Detalhes
Nome
António BaptistaCargo
Investigador AuxiliarDesde
03 abril 2023
Nacionalidade
PortugalCentro
Centro de Engenharia de Sistemas EmpresariaisContactos
+351222094398
antonio.baptista@inesctec.pt
2023
Autores
Peças, P; John, L; Ribeiro, I; Baptista, AJ; Pinto, SM; Dias, R; Henriques, J; Estrela, M; Pilastri, A; Cunha, F;
Publicação
Sustainability (Switzerland)
Abstract
In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven approaches with sustainability assessment approaches, since overcoming this challenge will foster more agile sustainable development. Without disregarding the effort of academics and practitioners in the development of sustainability assessment approaches, the authors consider the need for holistic frameworks that also encourage continuous improvement in sustainable development. The main objective of this research is to propose a holistic framework that supports companies to assess sustainability performance effectively and more easily, supported by digital capabilities and data-driven concepts, while integrating improvement procedures and methodologies. To achieve this objective, the research is based on the analysis of published approaches, with special emphasis on the data-driven concepts supporting sustainability assessment and Lean Thinking methods. From these results, we identified and extracted the metrics, scopes, boundaries, and kinds of output for decision-making. A new holistic framework is described, and we have included a guide with the steps necessary for its adoption in a given company, thus helping to enhance sustainability while using data availability and data-analytics tools. © 2023 by the authors.
2023
Autores
Pecas, P; John, L; Ribeiro, I; Baptista, AJ; Pinto, SM; Dias, R; Henriques, J; Estrela, M; Pilastri, A; Cunha, F;
Publicação
SUSTAINABILITY
Abstract
In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven approaches with sustainability assessment approaches, since overcoming this challenge will foster more agile sustainable development. Without disregarding the effort of academics and practitioners in the development of sustainability assessment approaches, the authors consider the need for holistic frameworks that also encourage continuous improvement in sustainable development. The main objective of this research is to propose a holistic framework that supports companies to assess sustainability performance effectively and more easily, supported by digital capabilities and data-driven concepts, while integrating improvement procedures and methodologies. To achieve this objective, the research is based on the analysis of published approaches, with special emphasis on the data-driven concepts supporting sustainability assessment and Lean Thinking methods. From these results, we identified and extracted the metrics, scopes, boundaries, and kinds of output for decision-making. A new holistic framework is described, and we have included a guide with the steps necessary for its adoption in a given company, thus helping to enhance sustainability while using data availability and data-analytics tools.
2023
Autores
de Matos, B; Salles, R; Mendes, J; Gouveia, JR; Baptista, AJ; Moura, P;
Publicação
MATHEMATICS
Abstract
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.
2022
Autores
Carneiro, T; Oliveira, J; Baptista, AJ; de Castro, PMST;
Publicação
Designs
Abstract
2022
Autores
Gouveia, JR; Goncalves, M; Rocha, R; Baptista, AJ; Monteiro, H;
Publicação
SUSTAINABLE PRODUCTION AND CONSUMPTION
Abstract
This study focuses on the characterization of the production process of a composite sandwich panel for an aircraft structure. Two curing alternatives were compared, namely hot-press and autoclave. A holistic assessment was conducted applying the Total Efficiency Framework, which combines both process efficiency and environmental performance analyses into a single index score to support manufacturing decision. The study provides inventory data, collected at laboratory scale regarding materials, energy consumption, and process operation for composite panel production, which are seldom available. This foreground data was used to quantify the process efficiency, based on lean design tool, and to estimate the potential environmental impacts, using Life Cycle Assessment methodology to determine the eco-efficiency of the production process. The results suggested that the autoclave curing outperforms the hot-press alternative in terms of efficiency, eco-efficiency, and environmental perfor-mance. Regarding the total efficiency index results for maximum productivity, the results show a difference of 12% between the two alternatives, indicating potential competitive advantages in an industrial setting.
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