (LICON), Graduate Program in Industrial Engineering (PPGEP), Universidade Tecnologica Federal do Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Taís Carvalho: Sustainable Production Systems Laboratory (LESP), Graduate Program in Industrial Engineering (PPGEP), Universidade Tecnológica Federal do Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Attilio Converti: Department of Civil, Chemical and Environmental Engineering, University of Genoa, Pole of Chemical Engineering, Via Opera Pia 15, 16145 Genoa, Italy
Hugo Valadares Siqueira: Computational Intelligence and Advanced Control Laboratory (LICON), Graduate Program in Industrial Engineering (PPGEP), Universidade Tecnologica Federal do Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil
Cassiano Moro Piekarski: Sustainable Production Systems Laboratory (LESP), Graduate Program in Industrial Engineering (PPGEP), Universidade Tecnológica Federal do Parana (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Ponta Grossa 84017-220, Brazil

Energies, 2022, vol. 15, issue 17, 1-15

Abstract: Over the years, cities have undergone transformations that, invariably, overload and even compromise the functioning of an energy matrix dependent on increasingly scarce resources. The high demand for energy has challenged stakeholders to invest in more sustainable alternatives, such as bioenergy, which, in addition, helps to reduce the pressure for finite resources, enable the energy recovery of waste and contribute to the mitigation of carbon emissions. For these improvements to be successful, stakeholders need specific technological strategies, requiring tools, methods and solutions that support the decision-making process. In this perspective, the current work aimed to develop a framework optimizing the evaluation of waste bioenergy projects through the application of algorithms. Therefore, a literature review was carried out to select the algorithms and identify the sectors/areas and stages in which they are applied. These algorithms were then grouped into two sequential phases. The first targeted the evaluation of region, based on the type and supply of biomass, while the second sought to optimize aspects related to infrastructure and logistics. Both phases were concluded with the application of multi-criteria methods, thus, identifying the areas/regions with the greatest potential for implementing bioenergy projects. In general, it was observed that there are different algorithms and multi-criteria analysis methods that can be suitable in bioenergy projects. They were used to identify and select the regions with the greatest potential for bioenergy plant implementation, focusing on the type, quantity and perpetuity of biomass supply, to assess the operational efficiency of machines, equipment, processes and to optimize the logistics chain, especially the collection and transport of biomass. Thus, the joint work between the use of algorithms and multi-criteria decision methods provides greater assertiveness in choices, helping to identify the most viable projects and mitigating risks and uncertainties for decision-makers.

Keywords: bioenergy; waste; algorithm; circular economy; framework; decision-making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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