Bhushan Gopaluni
Bhushan Gopaluni
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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
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
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
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
Deep reinforcement learning approaches for process control
SPK Spielberg, RB Gopaluni, PD Loewen
2017 6th international symposium on advanced control of industrial processes …, 2017
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
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
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
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
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
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
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
MPC relevant identification––tuning the noise model
RB Gopaluni, RS Patwardhan, SL Shah
Journal of Process Control 14 (6), 699-714, 2004
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
Nonlinear system identification under missing observations: The case of unknown model structure
RB Gopaluni
Journal of Process Control 20 (3), 314-324, 2010
On simultaneous on-line state and parameter estimation in non-linear state-space models
A Tulsyan, B Huang, RB Gopaluni, JF Forbes
Journal of Process Control 23 (4), 516-526, 2013
A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor
SB Chitralekha, J Prakash, H Raghavan, RB Gopaluni, SL Shah
Journal of Process Control 20 (8), 934-943, 2010
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
Systematic development of a new variational autoencoder model based on uncertain data for monitoring nonlinear processes
K Wang, MG Forbes, B Gopaluni, J Chen, Z Song
IEEE Access 7, 22554-22565, 2019
Particle filtering without tears: A primer for beginners
A Tulsyan, RB Gopaluni, SR Khare
Computers & Chemical Engineering 95, 130-145, 2016
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