Varanasi Santhosh Kumar
Verified @gmail.com
Scopus Publications
- Adaptive fault tolerant control of quadrotors: effects of sparse and dense sampling
G. Jithendra Sudheer, Santhosh Kumar Varanasi
International Journal of Dynamics and Control, 2026 - Sparse optimization assisted adaptive and smart hybrid data-driven modeling for process systems
Shubhasmita Behera, Santhosh Kumar Varanasi
Journal of Process Control, 2026 - A wavelet neural network assisted framework for active fault detection and diagnosis of process systems
Piyush Dasrao Hambarde, Santhosh Kumar Varanasi
IFAC Papersonline, 2025
Modern day industries invest heavily on looking for various methods for timely and accurate fault detection and diagnosis. Since training a model to learn all possible faults is challenging and impractical, developing an active learning based methodology which is capable of learning about any new faults arriving in the plant in the due course of operation is the main objective of this paper. This objective is achieved through a two staged methodology where in, an unsupervised learning strategy using one-class SVM is considered in the first stage to detect the presence of a new fault. In the second stage a multi-class classifier of Wavelet Neural Network is utilized to detect the nature of fault. The efficacy of the proposed method is demonstrated on a benchmark Tennessee Eastman Process and the results are compared with the existing methods. - Smart Optimization of Post-Combustion CO2 Capture from a Coal-fired Power Plant: A Bayesian Framework with Wavelet Neural Networks
Prince Kumar Jain, Santhosh Kumar Varanasi
IFAC Papersonline, 2025
Post-Combustion CO 2 capture has been a major focus for decades in efforts to reduce global warming. In this study, CO 2 emissions from a coal power plant are analyzed taking into account an existing process available in Aspen Hysys. In this paper, the main objective is to identify the operating conditions that result in an economical operation of the process. Since the process is complex, instead of relying on a first-principle-based steady-state model, a data-driven approach via a wavelet neural network was considered because of its linearity with respect to the parametric structure. This allows for faster training and provides accurate predictions. Although the model is accurate, due to changes in operating conditions in a process plant, a mismatch between the actual plant output and the predicted model output may exist. To account for this mismatch, Bayesian optimization is employed using Gaussian process regression, which estimates both the mean value and uncertainty of the mismatch. The trust region approach is applied to balance the crucial factors of exploration and exploitation. The efficacy of the proposed method is demonstrated via a Benoit system and a PCC process. - Sparse optimization assisted hybrid data driven modeling of process systems
Abhishek Raj, Ankit Ajay Mandpe, Santhosh Kumar Varanasi
IFAC Papersonline, 2025
Digitization, which involves the adoption of various domain-relevant technologies to create a digital equivalent of physical assets, is the main principle of Industry 4.0. As most process plants such as Waster water treatment process (WWTP) and Post-carbon combustion capture (PCC) process exhibit multi-scale dynamics, identification of models in differential equation form (continuous-time) is advantageous. Further, any underlying physical understanding of the system can be easily captured in this modeling strategy, through appropriate choice of functionality resulting the model Gray-box in nature. Since models in differential equation form are considered, the accuracy of modeling depends on the estimation of derivatives from the sampled data. Therefore, the main objective of this paper is to develop an identification methodology in continuous-time (CT) framework that can capture the physical behavior of the system. To address the issue with the derivative information, the data set is fitted using functions like B-splines subjected to a model-based penalty to ensure that the data fit also satisfies the model of the process. For estimation of a parsimonious model, a sparsity constraint in terms of zero norm on the parameter vector of the model is considered. The efficacy of the method is demonstrated on a Van der Pol oscillator and a Continuous stirred tank reactor (CSTR) system and the results are compared with the existing methods. - Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning
Hongtian Chen, Oguzhan Dogru, Santhosh Kumar Varanasi, Xunyuan Yin, Biao Huang
IEEE Transactions on Cognitive and Developmental Systems, 2024
Multiagent systems are usually large-scaled with a growing degree of intelligence and integration. Direct applications of traditional (centralized) methods will become incompetent for effective process monitoring of multiagent systems. It necessitates the cognitive learning strategies that determine the effective interactions among subsystems or individuals. Therefore, in order to improve the monitoring performance, this article targets the development of a new distributed process monitoring method that has the cognitive learning ability by embedding an adaptive pickup rule. The proposed cognitive learning-based method can reduce the computation loads in both offline and online phases because only necessary information exchange (or communication topology) is involved. Furthermore, the threshold used for system monitoring is obtained by developing a fast search algorithm based on the statistical learning theory. Case studies on the wastewater treatment system, which can be regarded as a typical multiagent system, demonstrate the superiority of the proposed distributed process-monitoring method. - Active learning using one class SVM for fault detection and diagnosis of process systems
Piyush Dasrao Hambarde, Ankit Ajay Mandpe, Santhosh Kumar Varanasi
2024 10th Indian Control Conference Icc 2024 Proceedings, 2024
Today's process industries are investing heavily in the field of process safety and efficiency so as to ensure that the plant is running at the optimal conditions and the overall profit is maximized. Early and fast detection and diagnosis of faults is one of the way of ensuring this objective. Owing to better and reliable data acquiring systems and/ or due to boom of Artificial Intelligence in the era of Industry 4.0, researchers are looking at various ways of incorporating different Machine Learning techniques for development of efficient fault detection and diagnosis methods. As it is not feasible to train a model to detect all possible faults that may arise in a plant, it is required to have a methodology, that is capable enough to learn about any new faults that might occur in the due course of operating a process and is the main objective of this paper. To achieve this objective, a two stage approach wherein, in the first stage, an unsupervised learning strategy using one-class SVM is considered in this paper to detect whether a fault is existing or a new one. In the second stage, a multi-class classifier using Neural networks like Extreme learning machine (ELM) has been employed to detect the nature of fault. The efficacy of the proposed method is demonstrated by considering a Tennessee Eastman process and the results are compared with existing methods. - Smart optimization with PPCR modeling in the presence of missing data, time delay and model-plant mismatch
Alireza Memarian, Santhosh Kumar Varanasi, Biao Huang, Graham Slot
Chemometrics and Intelligent Laboratory Systems, 2023 - Stochastic state-feedback control using homotopy optimization and particle filtering
Venkata Goutham Polisetty, Santhosh Kumar Varanasi, Phanindra Jampana
International Journal of Dynamics and Control, 2022 - Sparse Inverse Covariance Estimation for Causal Inference in Process Data Analytics
Arun Senthil Sundaramoorthy, Santhosh Kumar Varanasi, Biao Huang, Yanjun Ma, Haitao Zhang, Dian Wang
IEEE Transactions on Control Systems Technology, 2022
Causal analysis plays a vital role in determining the underlying relationship among the variables in a system from the data. In this article, the sparse inverse covariance (SIC) estimation is coupled with likelihood score, and a two-step approach is proposed to address the problem of causal analysis. The estimation of SIC matrix for undirected sparse network reconstruction is performed with the $L_{0}$ -norm constraint in the framework of greedy sparse simplex (GSS) algorithm. Furthermore, the GSS algorithm is suitably modified to incorporate the additional constraint of positive semidefiniteness of the inverse covariance matrix. To determine the causal direction among the variables, the likelihood score is computed for the associated variables in the reconstructed network in the second step. The efficacy of the proposed approach for causal analysis is illustrated using numerical examples and an industrial application on prediction of flooding and weeping in a deethanizer column associated with a fluid catalytic cracking unit. From these studies, it is observed that the proposed approach is able to recover causal connections accurately in both cases. Furthermore, the probable reasons for the occurrence of flooding and weeping phenomena in an industrial deethanizer unit are also inferred from the identified causal network. - Sparsity constrained wavelet neural networks for robust soft sensor design with application to the industrial KIVCET unit
Santhosh Kumar Varanasi, Atefeh Daemi, Biao Huang, Graham Slot, Primo Majoko
Computers and Chemical Engineering, 2022 - Data-driven self-optimization of processes in the presence of the model-plant mismatch
Alireza Memarian, Santhosh Kumar Varanasi, Biao Huang
IFAC Papersonline, 2022 - MPC Model-Plant-Mismatch Detection Through Slow Feature Analysis Preprocessing with Industrial Application
Cameron Dyson, Santhosh Kumar Varanasi, Graham Slot, Primo Majoko, Biao Huang
2022 IEEE International Symposium on Advanced Control of Industrial Processes Adconip 2022, 2022 - Mixture robust semi-supervised probabilistic principal component regression with missing input data
Alireza Memarian, Santhosh Kumar Varanasi, Biao Huang
Chemometrics and Intelligent Laboratory Systems, 2021 - Minimum attention stochastic control with homotopy optimization
Santhosh Kumar Varanasi, Phanindra Jampana, C. P. Vyasarayani
International Journal of Dynamics and Control, 2021 - Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions
Faraz Amjad, Santhosh Kumar Varanasi, Biao Huang
IEEE Transactions on Instrumentation and Measurement, 2021 - Convergence of Particle Filter for Output Feedback Control
European Control Conference 2020 Ecc 2020, 2020 - Nuclear norm subspace identification of continuous time state–space models with missing outputs
Santhosh Kumar Varanasi, Phanindra Jampana
Control Engineering Practice, 2020 - Sparsity constrained reconstruction for electrical impedance tomography
Ganesh Teja Theertham, Santhosh Kumar Varanasi, Phanindra Jampana
IFAC Papersonline, 2020 - Input Design for Continuous Time Output Error Models
Santhosh Kumar Varanasi, Chaitanya Manchikatla, Phanindra Jampana
Industrial and Engineering Chemistry Research, 2019 - Comparative Study of Parsimonious NARX Models for Three Phase Separator
Santhosh Kumar Varanasi, Siddhartha Varma Tirumalaraju, Phanindra Jampana
2019 5th Indian Control Conference Icc 2019 Proceedings, 2019 - Error bounds for identification of a class of continuous LTI systems
Venkata Goutham Polisetty, Santhosh Kumar Varanasi, Phanindra Jampana
IFAC Papersonline, 2019 - Sparse optimization for image reconstruction in Electrical Impedance Tomography
Santhosh Kumar Varanasi, Chaitanya Manchikatla, Venkata Goutham Polisetty, Phanindra Jampana
IFAC Papersonline, 2019 - Identification of neuronal networks from calcium oscillation data
Santhosh Kumar Varanasi, Sarpras Swain, Lopamudra Giri, Phanindra Jampana
IFAC Papersonline, 2019 - Identification of parsimonious continuous time LTI models with applications
Santhosh Kumar Varanasi, Phanindra Jampana
Journal of Process Control, 2018 - Topology identification of sparse networks of continuous time systems
Santhosh Kumar Varanasi, Phanindra Jampana
2018 Indian Control Conference Icc 2018 Proceedings, 2018 - Nuclear Norm Subspace Identification Of Continuous Time State-Space Models
Santhosh Kumar Varanasi, Phanindra Jampana
2018 - Parameter Estimation and Model Order Identification of LTI Systems
Santhosh Kumar Varanasi, Phanindra Jampana
IFAC Papersonline, 2016
RECENT SCHOLAR PUBLICATIONS
- Adaptive fault tolerant control of quadrotors: effects of sparse and dense sampling: G. Jithendra Sudheer. and SK Varanasi
G Jithendra Sudheer, SK Varanasi
International Journal of Dynamics and Control 14 (5), 185 , 2026
2026 - Sparse optimization assisted adaptive and smart hybrid data-driven modeling for process systems
S Behera, SK Varanasi
Journal of Process Control 159, 103642 , 2026
2026 - Minimum Attention Control (MAC) in a Receding Horizon Framework with Applications
TG Teja, SK Varanasi, P Jampana
arXiv e-prints, arXiv: 2507.20835 , 2025
2025 - Sparse optimization assisted hybrid data driven modeling of process systems
A Raj, AA Mandpe, SK Varanasi
IFAC-PapersOnLine 59 (6), 313-318 , 2025
2025
Citations: 1 - Smart Optimization of Post-Combustion CO2 Capture from a Coal-fired Power Plant: A Bayesian Framework with Wavelet Neural Networks
PK Jain, SK Varanasi
IFAC-PapersOnLine 59 (6), 439-444 , 2025
2025
Citations: 1 - A wavelet neural network assisted framework for active fault detection and diagnosis of process systems
PD Hambarde, SK Varanasi
IFAC-PapersOnLine 59 (6), 151-156 , 2025
2025 - Active Learning Using One Class SVM for Fault Detection and Diagnosis of Process Systems
PD Hambarde, AA Mandpe, SK Varanasi
2024 Tenth Indian Control Conference (ICC), 397-402 , 2024
2024
Citations: 1 - Smart optimization with PPCR modeling in the presence of missing data, time delay and model-plant mismatch
A Memarian, SK Varanasi, B Huang, G Slot
Chemometrics and Intelligent Laboratory Systems 237, 104812 , 2023
2023
Citations: 12 - Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning
H Chen, O Dogru, SK Varanasi, X Yin, B Huang
IEEE Transactions on Cognitive and Developmental Systems 16 (1), 8-19 , 2022
2022
Citations: 13 - MPC Model-Plant-Mismatch Detection Through Slow Feature Analysis Preprocessing with Industrial Application
C Dyson, SK Varanasi, G Slot, P Majoko, B Huang
2022 IEEE International Symposium on Advanced Control of Industrial … , 2022
2022
Citations: 2 - Stochastic state-feedback control using homotopy optimization and particle filtering
VG Polisetty, SK Varanasi, P Jampana
International Journal of Dynamics and Control 10 (3), 942-955 , 2022
2022
Citations: 4 - Sparsity constrained wavelet neural networks for robust soft sensor design with application to the industrial KIVCET unit
SK Varanasi, A Daemi, B Huang, G Slot, P Majoko
Computers & Chemical Engineering 159, 107695 , 2022
2022
Citations: 12 - Data-driven self-optimization of processes in the presence of the model-plant mismatch
A Memarian, SK Varanasi, B Huang
IFAC-PapersOnLine 55 (7), 532-537 , 2022
2022
Citations: 10 - Sparse inverse covariance estimation for causal inference in process data analytics
AS Sundaramoorthy, SK Varanasi, B Huang, Y Ma, H Zhang, D Wang
IEEE Transactions on Control Systems Technology 30 (3), 1268-1280 , 2021
2021
Citations: 14 - Mixture robust semi-supervised probabilistic principal component regression with missing input data
A Memarian, SK Varanasi, B Huang
Chemometrics and Intelligent Laboratory Systems 214, 104315 , 2021
2021
Citations: 32 - Minimum attention stochastic control with homotopy optimization
SK Varanasi, P Jampana, CP Vyasarayani
International Journal of Dynamics and Control 9 (1), 266-274 , 2021
2021
Citations: 4 - Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions
F Amjad, SK Varanasi, B Huang
IEEE Transactions on Instrumentation and Measurement 70, 1-8 , 2021
2021
Citations: 14 - Convergence of Particle Filter for Output Feedback Control
VG Polisetty, SK Varanasi, P Jampana
2020 European Control Conference (ECC), 1745-1750 , 2020
2020 - Nuclear norm subspace identification of continuous time state–space models with missing outputs
SK Varanasi, P Jampana
Control Engineering Practice 95, 104239 , 2020
2020
Citations: 24 - Sparsity Constrained Reconstruction for Electrical Impedance Tomography
GT Theertham, SK Varanasi, P Jampana
IFAC-PapersOnLine 53 (2), 355-360 , 2020
2020
Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
- Mixture robust semi-supervised probabilistic principal component regression with missing input data
A Memarian, SK Varanasi, B Huang
Chemometrics and Intelligent Laboratory Systems 214, 104315 , 2021
2021
Citations: 32 - Nuclear norm subspace identification of continuous time state–space models with missing outputs
SK Varanasi, P Jampana
Control Engineering Practice 95, 104239 , 2020
2020
Citations: 24 - Sparse inverse covariance estimation for causal inference in process data analytics
AS Sundaramoorthy, SK Varanasi, B Huang, Y Ma, H Zhang, D Wang
IEEE Transactions on Control Systems Technology 30 (3), 1268-1280 , 2021
2021
Citations: 14 - Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions
F Amjad, SK Varanasi, B Huang
IEEE Transactions on Instrumentation and Measurement 70, 1-8 , 2021
2021
Citations: 14 - Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning
H Chen, O Dogru, SK Varanasi, X Yin, B Huang
IEEE Transactions on Cognitive and Developmental Systems 16 (1), 8-19 , 2022
2022
Citations: 13 - Smart optimization with PPCR modeling in the presence of missing data, time delay and model-plant mismatch
A Memarian, SK Varanasi, B Huang, G Slot
Chemometrics and Intelligent Laboratory Systems 237, 104812 , 2023
2023
Citations: 12 - Sparsity constrained wavelet neural networks for robust soft sensor design with application to the industrial KIVCET unit
SK Varanasi, A Daemi, B Huang, G Slot, P Majoko
Computers & Chemical Engineering 159, 107695 , 2022
2022
Citations: 12 - Data-driven self-optimization of processes in the presence of the model-plant mismatch
A Memarian, SK Varanasi, B Huang
IFAC-PapersOnLine 55 (7), 532-537 , 2022
2022
Citations: 10 - Parameter Estimation and Model Order Identification of LTI Systems
SK Varanasi, P Jampana
IFAC-PapersOnLine 49 (7), 1002-1007 , 2016
2016
Citations: 10 - Sparse optimization for image reconstruction in Electrical Impedance Tomography
SK Varanasi, C Manchikatla, VG Polisetty, P Jampana
IFAC-PapersOnLine 52 (1), 34-39 , 2019
2019
Citations: 7 - Identification of parsimonious continuous time LTI models with applications
SK Varanasi, P Jampana
Journal of Process Control 69, 128-137 , 2018
2018
Citations: 6 - Comparative Study of Parsimonious NARX Models for Three Phase Separator
SK Varanasi, SV Tirumalaraju, P Jampana
2019 Fifth Indian Control Conference (ICC), 430-435 , 2019
2019
Citations: 5 - Stochastic state-feedback control using homotopy optimization and particle filtering
VG Polisetty, SK Varanasi, P Jampana
International Journal of Dynamics and Control 10 (3), 942-955 , 2022
2022
Citations: 4 - Minimum attention stochastic control with homotopy optimization
SK Varanasi, P Jampana, CP Vyasarayani
International Journal of Dynamics and Control 9 (1), 266-274 , 2021
2021
Citations: 4 - Sparsity Constrained Reconstruction for Electrical Impedance Tomography
GT Theertham, SK Varanasi, P Jampana
IFAC-PapersOnLine 53 (2), 355-360 , 2020
2020
Citations: 4 - MPC Model-Plant-Mismatch Detection Through Slow Feature Analysis Preprocessing with Industrial Application
C Dyson, SK Varanasi, G Slot, P Majoko, B Huang
2022 IEEE International Symposium on Advanced Control of Industrial … , 2022
2022
Citations: 2 - Error Bounds for Identification of a Class of Continuous LTI Systems
VG Polisetty, SK Varanasi, P Jampana
IFAC-PapersOnLine 52 (1), 418-423 , 2019
2019
Citations: 2 - Sparse optimization assisted hybrid data driven modeling of process systems
A Raj, AA Mandpe, SK Varanasi
IFAC-PapersOnLine 59 (6), 313-318 , 2025
2025
Citations: 1 - Smart Optimization of Post-Combustion CO2 Capture from a Coal-fired Power Plant: A Bayesian Framework with Wavelet Neural Networks
PK Jain, SK Varanasi
IFAC-PapersOnLine 59 (6), 439-444 , 2025
2025
Citations: 1 - Active Learning Using One Class SVM for Fault Detection and Diagnosis of Process Systems
PD Hambarde, AA Mandpe, SK Varanasi
2024 Tenth Indian Control Conference (ICC), 397-402 , 2024
2024
Citations: 1
