Publications

2019

1. Assessment of Simplifications to a Pseudo–2D Electrochemical Model of Li-ion Batteries Kong, X.R., Wetton, B., and Gopaluni, R.B. 2019. In Proceedings of DYCOPS Conference, , 946-951. [Abstract] [PDF]

   Lithium-ion batteries are ubiquitous in modern society. Their high power and energy density compared to other forms of electrochemical energy storage make them very popular in a wide range of applications. To ensure safe, prolonged, and reliable operations, significant research effort has been put into understanding, modelling, and predicting the key limiting phenomena, which has led to various battery models with different levels of complexity and prediction capabilities. This work focuses on implementing the pseudo-two-dimensional (P2D) model, the most widely accepted electrochemical model on lithium-ion batteries. The unparalleled prediction abilities of the P2D model, however, are over shadowed by its high complexity. Thus, much of this work focuses on model reduction to shorten effective simulation time. In the end, four model reductions have been identified and successfully implemented. Comparisons to the full model at 1C, 2C and 5C discharge rates are reported.

2. Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction Wang, K., Gopaluni, R.B., Chen, J., and Song, Z. 2019. IEEE Transactions on Industrial Informatics, To Appear , . [Abstract] [PDF] [DOI]

   Batch process quality prediction is an important application in the chemical industry. The complexity of batch processes is characterized by multiphase, nonlinearity, dynamics and uneven durations, so modeling of batch processes is very difficult. Moreover, there are other challenges in quality prediction. As the process trajectories over the whole running duration make contributions to the final targets, the prediction issue embraces tremendously high-dimensional inputs but very low-dimensional outputs. That is, the prediction suffers from a severe dimensional imbalance between inputs and outputs. To conquer these difficulties, this paper proposes a new deep learning-based framework for complex features and quality prediction. The long short-term memory is used to extract quality-relevant hidden features from a long-time sequence in each phase, significantly reducing the predictor dimensions. And these features from different phases are further integrated and compressed by a stacked auto-encoder. A practical industrial example testifies to the efficacy of the proposed framework.

3. Identification of Symmetric Noncausal Processes Lu, Q., Loewen, P.D., Gopaluni, R.B., Forbes, M.G., Backstrom, J.U., Dumont, G.A., and Davies, M.S. 2019. Automatica, 103 , 515-530. [Abstract] [PDF]

   We propose a maximum likelihood estimation approach for the identification of symmetric noncausal models. Such models are used to represent the cross-directional dynamic response of many industrial processes that are generally modeled with a high- dimensional gain matrix. Reducing the number of parameters in a noncausal model can significantly reduce the uncertainty associated with parameter estimates. We adapt the maximum likelihood method to treat symmetric noncausal models by showing that every symmetric noncausal process admits a spectrally equivalent causal model. It is then proved that the maximum likelihood estimate of this causal model converges to that of the original noncausal model. We present an iterative identification algorithm to efficiently estimate the parameters in noncausal models. Finally, we show that the parameter covariance estimate obtained from the causal model also converges to that of the noncausal model, which lays a foundation for optimal input design in noncausal processes. Several numerical examples illustrate the effectiveness of the proposed algorithm.

4. Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes Wang, K., Forbes, M.G., Gopaluni, R.B., Chen, J., and Song, Z. 2019. IEEE Access, 7 , 22554-22565. [Abstract] [PDF] [DOI]

   Deep learning models have been applied to industrial process fault detection because of their ability to approximate complex nonlinear behavior. They have been proven to outperform shallow neural network models. However, there are no good guidelines on how to build these deep models. Therefore, a good deep model is often constructed through a trial and error exercise. It is not easy to interpret the model because of features that do not have any physical interpretation. In addition, latent variables (or features) in a deep model are not independent. This causes features to overlap with each other, resulting in challenges in evaluating distributions of features and designing suitable monitoring indices. Lastly, typical deep learning models in process monitoring are used in a deterministic manner and do not automatically provide confidence levels for each decision. In this paper, a variational auto-encoder is utilized to develop a framework for monitoring uncertain nonlinear processes. The learned latent variables are guaranteed to be independent (or orthogonal) of each other under a specific optimization objective with constraints. The proposed method provides density estimates of latent variables and residuals intead of point estimates. The density functions are used to design appropriate indices for monitoring. A simulation example and an industrial paper machine example are presented to validate the effectiveness of the proposed method.

5. Data-Driven Dynamic Modeling and Online Monitoring for Multiphase and Multimode Batch Processes with Uneven Batch Durations Wang, K., Forbes, L, Chen, J., Song, Z., and Gopaluni, R.B. 2019. Industrial and Engineering Chemistry Research (Prof. Dominique Bonvin - Festschrift), 7 , 22554-22565. [Abstract] [PDF] [DOI]

   Batch processes are often characterized by piecewise linear dynamics due to varying operating conditions. Multiphase and multimode modeling of batch processes is a common technique that offers insight into the process operation and improved online monitoring. However, existing monitoring methods have several drawbacks such as neglecting process dynamics, requiring separate treatment of transient behavior, and relying on uniformity between batches. These challenges are addressed here by proposing a new strategy to construct a dynamic model for monitoring multimode and multiphase batch processes. A linear dynamic system partitions phases and describes local dynamic behavior before modes of operation are clustered based on the global differences between batches. Lastly, an expectation maximization algorithm for multibatch data in the same mode is applied to estimate phase parameters. Process monitoring results on a benchmark penicillin fermentation data set suggest a significant improvement over previous methods.

6. Machine Direction Adaptive Control on a Paper Machine Rippon, L. D., Lu, Q., Forbes, M.G., Gopaluni, R.B., Loewen, P.D., and Backstrom, J.U. 2019. Industrial and Engineering Chemistry Research (Prof. Sirish Shah - Festschrift), 7 , 22554-22565. [Abstract] [PDF] [DOI]

   Control of industrial sheet and film processes involves separate controllers and ac- tuators for minimizing both temporal variations along the machine direction (MD) and spatial variations along the cross direction (CD). Model-based control methods such as model predictive control (MPC) have gained widespread implementation for con- trolling both the MD and CD processes. One limitation of model-based methods is that changes in the true process pose significant identification challenges for operators which are often resolved with costly open loop identification experiments. The pre- dominant industrial measurement technology acquires a signal of mixed MD and CD variations that requires separation. This work compares various model-free approaches for MD-CD separation as a prerequisite to effective MD control. To address the chal- lenges of model-based control this paper introduces an adaptive control method for the MD process. Closed loop identification experiments are conducted and compared to benchmarks on an industrial paper machine simulator.

7. Univariate model-based deadband alarm design for nonlinear processes Tulsyan, A., and Gopaluni, R.B. 2019. Industrial and Engineering Chemistry Research (Prof. Sirish Shah - Festschrift), To Appear , . [Abstract] [PDF] [DOI]

   Alarm design is an important industrial problem with significant implications to safety and performance. Standard alarm design algorithms are based on the assumption that the data are uncorrelated and stationary. In this article, we relax these assumptions and develop a novel approach to design alarms for processes modeled as a stochastic nonlinear time-series model. In particular, we develop an algorithm to design deadband alarms by minimizing the false and missed alarm rates. The resulting algorithm is illustrated through extensive simulations on a reactor system.

8. Development and Characterization of a Non- Intrusive Magnetic Sensor to Measure Wear in Centrifugal Pumps Bohn, B., Khoie, R., Gopaluni, R.B., Olson, J.A., and Stoeber, B. 2019. IEEE Sensors, To Appear , . [Abstract] [PDF] [DOI]

   A magnetic sensor is designed and fabricated that allows for impeller blade wear measurement while a centrifugal pump is in operation. The sensor can be installed on existing pumps and does not require structural modification. Over time, as the pump impeller erodes, the gap between the impeller and pump side plate increases from an unworn width of 0.65 mm to 2.50 mm, the maximum allowable wear on the testbed pump used for experimental validation. The extent of the impeller wear is determined by measuring over time the change in reluctance of a magnetic circuit passing through the eroding area. The sensor flux guide mechanism has a relative magnetic permeability of 10,000. Flux through the circuit is driven by an inductive coil excited with a 1.0 V AC voltage signal at 70 Hz. As wear occurs, the impeller gap grows and the total reluctance of the magnetic circuit increases, which cause the peak inductance of the coil to drop. Trial data is collected at a sampling frequencies up to 500 kHz and then assessed in the frequency domain using Fast Fourier transform (FFT). The amplitude of the FFT signal at the pump’s rotational frequency is then considered to estimate wear. Sampling data for one second at 500 kHz, the sensor demonstrates a maximum signal to noise ratio of 17.8 dB with an average sensitivity of 0.022 mV/mm and resolution of 0.38 mm.

9. Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control Speilberg, S., Tulsyan, A., Lawrence, N.P., Loewen, P.D., and Gopaluni, R.B. 2019. AIChE Journal, To Appear , . [Abstract] [PDF] [DOI]

   Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a common practice to monitor controller performance continuously and to initiate a remedial model re-identification procedure in the event of performance degradation. Such procedures are typically complicated and resource-intensive, and they often cause costly interruptions to normal operations. In this paper, we exploit recent developments in reinforcement learning and deep learning to develop a novel adaptive, model-free controller for general discrete-time processes. The DRL controller we propose is a data-based controller that learns the control policy in real time by merely interacting with the process. The effectiveness and benefits of the DRL controller are demonstrated through many simulations.

10. Design and Application of a Database-Driven PID Controller with Data-Driven Updating Algorithm Wakitani, S., Yamamoto, T., and Gopaluni, R.B. 2019. Industrial and Engineering Chemistry Research, To Appear , . [Abstract] [PDF] [DOI]

    This study deals with an updating algorithm for a database-driven proportional-integral-derivative (DD-PID) controller that uses a database for tuning control parameters.PID controllers are still used in many process systems including chemical processes.However, if systems exhibit nonlinearity, PID controllers with fixed PID parameters cannot achieve the desired control performance when the systems’ equilibrium points are changed by set point changes.The DD-PID controller has been proposed to solve this problem.This controller can realize good control performance for nonlinear systems because it updates the PID parameters in its database so that the control performance around each equilibrium point has a desired characteristic.However, many experiments have to be performed for this controller to obtain a suitable database.This paper proposes a new offline database updating method called data-driven extended fictitious reference iterative tuning (DD-E-FRIT) method.In this method, the E-FRIT method, which is a direct control parameter tuning method for the linear system, is applied to the updating rule of the DD-PID controller.The DD-E-FRIT method can update a database offline and obtain the database for realizing the desired tracking property of a closed-loop system by storing one-shot operating data into the database.

11. A Comparison of Clustering and Prediction Methods for Identifying Key Chemical-Biological Features Affecting Bioreactor Performance Yiting, T., Baldwin, S., Lim, S., and Gopaluni, R.B. 2019. Processes, To Appear , . [Abstract] [PDF] [DOI]

    Chemical-biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis strategies, which include three predictive models (Random Forests, Support Vector Machines, and Neural Networks), three clustering models (hierarchical, Gaussian mixtures, and Dirichlet mixtures), and two feature selection approaches (Mean Decrease in Accuracy and its conditional variant). These methods not only predict the bioreactor outcome with sufficient accuracy, but the important features correlated with said outcome are also identified. The novelty of this work lies in the extensive exploration and critique of a wide arsenal of methods, instead of single methods as observed in many papers of similar nature. The results show that Random Forest models predict test set outcomes with the highest accuracy. The identified contributory features include process features which agree with domain knowledge, as well as several different biomarker OTUs. The results reinforce the notion that both chemical and biological features significantly affect the bioreactor performance. However, they also indicate that the quality of the biological features can be improved by considering non-clustering methods, which may better represent the true behaviour within the OTU communities.

12. An economic model predictive control framework for mechanical pulping processes Tian, H., Lu, Q., Gopaluni, R.B., Zavala, V.M., and Olson, J.A. 2019. Control Engineering Practice, 85 , 100-109. [Abstract] [PDF] [DOI]

    We develop a multi-objective economic model predictive control (m-econ MPC) framework to control and optimize a nonlinear mechanical pulping (MP) process. M-econ MPC interprets economic MPC as a multi-objective optimization problem that trades off economic and set-point tracking performance. This interpretation allows us to construct a stabilizing constraint that guarantees closed-loop stability. The framework infers unmeasured states of the MP process (associated with product consistency) by using a moving horizon estimator (MHE). The MP process dynamics are described by using a nonlinear Wiener model. Examples from a two-stage high-consistency MP process are employed to demonstrate that significant improvements in economic performance are achievable.

2018

1. State of Health Estimation for Lithium-Ion Batteries Kong, X., Bonakdarpour, A., Wetton, B.T., Wilkinson, D.P., and Gopaluni, R.B. 2018. In Proceedings of ADCHEM Conference, 51 , 661-665. [Abstract] [PDF] [DOI]

   The state of health (SoH) of lithium-ion batteries and battery packs must be monitored effectively for abuse to prevent failure and accidents, and to prolong the useful lifetime of the batteries. Many studies have suggested that temperature and discharge/charge current rate are the primary factors causing battery aging. However, there is yet to be a concrete mathematical model to predict the battery SoH. In this study, we introduce two SoH prediction models: the decreasing battery V0+ model, the increasing CV charge capacity model. Additionally, we derive a simple thermal model for the cell based on variation of temperature data.

2. Design and Assessment of Delay Timer Alarm Systems for Nonlinear Chemical Processes Tulsyan, A., Alrowaie, F., and Gopaluni, R.B. 2018. AIChE Journal, 64((1) , 77-90. [Abstract] [PDF] [DOI]

   In process and manufacturing industries, alarm systems play a critical role in ensuring safe and efficient operations. The objective of a standard industrial alarm system is to detect undesirable deviations in process variables as soon as they occur. Fault detection and diagnosis (FDD) systems often need to be alerted by an industrial alarm system; however, poorly designed alarms often lead to alarm flooding and other undesirable events. In this paper, we consider the problem of industrial alarm design for processes represented by stochastic nonlinear time-series models. The alarm design for such complex processes faces three important challenges: 1) industrial processes exhibit highly nonlinear behavior; 2) state variables are not precisely known (modeling error); and 3) process signals are not necessarily Gaussian, stationary or uncorrelated. In this paper, a procedure for designing a delay timer alarm configuration is proposed for the process states. The proposed design is based on minimization of the rate of false and missed alarm rates – two common performance measures for alarm systems. To ensure the alarm design is robust to any non-stationary process behavior, an expected-case and a worst-case alarm designs are proposed. Finally, the efficacy of the proposed alarm design is illustrated on a non-stationary chemical reactor problem.

3. Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment Sadeghassadi, M., Macnab, C.J.B., Gopaluni, R.B., and Westwick, D. 2018. Computers & Chemical Engineering, 115 , 150–160. [Abstract] [PDF] [DOI]

   This paper addresses both the design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in a biological wastewater treatment process. Although exact knowledge of influent changes during rain/storm events is unrealistic, we take advantage of the fact that during dry weather conditions the influent changes are periodic and thus predictable. Specifically, a nonlinear optimization procedure utilizes dry weather data to decide on a nominal fixed setpoint, or a weighting gain, or both; during weather events an algorithm uses the optimization solution(s) together with the ammonium predictions to adjust the setpoint dynamically (responding appropriately to significant changes in the influent). A constrained nonlinear neural-network model predictive control tracks the setpoint. Simulations with the BSM1 compare several variations of the proposed methods to a fixed-setpoint PI control, demonstrating improvement in effluent quality or reduction in energy use, or both.

4. Locality Preserving Discriminative Canonical Variate Analysis for Fault Diagnosis Lu, Q., Jiang, B., Gopaluni, R.B., Loewen, P.D., and Braatz, R.D. 2018. Computers & Chemical Engineering, 117 , 309-319. [Abstract] [PDF] [DOI]

   This paper proposes a locality preserving discriminative canonical variate analysis (LP-DCVA) scheme for fault diagnosis. The LP-DCVA method provides a set of optimal projection vectors that simultaneously maximizes the within-class mutual canonical correlations, minimizes the between-class mutual canonical correlations, and preserves the local structures present in the data. This method inherits the strength of canonical variate analysis (CVA) in handling high-dimensional data with serial correlations and the advantages of Fisher discriminant analysis (FDA) in pattern classification. Moreover, the incorporation of locality preserving projection (LPP) in this method makes it suitable for dealing with nonlinearities in the form of local manifolds in the data. The solution to the proposed approach is formulated as a generalized eigenvalue problem. The effectiveness of the proposed approach for fault classification is verified by the Tennessee Eastman process. Simulation results show that the LP-DCVA method outperforms the FDA, dynamic FDA (DFDA), CVA-FDA, and localized DFDA (L-DFDA) approaches in fault diagnosis.

5. Sparse canonical variate analysis approach for process monitoring Lu, Q., Jiang, B., Gopaluni, R.B., Loewen, P.D., and Braatz, R.D. 2018. Journal of Process Control, 71 , 90-102. [Abstract] [PDF] [DOI]

   Canonical variate analysis (CVA) has shown its superior performance in statistical process monitoring due to its effectiveness in handling high-dimensional, serially, and cross-correlated dynamic data. A restrictive condition for CVA is that the covariance matrices of dependent and independent variables must be invertible, which may not hold when collinearity between process variables exists or the sample size is small relative to the number of variables. Moreover, CVA often yields dense canonical vectors that impede the interpretation of underlying relationships between the process variables. This article employs a sparse CVA (SCVA) technique to resolve these issues and applies the method to process monitoring. A detailed algorithm for implementing SCVA and its formulation in fault detection and identification are provided. SCVA is shown to facilitate the discovery of major structures (or relationships) among process variables, and assist in fault identification by aggregating the contributions from faulty variables and suppressing the contributions from normal variables. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process.

6. Pattern and Knowledge Extraction using Process Data Analytics: A Tutorial Tsai, Y., Lu, Q., Rippon, L., Lim, S., Tulsyan, A., and Gopaluni, R.B. 2018. In Proceedings of ADCHEM Conference, 51 , 13-18. [Abstract] [PDF] [DOI]

   Traditional techniques employed by process control engineers require a significant update in order to handle the increasing complexity of modern processes. Conveniently, advances in statistical machine learning and distributed computation have led to an abundance of new techniques that are potentially suitable for advanced analysis of chemical processes. In this tutorial we briefly introduce popular data analytics techniques and discuss their potential for application in the chemical process industries. Specifically, this tutorial provides control engineers with a practical understanding of data analytics techniques, some of which may be unconventional in chemical process applications. Each of the presented techniques can potentially extract valuable knowledge from raw data, which can then be utilized to make smarter process control decisions.

7. A Deep Learning Architecture for Predictive Control Spielberg, S., Tulsyan, A., Gopaluni, R.B., and Loewen, P.D. 2018. In Proceedings of ADCHEM Conference, Shenyang, China, 51 , 512-517. [Abstract] [PDF] [DOI]

   Model predictive control (MPC) is a popular control strategy that computes control actions by solving an optimization problem in real-time. Uncertainty and nonlinearity of a process, and the non-convexity of the resulting optimization problem can make online implementation of MPC nontrivial. Consequently, MPC is most often used in processes where the time constants are large and/or high-performance computing support is available. We propose a deep neural network (DNN) controller architecture to reduce the computational cost of implementing an MPC. This is done by training a DNN controller on simulated input-output data from a well-designed MPC. The online implementation of a DNN controller does not require solving an optimization problem. Once the DNN is trained, the MPC is fully replaced with the DNN controller. The benefits of this approach are illustrated through a simulated example.

8. An Efficient Model Based Control Algorithm for the Determination of an Optimal Control Policy for a Constrained Stochastic Linear System Prakash, J., Zamar, D., Gopaluni, R.B., and Kwok, E. 2018. In Proceedings of ADCHEM Conference, 51 , 584-589. [Abstract] [PDF] [DOI]

   In this paper, the authors have proposed an ensemble Kalman filter based stochastic model predictive control algorithm to determine an optimal control policy at every sampling time instant for a constrained stochastic linear system. To determine an optimal control policy for the constrained linear system affected by random disturbances and measurements corrupted by random noise, the authors have minimized the uncertain objective function, subject to uncertain state & output constraints and deterministic input constraints using the quantile based scenario analysis approach. In this work, ensemble Kalman filter is being employed, to generate a recursive estimate of states of the constrained stochastic linear system. The number of scenarios is considered to be equivalent to that of number of sample points used in the ensemble Kalman filter. Each scenario is viewed as one realization of the process noise, measurement noise over the prediction horizon as well as the ith sample point of the state estimate at the beginning of the prediction horizon generated by the ensemble Kalman filter. Simulation studies have been carried out to assess the efficacy of the proposed control scheme on the simulated model of the constrained single- input and single-output linear stochastic system.

9. A switching strategy for adaptive state estimation Tulsyan, A., Khare, S., Huang, B., Gopaluni, R.B., and Forbes, F.J. 2018. Signal Processing, 143 , 371-380. [Abstract] [PDF] [DOI]

   This paper develops a switching strategy for adaptive state estimation in systems represented by nonlinear, stochastic, discrete- time state space models (SSMs). The developed strategy is motivated by the fact that there is no single Bayesian estimator that is guaranteed to perform optimally for a given nonlinear system and under all operating conditions. The proposed strategy constructs a bank of plausible Bayesian estimators for adaptive state estimation, and then switches between them based on their performance. The performance of a Bayesian estimator is assessed using a performance metric derived from the posterior Cramer- Rao lower bound (PCRLB). It is shown that the switching strategy is stable, and yields estimates that are at least as good as any individual estimator in the bank. The efficacy of the switching strategy is illustrated on a practical simulation example.

2017

1. A Constrained k-means and Nearest Neighbor Approach for Route Optimization: With an application to the Bale Collection Zamar, D., Gopaluni, R.B., and Sokhansanj, S. 2017. In Proceedings of IFAC World Congress, , 12125-12130. [Abstract] [PDF] [Presentation]

   The bale collection problem (BCP) appears after harvest operations of agricultural crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation systems in autonomous agricultural vehicles and machines are able to provide accurate data for reliable bale collection planning. This paper presents a constrained k-means algorithm and nearest neighbour approach to the BCP, which minimizes travel time and hence fuel consumption. The constraints imposed on the k-means procedure is not the usual condition that certain groups of objects must be clustered together, but rather that the cluster centers must lie on valid locations, which may be specified as functions or sets of points. The algorithmic route generation provides the basis for a navigation tool dedicated to loaders and bale wagons. The approach is experimentally tested on a simulated study area similar to those found in real situations.

2. Model-Plant Mismatch Detection with Support Vector Machines Lu, Q., Gopaluni, R.B., Forbes, M.G., Loewen, P.D., Backstrom, J.U., and Dumont, G.A. 2017. In Proceedings of IFAC World Congress, , 7993-7998. [Abstract] [PDF]

   We propose a model-plant mismatch (MPM) detection strategy based on a novel closed-loop identification approach and one-class support vector machine (SVM) learning technique. With this scheme we can monitor MPM and noise model change separately, thus discriminating the MPM from noise model change. Another advantage of this approach is that it is applicable to routine operating data that may lack any external excitations. Theoretical derivations on the closed-loop identification method are provided in this paper, showing that it can furnish a consistent parameter estimate for the process model even in the case where a priori knowledge about the true noise model structure is not available. We build an SVM model based on process and noise model estimates from training data to predict the occurrence of MPM in the testing data. An example on paper machine control is provided to verify the proposed MPM detection framework.

3. Noncausal Modeling and Closed-Loop Optimal Input Design for Cross-Directional Processes of Paper Machines Lu, Q., Gopaluni, R.B., Forbes, M.G., Loewen, P.D., Backstrom, J.U., and Dumont, G.A. 2017. In Proceedings of American Control Conference, , 2837-2842. [Abstract] [PDF] [Presentation]

   We propose to use noncausal transfer functions to model the spatial behavior of cross-directional (CD) processes so as to circumvent the high-dimensionality of a causal transfer function. This noncausal representation is shown to have a causal-equivalent form. We prove that the covariance of maximum likelihood estimate of the causal-equivalent model asymptotically converges to that of the noncausal model. This result is then used to design optimal inputs for the original noncausal model of the CD process. An illustrative example is provided to highlight the advantage of using optimally designed excitation signal for CD closed-loop identification over white noise excitation or the current industrial practice of spatial bump excitation.

4. Deep Reinforcement Learning Approaches for Process Control Spielberg, S.P., Gopaluni, R.B., and Loewen, P.D. 2017. In Proceedings of ADCONIP, , 201-206. [Abstract] [PDF]

   In this work, we have extended the current success of deep learning and reinforcement learning to process control problems. We have shown that if reward hypothesis functions are formulated properly, they can be used for industrial process control. The controller setup follows the typical reinforcement learning setup, whereby an agent (controller) interacts with an environment (process) through control actions and receives a reward in discrete time steps. Deep neural networks serve as function approximators and are used to learn the control policies. Once trained, the learned network acquires a policy that maps system output to control actions. Though the policies are not explicitly specified, the deep neural networks were able to learn policies that are different from the traditional controllers. We evaluated our approach on Single Input Single Output Systems (SISO), Multi-Input Multi-Out.

5. Performance Assessment of Cross-Directional Control for Paper Machines Lu, Q., Forbes, M.G., Gopaluni, R.B., Loewen, P.D., Backström, J.U., and Dumont, G.A. 2017. IEEE Transactions on Control Systems Technology, 25 (1), 208–221. [Abstract] [PDF] [DOI]

   The minimum variance controller has been extensively used as a benchmark in the performance assessment of both univariate and multivariate control loops when time delay is the fundamental performance limitation. In this paper, the spatial and temporal performance limitations in the cross- directional (CD) control of paper machines are analyzed. The idea of minimum variance benchmarking is extended to the CD process based on these performance limitations. Based on an industrial CD controller, a user-specified benchmark, which is more practical and less aggressive, is also proposed. In addition, several related performance indices are proposed for the CD process based on both the minimum variance benchmark and the user-specified benchmark. Illustrative examples from a paper machine simulator and industrial data sets are provided to show the effectiveness of the proposed performance indices.

6. A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty Zamar, D.S., Gopaluni, R.B., Sokhansanj, S., and Newlands, N.K. 2017. Computers & Chemical Engineering, 97 , 114–123. [Abstract] [PDF] [DOI]

   Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This paper develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. The proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.

7. Optimization of sawmill residues collection for bioenergy production Zamar, D.S., Gopaluni, R.B., and Sokhansanj, S. 2017. Applied Energy, 202 , 487–495. [Abstract] [PDF] [DOI]

   The collection of sawmill residues is an important logistic activity for the pulp and paper industry, which uses the biomass as a source of energy. We study a vehicle routing problem for a network composed of a single depot and several sawmills. The sawmills serve as potential suppliers of biomass residues to the depot, which in turn processes and distributes the residues to the pulp and paper mills. This problem consists of identifying the best daily routing schedule for a fixed number of trucks. The objective is to maximize the ratio of energy returned on energy invested, while satisfying a minimum daily amount of dried biomass residues. There are several random components in the problem, including the availability and moisture content of the biomass residues. We use a combination of scenario analysis and heuristics to solve this stochastic vehicle routing problem. A performance comparison of the proposed method reveals an estimated daily energy savings of 6 GJ over the benchmark method.

2016

1. Particle filtering without tears: A primer for beginners Tulsyan, Aditya, Gopaluni, R Bhushan, and Khare, Swanand R 2016. Computers & Chemical Engineering, 95 , 130–145. [Abstract] [PDF] [Presentation] [Code]

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2. Simple Self-Administered Method for Assessing Insulin Sensitivity in Diabetic Patients Barazandegan, M., Ekram, F., Kwok, E., and Gopaluni, R.B. 2016. Journal of Medical and Biological Engineering, 36 (2), 197–205. [Abstract] [PDF] [DOI]

   Several methods have been proposed for evaluating a person’s insulin sensitivity from an oral glucose tolerance test (OGTT) and the euglycemic insulin clamp technique. However, none are easy or inexpernsive to implement since the plasma insulin concentration, a key variable for assessing the insulin sensitivity index (ISI), is required to be clinically measured at specific times. Therefore, the purpose of this study is to develop a new ISI that can be easily and accurately obtained by patients themselves without costly, time-consuming, and inconvenient testing methods. This study proposes a simple self-administered testing method, simulated on a computerized model of type II diabetic patients, for estimating the ISI. The test involces a 75-g glucoase ingestion and two injections of 10 mU/kg insulin. Blood glucoe is measured one and two hours later. The test was evaluated by using a previously developed diabetic-patient dynamic model. Fifteen sets of OGTT data from diabetic patients published in the literature were used for the model development. A simulation of the proposed self-administered test indicates that the proposed ISI correlates well with the ISI called M-value obtained from the gold standard but elaborate euglycemic hyperinsulinemic clamp (r=0.927, ;p=0.0045). The proposed ISI is considered to be easy to perform, time-saving, inexpensive, and accurate enough for clinical assessments.

3. LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control Torchio, M., Magni, L., Gopaluni, R.B., Braatz, R.D., and Raimondo, D.M. 2016. Journal of The Electrochemical Society, 163 (7), A1192–A1205. [Abstract] [PDF] [DOI] [Code]

   Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications of lithium-ion batteries. Their optimal design and management are important for safe and profitable operations. The use of accurate mathematical models can help in achieving the best performance. This article provides a detailed description of a finite volume method (FVM) for a pseudo-two-dimensional (P2D) Li-ion battery model suitable for the development of model-based advanced battery management systems. The objectives of this work are to provide: (i) a detailed description of the model formulation, (ii) a parametrizable Matlab framework for battery design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed numerical implementation with respect to the COMSOL MultiPhysics commercial software and the Newman’s DUALFOIL code, and (iv) some demonstrative simulations involving thermal dynamics, a hybrid charge-discharge cycle emulating the throttle of an HEV, a model predictive control of state of charge, and a battery pack simulation.

4. Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks Jang, Hong, Kim, Kwang-Ki K, Braatz, Richard D, Gopaluni, R Bhushan, and Lee, Jay H 2016. Computers & Chemical Engineering, 90 , 111–120. [Abstract] [PDF] [Presentation] [Code]

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5. Economic Optimization of Sawmill Residues Collection for Bioenergy Conversion Zamar, David S, Gopaluni, Bhushan, Sokhansanj, Shahab, and Ebadian, Mahmood 2016. IFAC-PapersOnLine, 49 (7), 857–862. [Abstract] [PDF] [Presentation] [Code]

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6. Model-based detection of organ dysfunction and faults in insulin infusion devices for type 2 diabetic patients Barazandegan, M, Kwok, KE, and Gopaluni, RB 2016. In American Control Conference (ACC), 2016, , 3994–3999.
7. Economic nonlinear model predictive control for mechanical pulping processes Tian, Hui, Lu, Qiugang, Gopaluni, R Bhushan, Zavala, Victor M, and Olson, James A 2016. In American Control Conference (ACC), 2016, , 1796–1801.
8. Robust model-based delay timer alarm for non-linear processes Tulsyan, Aditya, and Gopaluni, R Bhushan 2016. In American Control Conference (ACC), 2016, , 2989–2994.
9. Multiobjective economic model predictive control of mechanical pulping processes Tian, Hui, Lu, Qiugang, Gopaluni, R Bhushan, and Zavala, Victor M 2016. In Decision and Control (CDC), 2016 IEEE 55th Conference on, , 4040–4045.
10. State-of-charge estimation in lithium-ion batteries: A particle filter approach Tulsyan, Aditya, Tsai, Yiting, Gopaluni, R Bhushan, and Braatz, Richard D 2016. Journal of Power Sources, 331 , 208–223. [Abstract] [PDF] [Presentation] [Code]

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11. A comprehensive compartmental model of blood glucose regulation for healthy and type 2 diabetic subjects Vahidi, O., Kwok, K.E., Gopaluni, R.B., and Knop, F.K. 2016. Medical & biological engineering & computing, 54 (9), 1383–1398. [Abstract] [PDF] [DOI]

    We have expanded a former compartmental model of blood glucose regulation for healthy and type II diabetic subjects. The former model was a detailed physiological model which considered the interactions of three substances, glucose, insulin and glucagon on regulating the blood sugar. The main drawback of the former model was its restriction on the route of glucose entrance to the body which was limited to the intravenous glucose injection. To handle the oral glucose intake, we have added a model of glucose absorption in the gastrointestinal tract to the former model to address the resultant variations of blood glucose concentrations following an oral glucose intake. Another model representing the incretins production in the gastrointestinal tract along with their hormonal effects on boosting pancreatic insulin production is also added to the former model. We have used two sets of clinical data obtained during oral glucose tolerance test and isoglycemic intravenous glucose infusion test from both type II diabetic and healthy subjects to estimate the model parameters and to validate the model results. The estimation of model parameters is accomplished through solving a nonlinear optimization problem. The results show acceptable precision of the estimated model parameters and demonstrate the capability of the model in accurate prediction of the body response during the clinical trials.

2015

1. A magnetic sensor to measure wear in centrifugal pumps Khoie, Ramin, Gopaluni, Bhushan, Olson, James A, and Stoeber, Boris 2015. In SENSORS, 2015 IEEE, , 1–4.
2. Detection of abnormalities in type II diabetic patients using particle filters Vahidi, Omid, Gopaluni, R Bhushan, and Kwok, Ezra 2015. Journal of Medical and Biological Engineering, 35 (2), 188–201. [Abstract] [PDF] [Presentation] [Code]

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3. Impact of model plant mismatch on performance of control systems: An application to paper machine control Yousefi, M, Gopaluni, RB, Loewen, PD, Forbes, MG, Dumont, GA, and Backstrom, J 2015. Control Engineering Practice, 43 , 59–68. [Abstract] [PDF] [Presentation] [Code]

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4. Detecting model-plant mismatch without external excitation Yousefi, Mahdi, Lu, Qiugang, Gopaluni, R Bhushan, Loewen, Philip D, Forbes, Michael G, Dumont, Guy Albert, and Backstrom, J 2015. In American Control Conference (ACC), 2015, , 4976–4981.
5. Cross-directional controller performance monitoring for paper machines Lu, Qiugang, Rippon, Lee D, Gopaluni, R Bhushan, Forbes, Michael G, Loewen, Philip D, Backstrom, Johan, and Dumont, Guy A 2015. In American Control Conference (ACC), 2015, , 4970–4975.
6. Real-time model predictive control for the optimal charging of a lithium-ion battery Torchio, Marcello, Wolff, Nicolas A, Raimondo, Davide M, Magni, Lalo, Krewer, Ulrike, Gopaluni, R Bushan, Paulson, Joel A, and Braatz, Richard D 2015. In American Control Conference (ACC), 2015, , 4536–4541.
7. A Novel Algorithm for Model-Plant Mismatch Detection for Model Predictive Controllers Tsai, Yiting, Gopaluni, RB, Marshman, D, and Chmelyk, T 2015. IFAC-PapersOnLine, 48 (8), 746–752. [Abstract] [PDF] [Presentation] [Code]

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8. Overload Detection in Semi-Autogenous Grinding: A Nonlinear Process Monitoring Approach McClure, KS, and Gopaluni, RB 2015. IFAC-PapersOnLine, 48 (8), 960–965. [Abstract] [PDF] [Presentation] [Code]

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9. Model predictive control in industry: Challenges and opportunities Forbes, Michael G, Patwardhan, Rohit S, Hamadah, Hamza, and Gopaluni, R Bhushan 2015. IFAC-PapersOnLine, 48 (8), 531–538. [Abstract] [PDF] [Presentation] [Code]

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10. Robust Optimization of Competing Biomass Supply Chains Under Feedstock Uncertainty Zamar, David S, Gopaluni, Bhushan, Sokhansanj, Shahab, and Newlands, Nathaniel K 2015. IFAC-PapersOnLine, 48 (8), 1222–1227. [Abstract] [PDF] [Presentation] [Code]

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11. Moving-Horizon Predictive Input Design for Closed-Loop Identification Yousefi, M, Rippon, LD, Forbes, MG, Gopaluni, RB, Loewen, PD, Dumont, GA, and Backstrom, J 2015. IFAC-PapersOnLine, 48 (8), 135–140. [Abstract] [PDF] [Presentation] [Code]

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12. Evaluation of treatment regimens for blood glucose regulation in type II diabetes using pharmacokinetic-pharmacodynamic modeling Ekram, Fatemeh, Barazandegan, Melissa, Kwok, Ezra, and Gopaluni, Bhushan 2015. In Control Conference (CCC), 2015 34th Chinese, , 8519–8524.
13. Assessment of type II diabetes mellitus using irregularly sampled measurements with missing data Barazandegan, Melissa, Ekram, Fatemeh, Kwok, Ezra, Gopaluni, Bhushan, and Tulsyan, Aditya 2015. Bioprocess and biosystems engineering, 38 (4), 615–629. [Abstract] [PDF] [Presentation] [Code]

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2014

1. SELF-ASSESSMENT OF INSULIN SENSITIVITY Barazandegan, M, Ekram, F, Kwok, KE, and Gopaluni, RB 2014. SELF, ,
2. Performance assessment, diagnosis, and optimal selection of non-linear state filters Tulsyan, Aditya, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2014. Journal of Process Control, 24 (2), 460–478. [Abstract] [PDF] [Presentation] [Code]

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3. Fault Isolation based on General Observer Scheme in Stochastic Non-linear State-Space Models Using Particle Filters Alrowaie, F, Kwok, KE, and Gopaluni, RB 2014. ADCONP, ,
4. A moving horizon approach to input design for closed loop identification Patwardhan, Rohit S, and Goapluni, R Bhushan 2014. Journal of Process Control, 24 (3), 188–202. [Abstract] [PDF] [Presentation] [Code]

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5. Sparse identification in chemical master equations for monomolecular reaction networks Kim, Kwang-Ki K, Jang, Hong, Gopaluni, R Bhushan, Lee, Jay H, and Braatz, Richard D 2014. In American Control Conference (ACC), 2014, , 3698–3703.
6. Sensitivity of controller performance indices to model-plant mismatch: An application to paper machine control Yousefi, Mahdi, Forbes, Michael G, Gopaluni, R Bhushan, Dumont, Guy Albert, Backstrom, J, and Malhotra, A 2014. In American Control Conference (ACC), 2014, , 3506–3511.
7. Increased CHO cell fed-batch monoclonal antibody production using the autophagy inhibitor 3-MA or gradually increasing osmolality Nasseri, S Soroush, Ghaffari, Navid, Braasch, Katrin, Jardon, Mario A, Butler, Michael, Kennard, Malcolm, Gopaluni, Bhushan, and Piret, James M 2014. Biochemical Engineering Journal, 91 , 37–45. [Abstract] [PDF] [Presentation] [Code]

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8. Sensitivity of mimo controller performance to model-plant mismatch, with applications to paper machine control Yousefi, Mahdi, Forbes, Michael G, Gopaluni, R Bhushan, Loewen, Philip D, Dumont, Guy Albert, and Backstrom, J 2014. In Control Applications (CCA), 2014 IEEE Conference on, , 204–209.
9. Alarm design for nonlinear stochastic systems Alrowaie, F, Gopaluni, RB, and Kwok, KE 2014. In Intelligent Control and Automation (WCICA), 2014 11th World Congress on, , 473–479.
10. Robust level control of a dry-surge ore pile McClure, Kenneth Scott, Nagamune, Ryozo, Marshman, Devin, Chmelyk, Terrance, and Gopaluni, Bhushan 2014. The Canadian Journal of Chemical Engineering, 92 (3), 486–495.

2013

1. A moving horizon approach to multivariable input design in general linear systems with constraints Patwardhan, RS, and Gopaluni, RB 2013. IFAC Proceedings Volumes, 46 (32), 577–582. [Abstract] [PDF] [Presentation] [Code]

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2. Use of Molecular Dynamics for the Refinement of an Electrostatic Model for the In Silico Design of a Polymer Antidote for the Anticoagulant Fondaparinux Cajiao, Adriana, Kwok, Ezra, Gopaluni, Bhushan, and Kizhakkedathu, Jayachandran N 2013. Journal of medical engineering, 2013 , [Abstract] [PDF] [Presentation] [Code]

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3. A particle filter approach to approximate posterior Cramér-Rao lower bound: The case of hidden states Tulsyan, Aditya, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. IEEE Transactions on Aerospace and Electronic Systems, 49 (4), 2478–2495. [Abstract] [PDF] [Presentation] [Code]

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4. On-line Bayesian parameter estimation in general non-linear state-space models: a tutorial and new results Tulsyan, Aditya, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. arXiv preprint arXiv:1307.3490, ,
5. Input design for Bayesian identification of non-linear state-space models Tulsyan, Aditya, Khare, Swanand R, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. arXiv preprint arXiv:1307.6258, ,
6. Error analysis in Bayesian identification of non-linear state-space models Tulsyan, Aditya, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. arXiv preprint arXiv:1307.6254, ,
7. Constrained dual ensemble Kalman filter for state and parameter estimation Bavdekar, Vinay A, Prakash, Jagdeesan, Shah, Sirish L, and Gopaluni, R Bhushan 2013. In American Control Conference (ACC), 2013, , 3093–3098.
8. Optimal control and state estimation of lithium-ion batteries using reformulated models Suthar, Bharatkumar, Ramadesigan, Venkatasailanathan, Northrop, Paul WC, Gopaluni, Bhushan, Santhanagopalan, Shriram, Braatz, Richard D, and Subramanian, Venkat R 2013. In American Control Conference (ACC), 2013, , 5350–5355.
9. Nonlinear process monitoring using supervised locally linear embedding projection McClure, Kenneth S, Gopaluni, R Bhushan, Chmelyk, Terrance, Marshman, Devin, and Shah, Sirish L 2013. Industrial & Engineering Chemistry Research, 53 (13), 5205–5216. [Abstract] [PDF] [Presentation] [Code]

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10. Bayesian identification of non-linear state-space models: Part II-Error Analysis Tulsyan, Aditya, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. IFAC Proceedings Volumes, 46 (32), 631–636. [Abstract] [PDF] [Presentation] [Code]

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11. Bayesian identification of non-linear state-space models: Part I-Input design Tulsyan, Aditya, Khare, Swanand R, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. IFAC Proceedings Volumes, 46 (32), 774–779. [Abstract] [PDF] [Presentation] [Code]

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12. Evaluation of adaptive extended Kalman filter algorithms for state estimation in presence of model-plant mismatch Bavdekar, Vinay A, Gopaluni, R Bhushan, and Shah, Sirish L 2013. IFAC Proceedings Volumes, 46 (32), 184–189. [Abstract] [PDF] [Presentation] [Code]

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13. State of charge estimation in Li-ion batteries using an isothermal pseudo two-dimensional model Gopaluni, R Bhushan, and Braatz, Richard D 2013. IFAC Proceedings Volumes, 46 (32), 135–140. [Abstract] [PDF] [Presentation] [Code]

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14. A Comparison of Moving Horizon and Bayesian State Estimators with an Application to a pH Process Bavdekar, Vinay A, Gopaluni, R Bhushan, and Shah, Sirish L 2013. IFAC Proceedings Volumes, 46 (32), 160–165. [Abstract] [PDF] [Presentation] [Code]

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15. On simultaneous on-line state and parameter estimation in non-linear state-space models Tulsyan, Aditya, Huang, Biao, Gopaluni, R Bhushan, and Forbes, J Fraser 2013. Journal of Process Control, 23 (4), 516–526. [Abstract] [PDF] [Presentation] [Code]

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2012

1. Predicting binding affinities of a novel polymer for the neutralization of fondaparinux and Cajiao, Adriana 2012. ,
2. A feedback glucose control strategy for type II diabetes mellitus based on fuzzy logic Ekram, F, Sun, L, Vahidi, O, Kwok, E, and Gopaluni, RB 2012. The Canadian Journal of Chemical Engineering, 90 (6), 1411–1417. [Abstract] [PDF] [Presentation] [Code]

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3. Fault detection and isolation in stochastic non-linear state-space models using particle filters Alrowaie, F, Gopaluni, RB, and Kwok, KE 2012. Control Engineering Practice, 20 (10), 1016–1032. [Abstract] [PDF] [Presentation] [Code]

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4. Performance assessment of nonlinear state filters Tulsyan, Aditya, Huang, Biao, Gopaluni, RB, and Forbes, J Fraser 2012. IFAC Proceedings Volumes, 45 (15), 371–376. [Abstract] [PDF] [Presentation] [Code]

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5. Nonlinear Bayesian state estimation: A review of recent developments Patwardhan, Sachin C, Narasimhan, Shankar, Jagadeesan, Prakash, Gopaluni, Bhushan, and L Shah, Sirish 2012. Control Engineering Practice, 20 (10), 933–953. [Abstract] [PDF] [Presentation] [Code]

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6. Editorial: 4th Symposium on Advanced Control of Industrial Processes (ADCONIP) Gudi, Ravi, Gopaluni, Bhushan, and Huang, Biao 2012. Control Engineering Practice, 20 (10), 931–932. [Abstract] [PDF] [Presentation] [Code]

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7. Parameter estimation in models with hidden variables: An application to a biotech process Jang, Seunghee S, Hoz, Hector, Ben-zvi, Amos, McCaffrey, William C, and Gopaluni, R Bhushan 2012. The Canadian Journal of Chemical Engineering, 90 (3), 690–702. [Abstract] [PDF] [Presentation] [Code]

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8. A Receding Horizon Approach to Input Design for Closed Loop Identification Patwardhan, Rohit S, and Gopaluni, R Bhushan 2012. , [Abstract] [PDF] [Presentation] [Code]

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2011

1. Reconstructing Paper Machine Sheet Process Data Variation Using Compressive Sensing Davies, Michael, Gopaluni, Bhushan, Loewen, Philip, Towfighi, Parisa, and Dumont, Guy 2011. IFAC Proceedings Volumes, 44 (1), 4266–4271. [Abstract] [PDF] [Presentation] [Code]

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2. Detection of organ dysfunction in type II diabetic patients Vahidi, O, Gopaluni, RB, and Kwok, KE 2011. In American Control Conference (ACC), 2011, , 4769–4774.
3. Characterization of gas–solid fluidization: A comparative study of acoustic and pressure signals Li, Yan-qin, Grace, John R, Gopaluni, R Bhushan, Bi, Hsiaotao, Lim, C Jim, and Ellis, Naoko 2011. Powder technology, 214 (2), 200–210. [Abstract] [PDF] [Presentation] [Code]

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4. An algorithm for fault detection in stochastic non-linear state-space models using particle filters Alrowaie, F, Kwok, KE, and Gopaluni, RB 2011. In Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on, , 60–65.
5. Parameter estimation in nonlinear chemical and biological processes with unmeasured variables from small data sets Jang, SS, and Gopaluni, RB 2011. Chemical Engineering Science, 66 (12), 2774–2787. [Abstract] [PDF] [Presentation] [Code]

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6. Nonlinear bayesian state estimation: Review and recent trends Prakash, J, Gopaluni, RB, Patwardhan, Sachin C, Narasimhan, Shankar, and Shah, Sirish L 2011. In Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on, , 450–455.
7. Input Design for Nonlinear Stochastic Dynamic Systems-A Particle Filter Approach Gopaluni, R Bhushan, Schön, Thomas B, and Wills, Adrian G 2011. In IFAC World Congress, 18 (1), 13191–13196.
8. Pharmacokinetic-Pharmacodynamic Modeling of Metformin for the Treatment of Type II Diabetes Mellitus Sun, Lin, Kwok, Ezra, Gopaluni, Bhushan, and Vahidi, Omid 2011. The Open Biomedical Engineering Journal, 5 , 1. [Abstract] [PDF] [Presentation] [Code]

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9. Reconstructing Variation in a Sheet of Paper Using Compressive Sensing Towfighi, Parisa, Dumont, Guy, Davies, Michael S, Gopaluni, Bhushan, and Loewen, Philip D 2011. In Proceedings of IFAC World Congress, 18 (1), 4266–4271.
10. In Silico Design of Polymeric Antidote for Anticoagulant Fondaparinux Cajiao, Adriana, Gopaluni, Bhushan, Kwok, Ezra, and Kizhakkedathu, Jayachandran N 2011. Journal of Medical and Biological Engineering, 31 (2), 129–134. [Abstract] [PDF] [Presentation] [Code]

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11. A Feedback Glucose Control Strategy for Type II Diabetes Mellitus Sun, Lin, Kwok, Ezra, Gopaluni, Bhushan, and Vahidi, Omid 2011. In Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on, , 349–352.
12. Developing a physiological model for type II diabetes mellitus Vahidi, O, Kwok, KE, Gopaluni, RB, and Sun, L 2011. Biochemical Engineering Journal, 55 (1), 7–16. [Abstract] [PDF] [Presentation] [Code]

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2010

1. A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor Chitralekha, Saneej B, Prakash, J, Raghavan, H, Gopaluni, RB, and Shah, Sirish L 2010. Journal of Process Control, 20 (8), 934–943. [Abstract] [PDF] [Presentation] [Code]

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2. Nonlinear system identification under missing observations: The case of unknown model structure and Gopaluni, R Bhushan 2010. Journal of Process Control, 20 (3), 314–324. [Abstract] [PDF] [Presentation] [Code]

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3. Energy optimization in a pulp and paper mill cogeneration facility Marshman, DJ, Chmelyk, T, Sidhu, MS, Gopaluni, RB, and Dumont, GA 2010. Applied Energy, 87 (11), 3514–3525. [Abstract] [PDF] [Presentation] [Code]

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4. Economic performance assessment with optimized LQG benchmarking in MIMO systems Marshman, Devin James, Chmelyk, Terrance, Sidhu, MS, Gopaluni, Ratna Bhushan, and Dumont, GA 2010. IFAC Proceedings Volumes, 43 (5), 769–774. [Abstract] [PDF] [Presentation] [Code]

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5. Development of a physiological model forpatients with type 2 diabetes mellitus Vahidi, O, Kwok, KE, Gopaluni, RB, and Sun, L 2010. In American Control Conference (ACC), 2010, , 2027–2032.
6. Special series of articles on process control Gopaluni, RB, and Duchesne, C 2010. The Canadian Journal of Chemical Engineering, 88 (5), 695–695. [Abstract] [PDF] [Presentation] [Code]

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7. Nonlinear System Identification from Small Data Sets Gopaluni, R Bhushan, and Marshman, Devin 2010. IFAC Proceedings Volumes, 43 (5), 589–594. [Abstract] [PDF] [Presentation] [Code]

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2009

1. Comparison of expectation-maximization based parameter estimation using particle filter, unscented and extended Kalman filtering techniques Chitralekha, Saneej, Prakash, Jagadeesan, Raghavan, Harigopal, Gopaluni, Ratna Bhushan, and Shah, Sirish 2009. In IFAC Conference on System Identification, 15 (1), 804–809.
2. Identification of nonlinear state-space models: the case of unknown model structure and Gopaluni, R Bhushan 2009. IFAC Proceedings Volumes, 42 (11), 470–475. [Abstract] [PDF] [Presentation] [Code]

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3. A note on separating machine direction and cross machine data on a paper machine Gopaluni, RB, Davies, MS, Loewen, PD, Stewart, GE, and Dumont, GA 2009. NORDIC Pulp & Paper Research Journal, 24 (3), 273–277.
4. Parameter Estimation of Stochastic Nonlinear Dynamic Processes using Multiple Experimental Data Sets and Jang, Seunghee Shelly 2009. ,
5. Particle filter approach to nonlinear system identification under missing observations with a real application Gopaluni, R Bhushan, Schön, Thomas B, and Wills, Adrian G 2009. IFAC Proceedings Volumes, 42 (10), 810–815. [Abstract] [PDF] [Presentation] [Code]

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2008

1. A particle filter approach to identification of nonlinear processes under missing observations and Gopaluni, RB 2008. The Canadian Journal of Chemical Engineering, 86 (6), 1081–1092. [Abstract] [PDF] [Presentation] [Code]

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2. An online non-intrusive method for alignment between actuators and their response centers on a paper machine Gopaluni, R Bhushan, Davies, Michael S, Loewen, Philip D, and Dumont, Guy A 2008. ISA transactions, 47 (3), 241–246. [Abstract] [PDF] [Presentation] [Code]

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3. Adaptive signal processing of asset price dynamics with predictability analysis Mamon, Rogemar S, Erlwein, Christina, and Gopaluni, R Bhushan 2008. Information Sciences, 178 (1), 203–219. [Abstract] [PDF] [Presentation] [Code]

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4. Identification of nonlinear processes with known model structure under missing observatrions and Gopaluni, Ratna Bhushan 2008. In Proceedings of the IFAC 17th World Congress, Seoul, Korea, July 6, 11 ,

2007

2006

1. Identification of delay dominant recycle systems Gopaluni, RB, Raghavan, H, Patwardhan, RS, Shah, SL, and Dumont, GA 2006. Journal of Process Control, 16 (9), 903–912. [Abstract] [PDF] [Presentation] [Code]

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2. Identification of Symmetric Noncausal Processes: Cross-Directional Response Modelling of Paper Machines Gopaluni, R Bhushan, Loewen, Philip D, Ammar, Mohammed, Dumont, Guy A, and Davies, Michael S 2006. In Decision and Control, 2006 45th IEEE Conference on, , 6744–6749.
3. Identification of chemical processes with irregular output sampling Raghavan, Harigopal, Tangirala, Arun K, Bhushan Gopaluni, R, and Shah, Sirish L 2006. Control engineering practice, 14 (5), 467–480. [Abstract] [PDF] [Presentation] [Code]

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4. Autonomous alignment of CD control on paper machines Farahmand, F, Gopulani, R, Dumont, G, Davies, M, and Loewen, P 2006. In Proc. FSA Control Syst. Conf., , 203–208.

2005

1. Gray-box identification of dynamic models for the bleaching operation in a pulp mill Raghavan, Harigopal, Gopaluni, R Bhushan, Shah, Sirish, Pakpahan, Johan, Patwardhan, Rohit, and Robson, Chris 2005. Journal of Process Control, 15 (4), 451–468. [Abstract] [PDF] [Presentation] [Code]

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2. ROBUST ADAPTIVE CONTROL FOR STRICT-FEEDBACK NONLINEAR SYSTEMS Mizumoto, Ikuro, Gopaluni, Ratna Bhushan, Shah, Sirish L, and Iwai, Zenta 2005. Asian Journal of Control, 7 (3), 231–243. [Abstract] [PDF] [Presentation] [Code]

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2004

1. MPC relevant identificationtuning the noise model Gopaluni, RB, Patwardhan, RS, and Shah, SL 2004. Journal of Process Control, 14 (6), 699–714. [Abstract] [PDF] [Presentation] [Code]

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2. System identification from multi-rate data Gopaluni, R Bhushan, Raghavan, Harigopal, and Shah, Sirish L 2004. IFAC Proceedings Volumes, 37 (1), 155–160. [Abstract] [PDF] [Presentation] [Code]

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3. Iterative identification and predictive control and Gopaluni, Ratna Bhushan 2004. , [Abstract] [PDF] [Presentation] [Code]

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2003

1. The nature of data pre-filters in MPC relevant identification?open-and closed-loop issues Gopaluni, R Bhushan, Patwardhan, RS, and Shah, SL 2003. automatica, 39 (9), 1617–1626. [Abstract] [PDF] [Presentation] [Code]

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2. A robust nonlinear adaptive backstepping controller for a CSTR Gopaluni, RB, Mizumoto, Ikuro, and Shah, SL 2003. Industrial & engineering chemistry research, 42 (20), 4628–4644. [Abstract] [PDF] [Presentation] [Code]

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3. Robust Adaptive Backstepping Control Based on High-Gain Feedback and Its Application to a CSTR Control Mizumoto, Ikuro, Michino, Ryuji, Iwai, Zenta, Gopaluni, Ratna Bhushan, and Shah, Sirish L 2003. Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, 69 (10), 2667–2674. [Abstract] [PDF] [Presentation] [Code]

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4. ???????????????????????????????????? CSTR ?????? ????, , ????, , and ????, 2003. ????????? C ?, 69 (686), 2667–2674. [Abstract] [PDF] [Presentation] [Code]

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2002

1. Experiment design for MPC relevant identification Gopaluni, Ratna Bhushan, Patwardhan, Rohit S, and Shah, Sirish L 2002. In American Control Conference, 2002. Proceedings of the 2002, 4 , 2713–2718.
2. Bias distribution in MPC relevant identification Gopaluni, Ratna Bhushan, Patwardhan, Rohit S, and Shah, Sirish L 2002. IFAC Proceedings Volumes, 35 (1), 435–440. [Abstract] [PDF] [Presentation] [Code]

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3. Robust Adaptive Control for Strict-Feedback Nonlinear Systems with Non-Parametric Uncertainties Mizumoto, Ikuro, Gopaluni, RB, Shah, SL, and Iwai, Zenta 2002. Transactions of the Society of Instrument and Control Engineers, 38 (12), 1069–1078. [Abstract] [PDF] [Presentation] [Code]

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4. ?????????????????? Strict-Feedback ??????????????????? ????, , GOPALUNI, RB, SHAH, SL, and ????, 2002. ???????????, 38 (12), 1069–1078. [Abstract] [PDF] [Presentation] [Code]

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5. Bias distribution in MPC relevant identification 15th Triennial World Congress Gopaluni, R, Patwardhan, R, and Shah, S 2002. In , ,