| Optimal Demand Response Scheduling of an Industrial Air Separation Unit Using Data-Driven Dynamic Models MB Calvin Tsay, Ankur Kumar, Jesus Flores Computers & Chemical Engineering, 2019 | 101 | 2019 |
| Smart manufacturing approach for efficient operation of industrial steam-methane reformers A Kumar, M Baldea, TF Edgar, OA Ezekoye Industrial & Engineering Chemistry Research 54 (16), 4360-4370, 2015 | 57 | 2015 |
| A Physics-based Model for Industrial Steam-Methane Reformer Optimization with Non-uniform Temperature Field A Kumar, M Baldea, TF Edgar Computers & Chemical Engineering, 2017 | 55 | 2017 |
| Deploying Kepler workflows as services on a cloud infrastructure for smart manufacturing P Korambath, J Wang, A Kumar, L Hochstein, B Schott, R Graybill, ... Procedia Computer Science 29, 2254-2259, 2014 | 53 | 2014 |
| Real-time optimization of an industrial steam-methane reformer under distributed sensing A Kumar, M Baldea, TF Edgar Control Engineering Practice 54 (September 2016), 140–153, 2016 | 46 | 2016 |
| A Smart Manufacturing Use Case: Furnace Temperature Balancing in Steam Methane Reforming Process via Kepler Workflows P Korambath, J Wang, A Kumar, J Davis, R Graybill, B Schott, M Baldea Procedia Computer Science 80, 680-689, 2016 | 39 | 2016 |
| Data-driven process monitoring and fault analysis for Reformer Units in Hydrogen plants: Industrial application and perspectives A Kumar, A Bhattacharya, J Flores-Cerrillo Computers & Chemical Engineering, 2020 | 30 | 2020 |
| Multi-resolution Model of an Industrial Hydrogen Plant for Plantwide Operational Optimization with Non-uniform Steam-Methane Reformer Temperature Field A Kumar, TF Edgar, M Baldea Computers & Chemical Engineering, 2017 | 30 | 2017 |
| Data-Driven Models and Algorithms for Demand Response Scheduling of Air Separation Units C Tsay, J Shi, A Kumar, J Flores-Cerrillo Computer Aided Chemical Engineering 44, 1273-1278, 2018 | 20 | 2018 |
| On optimal sensing and actuation design for an industrial scale steam methane reformer furnace A Kumar, M Baldea, TF Edgar AIChE Journal 62 (9), 3225-3237, 2016 | 16 | 2016 |
| Integrating Steady-State and Dynamic Models for Multi-Scale Flowsheet Optimization: A Steam-Methane Reforming Case Study C Tsay, A Kumar, M Baldea, TF Edgar 9th International Conference on Foundations of Computer-Aided Process Design …, 2019 | 5 | 2019 |
| Feature Based Fault Detection for Pressure Swing Adsorption Processes J Lee, A Kumar, J Flores-Cerrillo, J Wang, P He IFAC 53 (2), 11301-11306, 2020 | 3 | 2020 |
| Integration of chemical process operation with energy, global market, and plant systems infrastructure J Flores-Cerrillo, CLE Swartz, A Kumar, D Dering Computers & Chemical Engineering, 2023 | 2 | 2023 |
| Industrial Scale Demonstration of Smart Manufacturing, Achieving Transformational Energy Productivity Gains TFEF Edgar, M Baldea, O Ezekoye, H Ganesh, A Kumar, D Wanegar, ... The University of Texas at Austin, 2018 | 2 | 2018 |
| Feature-based statistical process monitoring for pressure swing adsorption processes J Lee, A Kumar, J Flores-Cerrillo, J Wang, P He Frontiers in Chemical Engineering, 2022 | 1 | 2022 |
| Application of smart manufacturing methods to steam methane reformers A Kumar, A Bhattacharya, M Baldea, T Edgar Smart Manufacturing Applications and Case Studies, 2020 | 1 | 2020 |
| Model based operation of industrial steam methane reformers using large scale sensor data A Kumar | 1 | 2016 |
| Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance A Kumar, JF Cerrillo https://play.google.com/store/books/details?id=7JXtEAAAQBAJ, 2024 | | 2024 |
| Machine Learning in Python for Dynamic Process Systems A Kumar, J Flores-Cerrillo https://leanpub.com/ML-Python-for-DPS, 2023 | | 2023 |
| Machine Learning in Python for Process Systems Engineering A Kumar, J Flores-Cerrillo https://leanpub.com/machineLearningPSE, 2022 | | 2022 |
