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Incorporating Ag Nanocrystals with LaFeO₃ Photocathodes Towards Greatly Enhanced Photo-Electro-Catalytic Properties
Sijie Li,
Hao Zeng,
Jiaqi Fan,
Mei Zhu,
Caiyi Zhang,
Xizhong An,
Zhifu Luo,
Haitao Fu,
Xiaohong Yang
Posted: 17 April 2025
Kinetic Analysis of Construction, Renovation, and Demolition (CRD) Wood Pyrolysis Using Model-Fitting and Model-Free Methods by Thermogravimetric Analysis (TGA)
Aravind Ganesan,
Simon Barnabé,
Younès Bareha,
Simon Langlois,
Olivier Rezazgui,
Cyrine Boussabbeh
Posted: 14 April 2025
Hydrothermal Liquefaction (HTL) of Lignin: The Adsorption Separation of Catechol Guaiacol and Phenol
Emmanuel Bala,
Ursel Hornung,
Nicolaus Dahmen
Posted: 10 April 2025
Natural Gas Sweetening Technologies: A Technical and Comparative Analysis of Processes and Applications
Jairo Rondón,
Claudio Lugo
Posted: 08 April 2025
Segmented Plug Flow Reactor Modeling of Hydrogen Separation from Syngas in Palladium Membrane Reactors under Different Operational Conditions
Osama Marzouk
Posted: 04 April 2025
Enhancing Single-Cell Protein Yield Through Grass-Based Substrates: A Study of Lolium perenne and Kluyveromyces marxianus
Tianyi Guo,
Joshua Bode,
Katrin Kuka,
Nils Tippkötter
Posted: 03 April 2025
Mining Waste Materials in Road Construction
Nuha Mashaan,
Bina Yogi
Posted: 02 April 2025
Highly Sensitive Titanium-Based MXene-Reduced Graphene Oxide Composite for Efficient Electrochemical Heavy Metal Detection of Cadmium and Copper Ions in Water
Dharshini Mohanadas,
Rosiah Rohani,
Siti Fatimah A Rahman,
Ebrahim Mahmoudi,
Yusran Sulaiman
Posted: 31 March 2025
Response Surface Optimization of Biodiesel Production via Esterification Reaction of Methanol and Oleic Acid Catalyzed by a Brönsted-Lewis Catalyst PW/UiO/CNTs-OH
Xuyao Xing,
Qiong Wu,
Li Zhang,
Qing Shu
Posted: 24 March 2025
A Sustainable Microwave-Assisted Process for Chemical Recycling and Reuse of Epoxy Resin Matrices
Fabrizio Cafaro,
Francesca Ferrari,
Gloria Anna Carallo,
Antonio Greco,
Alfonso Maffezzoli
Posted: 20 March 2025
Optimal Correlation between Thermal Insulation Properties of Fly Ash-Based Porous Geopolymer and its Strength Level
Adrian Ioana,
Lucian Paunescu,
Augustin Semenescu,
Ionela Luminita Canuta (Bucuroiu)
Posted: 12 March 2025
Recent Advances in Electrified Methane Pyrolysis Technologies for Turquoise Hydrogen Production
Hossein Rohani,
Galina Sudiiarova,
Stephen Matthew Lyth,
Arash Badakhsh
Posted: 06 March 2025
Kinetic Modeling of the Methanol-Assisted Autocatalytic Methanol Synthesis on Cu/ZnO/Al2O3
Wieland Kortuz,
Johannes Leipold,
Achim Kienle,
Andreas Seidel-Morgenstern
Posted: 05 March 2025
Comparative Study of Opuntia ficus-indica Polymers, HPAM, and Their Mixture for Enhanced Oil Recovery
Kamila Bourkaib,
Abdelkader Hadjsadok,
Charaf Eddine Izountar,
Mohamed Fouad Abi Mouloud,
Amin Bouhafs,
Amar Isseri,
Djamila Maatalah,
Meriem BRAIK,
Abdelali Guezei
This study investigates biopolymers as environmentally sustainable alternatives to par-tially hydrolysed polyacrylamide (HPAM), noted (H) in enhanced oil recovery (EOR). Mucilage (M) extracted from the cactus plant Opuntia ficus-indica is an alternative to tra-ditional polymer solutions, aiming to reduce dependency on synthetic materials. The study also evaluates the properties and performance of a blend of 80% HPAM and 20% mucilage. The polymers were analysed using characterisation techniques, including thermogravi-metric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR). Rheological tests demonstrated favorable viscoelastic properties for the 80-20 blend in saline environments at a concentration of 10,000 ppm. Core flooding tests conducted on core plugs from Algerian oil reservoirs at 120°C indicat-ed that incorporating Opuntia ficus-indica mucilage and the 80-20 blend significantly improved flow characteristics and pore permeability compared to HPAM alone. Notably, the recovery factors were 63.3% for HPAM, 84.35% for Mucilage, and 94.28% for the HPAM-mucilage blend, highlighting superior performance in enhancing oil recovery. In conclusion, this study highlights the potential of biopolymers and the blend as sus-tainable solutions for EOR. They offer an effective alternative to conventional polymers and leverage local resources in reservoir applications.
This study investigates biopolymers as environmentally sustainable alternatives to par-tially hydrolysed polyacrylamide (HPAM), noted (H) in enhanced oil recovery (EOR). Mucilage (M) extracted from the cactus plant Opuntia ficus-indica is an alternative to tra-ditional polymer solutions, aiming to reduce dependency on synthetic materials. The study also evaluates the properties and performance of a blend of 80% HPAM and 20% mucilage. The polymers were analysed using characterisation techniques, including thermogravi-metric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR). Rheological tests demonstrated favorable viscoelastic properties for the 80-20 blend in saline environments at a concentration of 10,000 ppm. Core flooding tests conducted on core plugs from Algerian oil reservoirs at 120°C indicat-ed that incorporating Opuntia ficus-indica mucilage and the 80-20 blend significantly improved flow characteristics and pore permeability compared to HPAM alone. Notably, the recovery factors were 63.3% for HPAM, 84.35% for Mucilage, and 94.28% for the HPAM-mucilage blend, highlighting superior performance in enhancing oil recovery. In conclusion, this study highlights the potential of biopolymers and the blend as sus-tainable solutions for EOR. They offer an effective alternative to conventional polymers and leverage local resources in reservoir applications.
Posted: 04 March 2025
Soft Actor-Critic Reinforcement Learning Improves Distillation Column Internals Design Optimization
Dhan Lord Fortela,
Holden Broussard,
Renee Ward,
Carly Broussard,
Ashley Mikolajczyk,
Magdy Bayoumi,
Mark Zappi
Amid the advancements in computer-based chemical process modeling and simulation packages used in commercial applications aimed at accelerating chemical process design and analysis, there are still certain tasks in design optimization such as distillation column internals design that become bottlenecks due to inherent limitations in such software packages. This work demonstrated the use of soft actor-critic (SAC) reinforcement learning (RL) in automating the task of determining the optimal design of trayed multi-stage distillation column. The design environment was created using the AspenPlus® software with its RadFrac module for the required rigorous modeling of column internals. The RL computational work was achieved by developing a Python package that allows interfacing with AspenPlus®, and by implementing in OpenAI’s Gymnasium module the learning space for the state and action variables. The results evidently show that: (1) SAC RL works as automation approach for the design of distillation column internals, (2) the reward scheme in the SAC model significantly affects SAC performance, (3) column diameter is a significant constraint in achieving column internals design specification in flooding, and (4) SAC hyperparameters have varying effect on SAC performance. SAC RL can be implemented as a one-shot learning model that can significantly improve the design of multistage distillation column internals by automating the optimization process.
Amid the advancements in computer-based chemical process modeling and simulation packages used in commercial applications aimed at accelerating chemical process design and analysis, there are still certain tasks in design optimization such as distillation column internals design that become bottlenecks due to inherent limitations in such software packages. This work demonstrated the use of soft actor-critic (SAC) reinforcement learning (RL) in automating the task of determining the optimal design of trayed multi-stage distillation column. The design environment was created using the AspenPlus® software with its RadFrac module for the required rigorous modeling of column internals. The RL computational work was achieved by developing a Python package that allows interfacing with AspenPlus®, and by implementing in OpenAI’s Gymnasium module the learning space for the state and action variables. The results evidently show that: (1) SAC RL works as automation approach for the design of distillation column internals, (2) the reward scheme in the SAC model significantly affects SAC performance, (3) column diameter is a significant constraint in achieving column internals design specification in flooding, and (4) SAC hyperparameters have varying effect on SAC performance. SAC RL can be implemented as a one-shot learning model that can significantly improve the design of multistage distillation column internals by automating the optimization process.
Posted: 04 March 2025
Synthesis of Kaolin Filter Cake-Fe3O4 Composite for Reactive Black 5 Dye Removal from Textile Wastewater: Optimization with Box-Behnken Design
Amdework Belay,
Esayas Alemayehu,
Zemene Worku,
Bernd Lennartz
Posted: 04 March 2025
Synthesis of Fe3O4 Modified Termite Mound Composite for Adsorption of Basic Blue 41 Dye from Textile Wastewater: Characterization and Box-Behnken Optimization
Amare Melaku,
Esayas Alemayehu,
Abebe Worku,
Bernd Lennartz
Posted: 03 March 2025
Radiation and Combustion Effects of Hydrogen-Enrichment on Biomethane Flames
Francisco Elmo Lima Uchoa Filho,
Helton Carlos Marques Sampaio,
Claudecir Fernandes de Freitas Moura Júnior,
Mona Lisa Moura de Oliveira,
Jesse Van Griensven Thé,
Paulo Alexandre Costa Rocha,
André Valente Bueno
Posted: 25 February 2025
Impact of Ultrasound Pretreatment and Temperature on Drying Kinetics and Quality Characteristics of Blood Orange: Comparison with Different Drying Methods
Damla Yılmaz,
Zeynep Hazal TEKIN-CAKMAK,
Salih Karasu
Posted: 24 February 2025
Conceptual Design of the Process of Making Cosmetic Emulsion
Estela Guardado Yordi,
Irma Sofia Guambuguete Guaman,
Mayra Elizabeth Freire Fuentes,
Matteo Radice,
Laura Scalvenzi,
Reinier Abreu-Naranjo,
Luis Ramón Bravo Sánchez,
Amaury Pérez Martínez
Posted: 18 February 2025
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