Bhushan Gopaluni
Bhushan Gopaluni
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Cited by
Cited by
Model predictive control in industry: Challenges and opportunities
MG Forbes, RS Patwardhan, H Hamadah, RB Gopaluni
IFAC-PapersOnLine 48 (8), 531-538, 2015
Lionsimba: a matlab framework based on a finite volume model suitable for li-ion battery design, simulation, and control
M Torchio, L Magni, RB Gopaluni, RD Braatz, DM Raimondo
Journal of The Electrochemical Society 163 (7), A1192, 2016
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
J Zhu, Y Wang, Y Huang, R Bhushan Gopaluni, Y Cao, M Heere, ...
Nature communications 13 (1), 2261, 2022
Nonlinear Bayesian state estimation: A review of recent developments
SC Patwardhan, S Narasimhan, P Jagadeesan, B Gopaluni, S L Shah
Control Engineering Practice 20 (10), 933-953, 2012
Deep reinforcement learning approaches for process control
SPK Spielberg, RB Gopaluni, PD Loewen
2017 6th international symposium on advanced control of industrial processes …, 2017
A particle filter approach to identification of nonlinear processes under missing observations
RB Gopaluni
The Canadian Journal of Chemical Engineering 86 (6), 1081-1092, 2008
Toward self‐driving processes: A deep reinforcement learning approach to control
S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, R Bhushan Gopaluni
AIChE journal 65 (10), e16689, 2019
State-of-charge estimation in lithium-ion batteries: A particle filter approach
A Tulsyan, Y Tsai, RB Gopaluni, RD Braatz
Journal of Power Sources 331, 208-223, 2016
Identification of chemical processes with irregular output sampling
H Raghavan, AK Tangirala, R Bhushan Gopaluni, SL Shah
Control engineering practice 14 (5), 467-480, 2006
Real-time model predictive control for the optimal charging of a lithium-ion battery
M Torchio, NA Wolff, DM Raimondo, L Magni, U Krewer, RB Gopaluni, ...
2015 American Control Conference (ACC), 4536-4541, 2015
Deep learning of complex batch process data and its application on quality prediction
K Wang, RB Gopaluni, J Chen, Z Song
IEEE Transactions on Industrial Informatics 16 (12), 7233-7242, 2018
Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment
M Sadeghassadi, CJB Macnab, B Gopaluni, D Westwick
Computers & Chemical Engineering 115, 150-160, 2018
Energy optimization in a pulp and paper mill cogeneration facility
DJ Marshman, T Chmelyk, MS Sidhu, RB Gopaluni, GA Dumont
Applied Energy 87 (11), 3514-3525, 2010
A deep learning architecture for predictive control
SSP Kumar, A Tulsyan, B Gopaluni, P Loewen
IFAC-PapersOnLine 51 (18), 512-517, 2018
A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
TM Alabi, EI Aghimien, FD Agbajor, Z Yang, L Lu, AR Adeoye, B Gopaluni
Renewable Energy 194, 822-849, 2022
Fault detection and isolation in stochastic non-linear state-space models using particle filters
F Alrowaie, RB Gopaluni, KE Kwok
Control Engineering Practice 20 (10), 1016-1032, 2012
Optimal control and state estimation of lithium-ion batteries using reformulated models
B Suthar, V Ramadesigan, PWC Northrop, B Gopaluni, ...
2013 American Control Conference, 5350-5355, 2013
Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
NP Lawrence, MG Forbes, PD Loewen, DG McClement, JU Backström, ...
Control Engineering Practice 121, 105046, 2022
Mpc relevant identification––tuning the noise model
RB Gopaluni, RS Patwardhan, SL Shah
Journal of Process Control 14 (6), 699-714, 2004
Design and application of a database-driven PID controller with data-driven updating algorithm
S Wakitani, T Yamamoto, B Gopaluni
Industrial & Engineering Chemistry Research 58 (26), 11419-11429, 2019
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