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Travis Kessler
Travis Kessler
Postdoctoral Researcher at UMass Lowell Energy and Combustion Research Laboratory
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Artificial neural network based predictions of cetane number for furanic biofuel additives
T Kessler, ER Sacia, AT Bell, JH Mack
Fuel 206, 171-179, 2017
822017
Application of a rectified linear unit (ReLU) based artificial neural network to cetane number predictions
T Kessler, G Dorian, JH Mack
Internal Combustion Engine Division Fall Technical Conference 58318, V001T02A006, 2017
252017
A comparison of computational models for predicting yield sooting index
T Kessler, PCS John, J Zhu, CS McEnally, LD Pfefferle, JH Mack
Proceedings of the Combustion Institute 38 (1), 1385-1393, 2021
202021
ECNet: Large scale machine learning projects for fuel property prediction
T Kessler, JH Mack
Journal of Open Source Software 2 (17), 401, 2017
122017
Predicting the cetane number of furanic biofuel candidates using an improved artificial neural network based on molecular structure
T Kessler, ER Sacia, AT Bell, JH Mack
Internal Combustion Engine Division Fall Technical Conference 50503, V001T02A010, 2016
92016
Screening compounds for fast pyrolysis and catalytic biofuel upgrading using artificial neural networks
T Kessler, T Schwartz, HW Wong, JH Mack
Internal Combustion Engine Division Fall Technical Conference 59346, V001T02A007, 2019
72019
Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations
T Kessler, T Schwartz, HW Wong, JH Mack
Internal Combustion Engine Division Fall Technical Conference 85512, V001T04A009, 2021
32021
Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks
T Kessler, T Schwartz, HW Wong, JH Mack
Internal Combustion Engine Division Fall Technical Conference 84034, V001T02A006, 2020
32020
ECabc: A feature tuning program focused on Artificial Neural Network hyperparameters
S Sharma, H Gelaf-Romer, T Kessler, JH Mack
Journal of Open Source Software 4 (39), 2019
32019
Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design
A SubLaban, TJ Kessler, N Van Dam, JH Mack
Journal of Energy Resources Technology 145 (10), 102302, 2023
22023
CO2 and HDPE Upcycling: A Plasma Catalysis Alternative
F Gorky, A Nambo, TJ Kessler, JH Mack, ML Carreon
Industrial & Engineering Chemistry Research 62 (46), 19571-19584, 2023
12023
Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy
TJ Kessler, A SubLaban, JH Mack
Engineering Archive, 2022
12022
Predictive Modeling and Statistical Evaluation of Chemical Properties Relevant to the Combustion of Alternative Fuels and Fuel Blends
TJ Kessler
University of Massachusetts Lowell, 2023
2023
Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives
T Kessler, A SubLaban, JH Mack
Internal Combustion Engine Division Fall Technical Conference 86540, V001T02A011, 2022
2022
Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks
TJ Kessler, C Hudson, L Whitmore, JH Mack
University of Massachusetts Lowell, 2020
2020
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