Research Library

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Research Papers (23598)

Correction to Acknowledgement

Unknown Author

3D Printing in Medicine • 2021

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Detection of alkali path in a pilot-scale combustor using laser spectroscopy and surface ionization — From vapor to particles

Jan Viljanen, Dan Gall, Ivan Gogolev et al.

Fuel • 2023

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Pilot-scale study of desulfurization wastewater evaporation: Emission and migration characteristics of mercury

Heng Chen, Lingxiao Zhan, Liyan Gu et al.

Fuel • 2022

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Synergistic reduction of SO2 emissions while co-firing biomass with coal in pilot-scale (1.5 MWth) and full-scale (471 MWe) combustors

Rajarshi Roy, Spencer Bandi, Xiaolong Li et al.

Fuel • 2024

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20 kW Pilot scale steam‑oxygen gasification of solid recovered fuel with a focus on newly developed off-line and on-line tar measurement methods

Yen-Hau Chen, Ashak Mahmud Parvez, Max Schmid et al.

Fuel Processing Technology • 2022

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Coprocessing of waste cooking oil and fatty acid model compounds with VGO in a pilot-scale FCC riser

Yudi Zhao, Xuan Guo, Yunming Fang

Fuel • 2024

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Modeling and design of a calciner for commercial-scale CO2 capture using stochastic methods and results from pilot tests

Martin Greco-Coppi, Jochen Ströhle, Bernd Epple

Fuel • 2025

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Effects of co-combustion Fe-rich mediocre ash coal on slag deposition of Ca-rich Zhundong coal in a 0.4 MW pilot-scale facility

Xiang He, Jing Jin, Weizhi Lv et al.

Fuel • 2023

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Pilot-scale study of methane-assisted catalytic bitumen partial upgrading

Zhaofei Li, Ali Omidkar, Hua Song

Fuel Processing Technology • 2024

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Fischer-Tropsch Kinetics Comparison and Sensitivity Analysis for a Pilot-Scale Jet Fuel Production Reactor

Alejandro Morales, Antoine Rouxhet, Grégoire Leonard

SSRN Electronic Journal • 2021

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Pilot-scale study on downdraft gasification of municipal solid waste with mass and energy balance analysis

Ayyadurai Saravanakumar, Wei-Hsin Chen, Kantha Deivi Arunachalam et al.

Fuel • 2022

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Experimental assessment of a pilot-scale gasification plant fueled with olive pomace pellets for combined power, heat and biochar production

Roque Aguado, Antonio Escámez, Francisco Jurado et al.

Fuel • 2023

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Steam reforming of tar using biomass gasification char in a Pilot-scale gasifier

Ali Abdelaal, Daniele Antolini, Stefano Piazzi et al.

Fuel • 2023

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Construction of a pilot scale biogas digester at the University of Ibadan Dairy Farm, Abadina

Chisom Emmanuel Aralu, Daniel Eseoghene Karakitie, David Abimbola Fadare

Fuel Communications • 2021

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Evaluation of 100% alternative fuel combustion under oxyfuel conditions in a pilot-scale burner for application in retrofit oxyfuel cement kiln

Cynthia Kroumian, Joerg Maier, Konstantina Peloriadi et al.

Fuel • 2025

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Entrained flow gasification: Pilot-scale experimental, balancing and equilibrium data for model validation

Maximilian Dammann, Ulrike Santo, David Böning et al.

Fuel • 2025

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Pilot scale production of fuel pellets from waste biomass leaves: Effect of milling size on pelletization process and pellet quality

Priyabrata Pradhan, Sanjay M. Mahajani, Amit Arora

Fuel • 2021

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Pilot-scale study of sorption-enhanced gasification of sewage sludge

Samuel Moles, Isabel Martinez, María Soledad Callén et al.

Fuel • 2024

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Megawatt-scale hydrogen fuel cell backup generator system for datacentres

Unknown Author

Fuel Cells Bulletin • 2021

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Steam reforming of tar studied in bench-scale experiments and pilot-scale tests with simulations

Qiang Li, Qian Wang, Yosuke Tsuboi et al.

Fuel • 2021

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Oxy-fuel combustion of softwood in a pilot-scale down-fired pulverized combustor – Fate of potassium

Emil Thorin, Alexey Sepman, Markus Carlborg et al.

Fuel • 2025

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Machine learning – A new kind of cultural tool? A “recontextualisation” perspective on machine learning + interprofessional learning

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David Guile

Learning, Culture and Social Interaction • 2023

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Public policymaking for international agricultural trade using association rules and ensemble machine learning

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Feras A. Batarseh, Munisamy Gopinath, Anderson Monken et al.

Machine Learning with Applications • 2021

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Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning

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Debora Di Caprio, Francisco J. Santos-Arteaga

Machine Learning with Applications • 2022

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Online Education User Behaviour Based on Machine Learning

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Unknown Author

Machine Learning Theory and Practice • 2022

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Automatic Text Classification Model Based on Machine Learning

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Unknown Author

Machine Learning Theory and Practice • 2023

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Prediction Children's Gnawing Behaviour Based on Machine Learning

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Unknown Author

Machine Learning Theory and Practice • 2021

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Machine Learning Based Cancer Classification Using Gene Expression Data

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Unknown Author

International Journal of Machine Learning • 2023

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A machine learning model pipeline for detecting wet pavement condition from live scenes of traffic cameras

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Clint Morris, Jidong J. Yang

Machine Learning with Applications • 2021

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Machine Learning for Robot Manipulation in Industrial Settings

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Unknown Author

Korea Journal on Machine Learning and Applications in Robotics • 2022

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Extremely randomised trees machine learning model for electricity theft detection

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Stanley Yaw Appiah, Emmanuel Kofi Akowuah, Valentine Chibueze Ikpo et al.

Machine Learning with Applications • 2023

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AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

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David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys

Foundations and Trends® in Machine Learning • 2023

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-performance solutions to arbitrary ML problems with minimal human interaction. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. However, these ambitions are unlikely to be achieved in a robust manner without the broader synthesis of various mechanisms and theoretical frameworks, which, at the present time, remain scattered across numerous research threads. Accordingly, this review seeks to motivate a more expansive perspective on what constitutes an automated/autonomous ML system, alongside consideration of how best to consolidate those elements. In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. We also develop a conceptual framework throughout the review, augmented by each topic, to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. Ultimately, we conclude that the notion of architectural integration deserves more discussion, without which the field of automated ML risks stifling both its technical advantages and general uptake.

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