| 1 |
Deep Learning for Classification of Profit-Based Operating Regions in Industrial Processes
Agarwal P, Tamer M, Sahraei MH, Budman H
Industrial & Engineering Chemistry Research, 59(6), 2378, 2020 |
| 2 |
A simplified strategy to reduce the desorbent consumption and equipment installed in a three-zone simulated moving bed process for the separation of glucose and fructose
Tangpromphan P, Budman H, Jaree A
Chemical Engineering and Processing, 126, 23, 2018 |
| 3 |
On the use of physical boundary conditions for two-phase flow simulations: Integration of control feedback
Agnaou M, Treeratanaphitak T, Mowla A, Ioannidis M, Abukhdeir NM, Budman H
Computers & Chemical Engineering, 118, 268, 2018 |
| 4 |
Fault Detection and Classification for Nonlinear Chemical Processes using Lasso and Gaussian Process
Du YC, Budman H, Duever TA, Du DP
Industrial & Engineering Chemistry Research, 57(27), 8962, 2018 |
| 5 |
Evaluation of a Hybrid Clustering Approach for a Benchmark Industrial System
Fontes CH, Budman H
Industrial & Engineering Chemistry Research, 57(32), 11039, 2018 |
| 6 |
Identification of active constraints in dynamic flux balance analysis
Nikdel A, Budman H
Biotechnology Progress, 33(1), 26, 2017 |
| 7 |
Applications of Polynomial Chaos Expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models
Kumar D, Budman H
Chemical Engineering Science, 167, 18, 2017 |
| 8 |
Applications of Polynomial Chaos Expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models
Kumar D, Budman H
Chemical Engineering Science, 167, 18, 2017 |
| 9 |
Comparison of stochastic fault detection and classification algorithms for nonlinear chemical processes
Du YC, Budman H, Duever TA
Computers & Chemical Engineering, 106, 57, 2017 |
| 10 |
Identification of Dynamic Metabolic Flux Balance Models Based on Parametric Sensitivity Analysis
Villegas RM, Budman H, Elkamel A
Industrial & Engineering Chemistry Research, 56(8), 1911, 2017 |
