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arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. delgado, C.A.D.M. de Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

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Phenomenological modeling of polyols, citric acid and bio-oil concurrent production by Yarrowia lipolytica from glycerol

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Cleaner Chemical engineering 2023年 5卷

作者： Ana Luiza B.M.A. Campos Felipe V. do Nascimento Argimiro R. Secchi Maria Alice Z. Coelho Chemical and Biochemical Process Engineering Program EPQB/EQ Universidade Federal do Rio de Janeiro PO Box 68502 ZIP 21941-972 Rio de Janeiro Brazil Biochemical Processes Technology Department Chemistry Institute Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil

Biochemical routes have shown to be an interesting alternative for the reuse of glycerol, largely generated as a co-product in biodiesel production reaction. Previous works from the research group have demonstrated that glycerol can be efficiently assimilated by Yarrowia lipolytica to produce erythritol, mannitol, citric acid and bio-oil. To better understand the metabolic pathways involved, this work proposes a mathematical model to describe the observed phenomenon. This is the first computational work to model polyol's production and to address the simultaneous formation of four products by Yarrowia lipolytica . Particle Swarm Optimization and Interior Point Optimization were employed together to find the global optima. The developed model proved to be significantly promising and capable of satisfactorily predict the system's behavior in more than one experimental condition. Not only products’ concurrent formation was accurately described, but also polyols’ modeling was successfully performed, reaching R² values greater than 0.93.

关键词： Yarrowia lipolytica Erythritol Mannitol Biodiesel Particle swarm optimization Interior point optimization

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Optimal Control of Crystal Size and Shape in Batch Crystallization Using a Bivariate Population Balance Modeling

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IFAC-PapersOnLine 2021年第3期54卷 653-660页

作者： Marcellus G.F. de Moraes Martha A. Grover Maurício B. de Souza Paulo L.C. Lage Argimiro R. Secchi Chemical Engineering Program - COPPE Universidade Federal do Rio de Janeiro Rua Horário Macedo 2030 Bloco G Cidade Universitária Rio de Janeiro 21941-914 Brazil School of Chemical & Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. NW Atlanta GA 30332-0100 USA

An optimal control framework was employed to obtain optimal supersaturation/temperature policies for controlling the crystal mass, size, and shape that meet target product specifications. It uses a bivariate population balance model that includes crystal nucleation, growth, dissolution, and disappearance. The optimal control scheme, solving a dynamic optimization problem, was applied to the batch cooling crystallization of potassium dihydrogen phosphate. The population balance model was evaluated in open-loop experiments, showing good prediction for the mean characteristic lengths and number of particles, both for the supersaturation and undersaturation zones. The deterministic optimal control simulations demonstrated the application of the control action policies to produce crystals of desired mass and average shape for different control targets.

关键词： crystallization dynamic optimization shape control population balance optimal control

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Correction: A meta-classification-based approach for outlier identification in GNSS networks

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GPS Solutions 2025年第2期29卷 1-1页

作者： Suraci, Stefano Sampaio de Oliveira, Leonardo Castro Klein, Ivandro Goldschmidt, Ronaldo Ribeiro Rofatto, Vinicius Francisco Defense Engineering Department Military Institute of Engineering Leonardo Castro de Oliveira Rio de Janeiro Brazil Defense Engineering Program Military Institute of Engineering Rio de Janeiro Brazil Graduate Program in Geodetic Sciences Federal University of Paraná Curitiba Brazil Dept. of Civil Construction Federal Institute of Santa Catarina Florianopolis Brazil Systems and Computing Program Military Institute of Engineering Rio de Janeiro Brazil Institute of Geography Federal University of Uberlândia Monte Carmelo Brazil

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An acoustic lens for displacement-free sectorial inspection of pipes with ultrasonic phased arrays

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NDT & E International 2025年 156卷

作者： Gustavo P. Pires Thiago E. Kalid Tatiana de A. Prado Vinícius L. Costa Gabriela R. Pereira Thiago A.R. Passarin Daniel R. Pipa Centro de Pesquisa Leopoldo Américo Miguez de Mello Av. Horácio Macedo 950 - Cidade Universitária Rio de Janeiro 21941-915 RJ Brazil Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Av. Sete de Setembro 3165 - Rebouças Curitiba 80230-901 PR Brazil Program of Metallurgical and Materials Engineering Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brazil

Pipes that transport acidic fluids are prone to pitting corrosion, which causes well-localized thickness loss. The detection and measurement of these small flaws via ultrasonic non-destructive testing (UT) requires a fine sampling of the surface of the pipe, which is time-consuming and expensive, especially in places where the inspections require dedicated infrastructure, such as subsea facilities. In this work, we present an acoustic lens that, used with a UT phased array system capable of beam steering, allows for the inspection of large sections (e.g., 90 degrees) of a submerged pipe without any mechanical displacement. The mathematical formulation is derived from geometrical optics, and algorithmic procedures to compute customized delay laws are provided. Practical aspects of the manufacturing and assembly of the device are also described. Spatial resolutions of a prototypical manufactured lens on three axes are assessed on a 140 mm-diameter pipe and compared to those obtained with a moving single-element transducer, showing competitive resolutions. Measurements of the remaining thicknesses of 1.5 mm-diameter machined flaws performed with the device provided a mean absolute error of 19% of one wavelength and maximum absolute error of 51% ( λ = 1 . 18 m m ). The results demonstrate the potential of the proposed concept to replace single-element UT, significantly reducing inspection time by removing mechanical displacement otherwise necessary during an axial sweep.

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Machine learning framework to predict product distribution of lignocellulosic biomass pyrolysis

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Bioresource technology 2025年 434卷 132746页

作者： Leonardo Voltolini Fernando Arrais Romero Dias Lima Carine Menezes Rebello Ivaldo Itabaiana Idelfonso B R Nogueira Argimiro Resende Secchi Maurício B de Souza School of Chemistry EPQB Universidade Federal do Rio de Janeiro (UFRJ) P.O. Box 68542 Rio de Janeiro 21941-909 RJ Brazil. Electronic address: voltolini@eq.ufrj.br. School of Chemistry EPQB Universidade Federal do Rio de Janeiro (UFRJ) P.O. Box 68542 Rio de Janeiro 21941-909 RJ Brazil. Chemical Engineering Department Norwegian University of Science and Technology (NTNU) Trondheim 793101 Trøndelag Norway. School of Chemistry EPQB Universidade Federal do Rio de Janeiro (UFRJ) P.O. Box 68542 Rio de Janeiro 21941-909 RJ Brazil Chemical Engineering Program PEQ/COPPE Universidade Federal do Rio de Janeiro (UFRJ) PO Box 68502 Rio de Janeiro 21941-972 RJ Brazil.

Machine learning methods have become a trend to model distinct chemical processes, as an alternative to complex first-principles models. Given the complexity of biomass pyrolysis mechanisms, these methods offer a promising approach but often face challenges regarding data scarcity and lack of interpretability. This study aims to develop an interpretable framework for modeling biomass pyrolysis using data from fixed-bed lignocellulosic biomass pyrolysis experiments. A mass change basis was proposed to construct machine learning models, including artificial neural network (ANN) and symbolic regression (SR) models. Feature importance was assessed using Shapley Additive Explanations (SHAP) and compared to Partial Least Squares (PLS) regression, with PLS consistently identifying the best features for symbolic regression. Both ANN and SR models showed similar accuracy, achieving coefficient of determination (R) greater than 0.85 across all phase products in the testing set. Additionally, an uncertainty assessment of SR parameters was conducted to improve model robustness ensuring prediction stability. SR models exhibited superior generalization capacity during extrapolation tests, achieving R values above 0.9 for char and gas phases. For oil values exceeding 10 grams, the SR models struggled with generalization. Overall, the proposed framework provides a valuable tool for interpreting and modeling pyrolysis process data, enabling its use in the decision-making process.

关键词： Artificial neural network Extrapolation assessment Feature selection techniques Symbolic regression Uncertainty assessment

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The use of alternative fuels to mitigate climate change impacts in the transportation sector in rio de janeiro, Brazil

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Transportation Research Procedia 2022年 62卷 752-759页

作者： Tatiana Bruce da Silva Patrícia Baptista Carlos A. Santos Silva Luan Santos IN+ Center for Innovation Technology and Policy Research – LARSys – Instituto Superior Técnico – Universidade de Lisboa Av. Rovisco Pais 1 Lisboa 1049-001 Portugal IN+ Center for Innovation Technology and Policy Research – LARSys – Associação para o desenvolvimento do Instituto Superior Técnico – Universidade de Lisboa Av. Rovisco Pais 1 Lisboa 1049-001 Portugal Production Engineering Program (PEP/COPPE/UFRJ) Av. Horácio Macedo 2030 – Cidade Universitária da Universidade Federal do Rio de Janeiro Rio de Janeiro 21941-598 Brazil

This paper assesses how increased use of alternative fuels (ethanol and electricity) contributes to reduce carbon dioxide (CO 2 ) emissions of light-duty vehicles in rio janeiro state (RJ), Brazil, a region of a developing country – most such analysis focus at the aggregate country level. Using an energy systems model (OSeMOSYS), we analyze scenarios that consider an improvement in the quality of the ethanol (in gCO 2 eq/MJ) consumed in RJ due to the development of the RenovaBio program, greater uptake of electric vehicles, carbon pricing, and a combination of these policies. We also analyze a scenario with the new RenovaBio targets, which the Brazilian government scaled down because of the COVID-19 pandemic. We find that electric vehicles are the most cost-effective policy to reduce CO 2 emissions (by 1.9%) when no carbon pricing is considered. When it is, however, CO 2 emissions are reduced the most (between 47% and 56%), regardless of the vehicle technology being used, but it is the costlier policy (between 5.4% and 15.3%). In scenarios with carbon pricing, flex-fuel vehicles switch to ethanol, an important result for a region where this technology already dominates the vehicle fleet. Greater uptake of electric vehicles leads to lower overall transportation costs (by 2.5%), but when a CO 2 price is considered, because the electricity system still relies on fossil fuels, the reduction in CO 2 emissions is compromised. In such a setting, increased ethanol quality reduces carbon emissions more than electric vehicle adoption (by 16%). The RenovaBio targets’ reduction due to the COVID-19 pandemic, however, jeopardizes this result. Such findings convey important evidence to policymakers and the research community: decarbonization of transportation in a region of a developing country, where economic resources are scarcer, can start before electric vehicles become affordable by increasing and maintaining continued access to lower-carbon fuels.

关键词： Alternative fuels ethanol electric vehicles climate change mitigation COVID-19 rio de janeiro state

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CONFIdeNTIALITY OF CYBER-PHYSICAL systems USING EVENT-BASED CRYPTOGRAPHY

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IFAC-PapersOnLine 2020年第2期53卷 1735-1740页

作者： Públio M. Lima Lilian K. Carvalho Marcos V. Moreira COPPE - Electrical Engineering Program Universidade Federal do Rio de Janeiro Cidade Universitária Ilha do Fundão Rio de Janeiro 21.945-970 RJ Brazil

One of the most important challenges for the application of cyber-physical systems (CPS) in smart industries is ensuring its security against cyber attacks. In this paper, we consider that the CPS is abstracted as a Discrete-Event System (deS), and we consider cyber attacks where the intruder eavesdrops the sensor communication channel to detect the occurrence of a sequence in the secret behavior of the system. In order to prevent the attacker from getting information from the sensor channel, we introduce a new cryptographic scheme based on events called event-based cryptography. We also define the property of confidentiality of deS, present a necessary and sufficient condition for ensuring this property, and propose a verification test.

关键词： Cyber-security Cyber-physical systems Network communication Discrete-event systems Automata Confidentiality Encryption

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Evaluation of the use of pullulan films with the addition of organophilic clay in the post-harvest conservation of guavas

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Food chemistry 2025年 491卷

作者： V R L Oliveira E F C Sérvulo Veronica Calado Postgraduate Program in Chemical and Biochemical Process Engineering Technology Center - School of Chemistry Universidade Federal do Rio de Janeiro Avenida Athos da Silveira Ramos Cidade Universitária Rio de Janeiro 21941-909 Brazil. Electronic address: vrafaeloliveira@eq.ufrj.br. Postgraduate Program in Chemical and Biochemical Process Engineering Technology Center - School of Chemistry Universidade Federal do Rio de Janeiro Avenida Athos da Silveira Ramos Cidade Universitária Rio de Janeiro 21941-909 Brazil.

Biopolymeric coatings are essential in food preservation, distribution, and marketing, offering a sustainable approach for postharvest quality maintenance. This study developed pullulan-based coatings (2 % w/v) reinforced with organically modified clay (CTAB-Mt) at varying concentrations to improve barrier and mechanical properties. The formulation containing 10 % CTAB-Mt (P2BO10) showed significant enhancements in water vapor resistance and mechanical strength. Guavas coated with P2BO10 exhibited a reduced respiration rate, contributing to better retention of nutritional quality. Mechanical analysis revealed a 90 % increase in elasticity and over 75 % reduction in solubility, indicating improved resistance to moisture and environmental stress. Physicochemical and physiological evaluations confirmed that P2BO10 effectively preserved the quality of 'Pedro Sato' guavas during 15 days of storage at 14 ± 2 °C and 80 ± 2 % RH. The coating extended shelf life while maintaining key quality attributes, demonstrating its potential as an eco-friendly solution for postharvest fruit preservation.

关键词： Biopolymer Guava Organically modified clay Package Pullulan

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Societal impact of the Heroine’s Learning Journey: Inspiring women in STEM through the power of role models

Societal Impacts

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Societal Impacts 2025年 5卷

作者： Luis Felipe Coimbra Costa Carlos Eduardo Barbosa Luciana Maria Azevedo Nascimento Larissa Galeno Yuri Lima Ana Moura Santos Geraldo Xexéo Jano Moreira de Souza Program of Systems and Computer Engineering – Universidade Federal do Rio de Janeiro (UFRJ) Av. Horácio Macedo 2030 Rio de Janeiro RJ 21941-450 Brazil Centro de Análises de Sistemas Navais (CASNAV) – Marinha do Brasil Rio de Janeiro RJ 20091-000 Brazil Department of Mathematics – Instituto Superior Técnico – Universidade Técnica de Lisboa (UTL) Av. Rovisco Pais 1096 Lisbon 1049-001 Portugal

The Heroine’s Learning Journey (HLJ) is an educational framework addressing gender disparities in STEM. Supported by the FOSTWOM project, it motivates young women through innovative practices and international collaboration. Drawing on heroic narratives from Campbell’s Hero’s Journey and Murdock’s Heroine’s Journey, HLJ integrates these stories with the Heroines Learning System (HeLaS), a digital platform for scalable and adaptable learning environments. This model aims to enhance female participation in STEM courses and assess the societal impact of HLJ. The Heroine’s Learning Journey has been implemented in educational settings, including online courses and a case study in the rural community of Recreio, Brazil. We collected quantitative and qualitative data to evaluate the model’s effectiveness. The results demonstrate the HLJ’s potential to inspire systemic change by fostering a more inclusive and diverse STEM landscape. This research highlights the importance of innovative educational approaches in driving societal transformation and shaping the future of STEM education.

关键词： Heroine’s Learning Journey STEM education Gender equality Narrative learning Student engagement Educational innovation

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