@misc{SafdarDorneanuSantosdaSilvaetal., author = {Safdar, Muddasar and Dorneanu, Bogdan and Santos da Silva, Jefferson and Santos Mascarenhas, Artur Jose and Valverde Pontes, Karen and Arellano-Garc{\´i}a, Harvey}, title = {Advancements in CO2 methanation: customized heterogeneous Ni-Perovskite catalyst for sustainable SNG production}, series = {Annual Meeting on Reaction Engineering and Electrochemical Processes 2024}, journal = {Annual Meeting on Reaction Engineering and Electrochemical Processes 2024}, language = {en} } @misc{AlvesAmorimValverdePontesDorneanuetal., author = {Alves Amorim, Ana Paula and Valverde Pontes, Karen and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Optimizing microgrid design and operation : a decision-making framework for residential distributed energy systems in Brazil}, series = {Chemical Engineering Research and Design}, volume = {214 (2025)}, journal = {Chemical Engineering Research and Design}, number = {February 2025}, publisher = {Elsevier}, issn = {0263-8762}, doi = {https://doi.org/10.1016/j.cherd.2024.12.033}, pages = {251 -- 268}, abstract = {This paper explores the optimization of microgrid design and operation for residential distributed energy systems in Brazil, addressing the growing demand for sustainable energy in the context of climate change. A decision-making framework based on Mixed-Integer Nonlinear Programming (MINLP) is proposed to integrate distributed energy resources (DERs) such as solar, wind, and biogas. Key challenges include managing the variability of renewable resources and complying with local regulations, while also addressing gaps in literature, particularly the impact of time-dependent efficiency profiles on energy sharing within microgrids. By employing innovative analyses and clustering techniques, the research optimizes microgrid configurations, accounting for seasonal demand fluctuations and the influence of incentive policies on system feasibility. The findings reveal that incorporating a time-dependent efficiency model can reduce total costs by 45 \%. This reduction underscores the importance of accurate efficiency predictions, as the model captures variations in energy generation and utilization efficiency over time, improving system optimization. Additionally, the findings reveal that a well-structured optimization model can meet 100 \% of electricity and hot water demands across all scenarios, with customized incentives playing a crucial role in reducing costs and promoting sustainability.}, language = {en} } @misc{AlvesAmorimDorneanuValverdePontesetal., author = {Alves Amorim, Ana Paula and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garc{\´i}a, Harvey}, title = {A framework for decision-making to encourage utilization of residential distributed energy systems in Brazil}, series = {Computer Aided Chemical Engineering}, volume = {Vol. 52}, journal = {Computer Aided Chemical Engineering}, issn = {1570-7946}, doi = {10.1016/B978-0-443-15274-0.50481-9}, pages = {3019 -- 3024}, abstract = {The Distributed Energy Systems (DES) or microgrid arose from the need to reduce greenhouse gases (GHG) emitted into the atmosphere by burning fossil fuels to generate energy. Reduction of energy losses, reconfiguration of the protection system and reduction of costs, and optimizing the configuration of these systems is recommended. Despite new research in literature, there is still a lack of optimization models that address the Brazilian reality. Therefore, the objective of this work is to introduce a decision-making framework for the design and operation of residential DES that takes into account the particularities of Brazil, based on mixed-integer programming models. The applicability of the framework is tested on a case study of a residential DES of 5 houses, located in Salvador, and used to compare scenarios pre- and post-COVID-19. The results show significant reduction in total annual cost and GHG emissions versus the base case without DES. This indicates that, although the country has a mostly "clean" energy matrix due to the use of hydroelectric plants, DES can enable improvement in residential electricity generation.}, language = {en} } @misc{CunhaCordeiroSafdarAquinoetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Aquino, Gabrielle S. and Silva, Jefferson S. and Paff, Jessica Sophie and Valverde Pontes, Karen and Dorneanu, Bogdan and Arellano-Garc{\´i}a, Harvey and Mascarenhas, Artur Jos{\´e}}, title = {Sustainable hydrogen production via biogas reforming over NiO-MxOy - Al2O3 catalysts (M = Na, K, Ca and Mg)}, series = {22 Congreso Brasileiro de Catalise}, journal = {22 Congreso Brasileiro de Catalise}, abstract = {A sustainable way to generate hydrogen is through dry biogas reforming, which uses methane gas and carbon dioxide to produce hydrogen. This study reveals partial results of the dry reforming of biogas in NiO-MxOy-Al2O3 catalysts (M=Na, K, Ca and Mg). The CO2 conversion varied between 79\% and 94\%, the CH4 conversion between 58\% and 75\%, the H2/CO ratio between 0.98 and 1.15 and the H2 yield between 37\% and 45\%. These values ​​surpass literary references and the industrial catalyst, highlighting the promise of these materials for sustainable hydrogen production. The catalyst with Ca stood out due to its higher surface basicity, exhibiting the best conversion results and yield in H2.}, language = {en} } @misc{CunhaCordeiroSafdarSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Santos da Silva, Jefferson and Silva de Aquino, Gabrielle and Vaz dos Santos Rios, Jo{\~a}o Gabriel and Brand{\~a}o dos Santos, Maur{\´i}cio and Teixeira Cruz, Fernanda and Alves Fiuza-Junio, Raildo and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Valverde Pontes, Karen and Santos Mascarenhas, Artur Jos{\´e}}, title = {Effect of support on Ni catalysts prepared by the combustion method applied in the dry reforming of biogas for production of sustainable hydrogen}, series = {International journal of hydrogen energy}, volume = {204}, journal = {International journal of hydrogen energy}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1879-3487}, doi = {10.1016/j.ijhydene.2025.153150}, pages = {1 -- 25}, abstract = {This work investigated Ni catalysts on different supports (MgO, ZrO2, NiAl2O4, CeO2 and La2O3) prepared by the combustion method aiming for sustainable hydrogen production via simulated biogas dry reforming. The Ni/NiAl2O4 catalyst stood out among the materials due to its high Ni dispersion, low crystallite size and strong metal-support interaction, being stable for 8 h of reaction with high H2 yield and low coke deposition. The Ni/CeO2 catalyst showed good catalytic activity, but with high coke deposition (11.7 \%). The Ni/La2O3 catalyst showed an increase over the reaction time, due to the dynamic reconstruction of the surface. The Ni/MgO and Ni/ZrO2 catalysts did not present satisfactory performance when compared to the other catalysts, due to the low Ni dispersion and high crystallite size. The Ni/NiAl2O4 catalyst is very promising, due to the high production of H2, low coke deposition, thermal stability, but new studies on durability and economic viability are necessary.}, language = {en} } @misc{JafariSantosdaSilvaDorneanuetal., author = {Jafari, Mitra and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Towards digitalization of catalysis design and reaction engineering : data-driven insights into methanol to DME}, series = {58. Jahrestreffen Deutscher Katalytiker}, journal = {58. Jahrestreffen Deutscher Katalytiker}, pages = {1 -- 2}, abstract = {Dimethyl ether (DME, methoxymethane) is a clean-burning fuel and a promising alternative to conventional fossil fuels, especially in transportation and power generation. Its production from methanol through dehydration offers a viable pathway toward energy sustainability, not only because of its environmental benefits but also due to the high purity of the resulting products and the efficient conversion rate of methanol [1]. However, optimizing this process requires understanding the intricate dependencies among reaction parameters, including temperature, pressure, catalyst type, and feedstock composition [2]. Machine learning offers transformative potential in this context by identifying complex, non-linear interactions among variables and providing predictive insights that can improve reaction efficiency, yield, and product quality. Through predictive modeling, machine learning can significantly reduce the need for experimental trial-and-error by identifying optimal reaction conditions quickly, thereby decreasing costs, enhancing scalability, and supporting continuous, real-time process optimization [3, 4]. In this study, first a dataset is generated including different descriptors like catalyst formulation, pretreatment, characteristics, activation, and reaction conditions. This dataset is then preprocessed by encoding, imputation, and normalization to make it ready for modelling, followed by data analysis to identify patterns and dependencies. Different models, including Gradient Boosting Regressor, XGBoost, LightGBM, and neural networks, are applied to predict methanol conversion and DME yield based on input variables. The models were evaluated through cross-validation, achieving highaccuracy and underscoring the potential of data-driven optimization in enhancing DME production. These steps are illustrated in Figure 1. Finally, the prediction accuracy of each model is investigated, and the best algorithm is selected. The effect of different descriptors on the respond have also been assessed to find out the most effective parameters on the catalyst performance. The best model is then used to predict DME yield and optimize the catalyst and reaction parameters.}, language = {en} } @misc{JafariShafieeSantosdaSilvaetal., author = {Jafari, Mitra and Shafiee, Parisa and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Advancing catalyst design with machine learning : insights from FTS and DME production}, series = {Annual Meeting on Reaction Engineering 2025}, journal = {Annual Meeting on Reaction Engineering 2025}, pages = {1 -- 6}, abstract = {This contribution presents a ML-based framework to optimize heterogeneous catalysts, with a primary focus on the targeted application for FTS and DME production. However, the framework is designed to be generic, with applicability to a broader range of heterogeneous catalytic processes. The study analyzes a variety of catalyst parameters, including composition, pretreatment, and operating conditions, and employs advanced ML techniques such as regression models, ensemble learning, and neural networks to model the relationships between these factors and reaction outcomes. Hyperparameter optimization and performance evaluation using metrics like R², RMSE, MSE, and AIC further improve the accuracy and robustness of the models. In addition, the study provides a comparative analysis of applying this ML framework to both FTS and DME processes, highlighting the similarities and differences in optimizing catalyst performance and operating conditions. The study aims to identify the most influential parameters that drive catalyst performance and to assess the predictive power of each model. By enhancing understanding of how catalyst properties and operating conditions influence the efficiency of FTS and DME production, this work provides valuable insights into more efficient and sustainable catalytic process design.}, language = {en} }