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Bhushan Gopaluni
Bhushan Gopaluni
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Title
Cited by
Cited by
Year
Model predictive control in industry: Challenges and opportunities
MG Forbes, RS Patwardhan, H Hamadah, RB Gopaluni
IFAC-PapersOnLine 48 (8), 531-538, 2015
3122015
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
2922016
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
2742022
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
2052012
Deep reinforcement learning approaches for process control
SPK Spielberg, RB Gopaluni, PD Loewen
2017 6th international symposium on advanced control of industrial processes …, 2017
1712017
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
1332008
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
1322019
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
1272016
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
1112006
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
932015
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
862018
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
802018
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
802010
A deep learning architecture for predictive control
SSP Kumar, A Tulsyan, B Gopaluni, P Loewen
IFAC-PapersOnLine 51 (18), 512-517, 2018
792018
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
772022
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
772012
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
722013
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
682022
Mpc relevant identification––tuning the noise model
RB Gopaluni, RS Patwardhan, SL Shah
Journal of Process Control 14 (6), 699-714, 2004
672004
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
622019
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Articles 1–20