| 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 | 96 | 2017 |
| 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 | 31 | 2017 |
| 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 | 29 | 2021 |
| ECNet: Large scale machine learning projects for fuel property prediction T Kessler, JH Mack Journal of Open Source Software 2 (17), 401, 2017 | 13 | 2017 |
| 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 | 9 | 2016 |
| 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 | 8 | 2019 |
| 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 | 6 | 2023 |
| 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 | 5 | 2023 |
| 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 | 3 | 2021 |
| 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 | 3 | 2020 |
| 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 | 3 | 2019 |
| 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 | 1 | 2022 |
| Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy T Kessler, A SubLaban, JH Mack University of Massachusetts Lowell, 2022 | 1 | 2022 |
| 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 |
| 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 |
| Eric Sacia A Bell, JH Mack, T Kessler, G Gunbas | | |
