Results 71 to 80 of about 11,890 (266)

Explainable machine learning-based prediction of fuel consumption in ship main engines using operational data

Brodogradnja
A significant percentage of fuel consumption and emissions from transportation activities is related to maritime transportation. Hence, accurate prediction models for fuel consumption are quite important. Machine learning offers a data-driven approach to
Anh Tuan Hoang, Thi Anh Em Bui, Xuan Phuong Nguyen, Van Hung Bui, Quang Chien Nguyen, Thanh Hai Truong, Nghia Chung   +6 more
doaj   +1 more source

Science‐Towards‐Technology Breakthrough in CO2 Electroreduction: Multiphysics, Multiscale, and Artificial Intelligence Insights

Advanced Materials, EarlyView.
Electrochemical CO2RR is a key technology for converting CO2 into chemicals, but there remains a gap between “laboratory science” and “engineering practice” in current research. This review establishes a multi‐scale research framework, encompassing atomic‐level characterization, microenvironment regulation, external field‐assisted optimization, and AI ...
Ping Hong, Changfan Xu, Huaping Zhao, Yong Lei   +3 more
wiley   +1 more source

Field Measurement and Calibration of HDM-4 Fuel Consumption Model on Interstate Highway in Florida

International Journal of Transportation Science and Technology, 2015
Fuel consumptions are measured by operating passenger car and tractor-trailer on two interstate roadway sites in Florida. Each site contains flexible pavement and rigid pavement with similar pavement, traffic and environmental condition.
Xin Jiao, Michael Bienvenu
doaj   +1 more source

A Neuro-Fuzzy Approach for Vehicle Fuel Consumption Prediction

Journal of Electrical Technology UMY, 2018
This paper presents a neuro-fuzzy approach for predicting vehicle fuel consumption. The prediction of fuel consumption of a vehicle has become a strategic issue. This is because it is not only related to the problem of the availability of fuel which is getting thinner but also the problem of the environmental impact caused.
Indah Soesanti, Ramadoni Syahputra
openaire   +2 more sources

Oxygen Evolution Reaction Catalysts for Acidic‐Media CO2 Electrolyzers

Advanced Materials, EarlyView.
Acidic‐media CO2 electroreduction (CO2R) could decarbonize chemical production, despite relying on rare‐earth elements for anodic oxygen evolution reaction (OER). Transferring the learnings from mature sister technologies (water electrolysis) could accelerate technological development.
Mingcheng Huang, Adnan Ozden
wiley   +1 more source

A Ship Energy Consumption Prediction Method Based on TGMA Model and Feature Selection

Journal of Marine Science and Engineering
Optimizing ship energy efficiency is a crucial measure for reducing fuel use and emissions in the shipping industry. Accurate prediction models of ship energy consumption are essential for achieving this optimization.
Yuhang Liu, Kai Wang, Yong Lu, Yongfeng Zhang, Zhongwei Li, Ranqi Ma, Lianzhong Huang   +6 more
doaj   +1 more source

Light‐Programmable Interfaces: From Molecular Photoswitching to Adaptive Membrane Separations

Advanced Materials Interfaces, EarlyView.
This review advances an interface‐centered framework for light‐responsive membranes, linking molecular photoswitches (azobenzene (AZO), spiropyran (SP), diarylethene (DAE), donor–acceptor Stenhouse adducts (DASA), photoacid) to integration strategies in polymeric, porous, self‐assembled, and mixed‐matrix systems.
Liangliang Zhang, Senlin Gu, Jianyang Zhao, Xiaojuan Hao, Lu Hua Li, Jihua Li, Lingxue Kong   +6 more
wiley   +1 more source

Predicting Dynamic Fuel Oil Consumption on Ships with Automated Machine Learning

Energy Procedia, 2019
This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval ...
Ahlgren, Fredrik, Mondejar, Maria E., Thern, Marcus   +2 more
openaire   +3 more sources

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Accelerating Discovery to Deployment: Argonne's Materials Engineering Research Facility (MERF) and Its Role in Scaling Materials Technologies for Water and Resource Solutions

Advanced Materials Technologies, EarlyView.
In this Perspective, we highlight the processing science and scale‐up capabilities of the Materials Engineering Research Facility (MERF) at the U.S. Department of Energy's Argonne National Laboratory, with an emphasis on practical solutions for sustainable water and critical resource recovery. We demonstrate how national laboratories bridge fundamental
Yuepeng Zhang, Bratin Sengupta, Xiaoao Shi, Haihui Pu, Jeffrey W. Elam, Gregory K. Krumdick, Jessica Durham Macholz, Allison Bennett Irion, Junhong Chen, Seth B. Darling   +9 more
wiley   +1 more source