Bhushan Gopaluni

• PhD
• Professor at University of British Columbia

Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or...

Identifying specific properties of fibers in paper hand sheets is a challenge being faced for many decades. In this chapter, some of the advanced algorithms for image segmentation to estimate these properties are systematically presented. The process of determining the appropriate operating conditions from the estimated properties is also elaborate...

In complex industrial processes, real-time monitoring of critical variables is essential for ensuring operational safety and efficiency. Traditional process monitoring models often struggle with processes characterized by multiple operating modes, leading to decreased prediction accuracy and reliability. Existing methods typically require prior kno...

Lithium‐ion batteries offer significant advantages in terms of their high energy and power density and efficiency, but capacity degradation remains a major issue during their usage. Accurately estimating the remaining capacity is crucial for ensuring safe operations, leading to the development of precise capacity estimation models. Data‐driven mode...

Protein biomarkers can be used to characterize symptom classes, which describe the metabolic or immunodeficient state of patients during the progression of a specific disease. Recent literature has shown that machine learning methods can complement traditional clinical methods in identifying biomarkers. However, many machine learning frameworks onl...

3D x‐ray tomography is a powerful scanning technique used for generating images of complex fibre structures. A novel machine‐learning algorithm to identify and separate individual fibres using 3D images is proposed in this article. The developed four‐step hybrid 3D fibre segmentation algorithm involves deep‐learning aided semantic segmentation that...

Co-processing biogenic feedstocks allows oil refiners to use their infrastructure while reducing the carbon intensity of the fuels they produce. Although policies such as British Columbia and California’s low carbon...

Fixed structure controllers (such as proportional-integral-derivative controllers) are used extensively in industry. Finding a practical and versatile method to tune these controllers, particularly with imprecise process models and limited online computational resources, is an industrially relevant problem which could improve the efficiency of many...

Much of the current research on supervised modelling is focused on maximizing outcome prediction accuracy. However, in engineering disciplines, an arguably more important goal is that of feature extraction, the identification of relevant features associated with the various outcomes. For instance, in microbial communities, the identification of key...

Rotary kilns are large-scale unit operations that are critical to many industrial processes such as cement production, pyrometallurgy, and kraft pulping. As expensive, energy-intensive units, it is imperative from both an economic and environmental perspective to ensure efficient operation of the rotary kiln. To provide additional insights for oper...

Data-driven disciplines such as biostatistics and chemometrics are undergoing a period of transformation propelled by powerful advances in computational hardware, parallel processing and algorithmic efficiency. Process systems engineering is positioned for concurrent advances in data-driven sub-disciplines such as modeling, optimization, control, f...

This paper develops a tractable approximation for stochastic model predictive control (SMPC). Under the proposed approach, we solve multiple deterministic MPC (DMPC) problems over individual scenarios of the uncertain variables to obtain a set of control policies and select from this candidate set a control input that yields the best approximation...

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent resul...

Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, such as neural networks, to update the controller parameters at each time-step of the underlying process. In this work, we present a simple finite-difference approach, base...

Deep reinforcement learning (DRL) has seen several successful applications to process control. Common methods rely on a deep neural network structure to model the controller or process. With increasingly complicated control structures, the closed-loop stability of such methods becomes less clear. In this work, we focus on the interpretability of DR...

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 proce...

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 proce...

Amino acid availability is a key factor that can be controlled to optimize the productivity of fed‐batch cultures. To study amino acid limitation effects, a serum‐free chemically defined basal medium was formulated to exclude the amino acids that became depleted in batch culture. The effect of limiting glutamine, asparagine, and cysteine on the cel...

We develop 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 separating the MPM from noise model changes. Another advantage of this approach is that it is appli...

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 dat...

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 proce...

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...

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 achie...

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...

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 reduc...

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 control- ling both t...

Batch processes are often characterized by non-linear dynamics and varying oper- ating conditions. Multiphase and multimode modeling of batch processes is a common technique that offers insight into the process operation and improved online monitor- ing. However, existing monitoring methods have several drawbacks such as neglecting process dynamics...

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...

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...

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 [1]. To ensure safe, prolonged, and reliable operations, significant research effort has been put into understanding, modelling, and predicting t...

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...

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 c...

Traditional techniques employed by 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 techniques suitable for advanced analysis. In this tutorial we introduce data analytics tec...

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 t...

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 str...

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 infl...

The state of health (SoH) of lithium-ion batteries and battery packs must be monitored effectively 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, due to the complex and often...

Traditional techniques employed by 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 techniques suitable for advanced analysis. In this tutorial we introduce data analytics tec...

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 measurem...

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 t...

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...

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;...

The bale collection problem (BCP) appears after harvest operations of agricultural crops. The solution to the BCP is defined by the sequence in which bales, that lie scattered across the field, are collected. This paper presents a constrained k-means algorithm and nearest neighbor approach to the BCP, which minimizes travel time and hence fuel cons...

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 ap...

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 condi...

In modern chemical processes, identification of the process variable connectivity and their topology is vital for maintaining the operational safety. As a general information theoretic method, transfer entropy can analyze the causality between two variables based on estimation of conditional probability density functions. Transfer entropy estimatio...

The collection of sawmill residuals is an important logistic activity for the pulp and paper industry, which use the biomass as a source of energy. We study a vehicle routing problem for a network composed of a single depot and 25 nearby sawmills in the Lower Mainland region of British Columbia, Canada. The sawmills serve as potential suppliers of...

The dynamics of lithium-ion batteries are complex and are often approximated by models consisting of partial differential equations (PDEs) relating the internal ionic concentrations and potentials. The Pseudo two-dimensional model (P2D) is one model that performs sufficiently accurately under various operating conditions and battery chemistries. De...

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 applicab...

The main purpose of this primer is to systematically introduce the theory of particle filters to readers with limited or no prior understanding of the subject. The primer is written for beginners and practitioners interested in learning about the theory and implementation of particle filtering methods. Throughout this primer we highlight the common...

We present a multiobjective economic model pre-dictive control (m-econ MPC) strategy to reduce the electrical energy consumption in a two-stage mechanical pulping (MP) process. The nonlinear MP process considered in this paper consists of a primary and a secondary refiner. In the proposed m-econ MPC technique, an auxiliary MPC controller and a stab...

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...

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 performan...

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 inexpensive to implement since the plasma insulin concentration, a key variable for assessing the insulin sensitivity index (ISI), is required to be clinica...

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. This paper develops a robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply our approach to address the problem of analyzing competing...

Detecting the onset of overloading in a semi-autogenous grinding (SAG) mill is a challenging task for operators to perform due to the complex and nonlinear nature of an overload. To detect an overload, operators must simultaneously monitor the correlations between several measurements of the SAG process. However, overloading often goes unnoticed at...

For Model Predictive Controlled (MPC) applications, the quality of the plant model determines the quality of performance of the controller. Model Plant Mismatch (MPM), the discrepancies between the plant model and actual plant transfer matrix, can both improve or degrade performance, depending on the context in which performance is measured. In thi...

With decades of successful application of model predictive control (MPC) to industrial processes, practitioners are now focused on ease of commissioning, monitoring, and automation of maintenance. Many industries do not necessarily need better algorithms, but rather improved usability of existing technologies to allow a limited workforce of varying...

We have expanded a former compartmental model of blood glucose regulation for healthy and type 2 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 r...

Model-based controllers based on incorrect estimates of the true plant behaviour can be expected to perform poorly. This work studies the effect of model plant mismatch on the closed loop behaviour and system performance for a certain class of MIMO systems. Performance is measured using a minimum variance index and a closely related user-specified...

Diabetes mellitus is one of the leading diseases in the developed world. In order to better regulate blood glucose in a diabetic patient, improved modelling of insulin-glucose dynamics is a key factor in the treatment of diabetes mellitus. In the current work, the insulin-glucose dynamics in type II diabetes mellitus can be modelled by using a stoc...

Any discrepancy between a process and the associated model used in control design will compromise closed-loop performance. In almost all current techniques to detect model-plant mismatch in model-based control systems there must be some sort of external excitation to overcome the effect of unmeasured disturbances on closed-loop signals. In this pap...

Li-ion batteries are widely used in industrial applications due to their high energy density, slow material degradation, and low self-discharge. The existing advanced battery management systems (ABMs) in industry employ semiempirical battery models that do not use first-principles understanding to relate battery operation to the relevant physical c...

Since the minimum variance controller (MVC) provides the smallest achievable output variance, performance indices based on minimum variance benchmarking have been applied extensively to both univariate and multivariate systems. This paper extends the idea of minimum variance benchmarking to the performance monitoring of cross-directional (CD) proce...

Patients, diagnosed with type II diabetes, first treated with an oral drug but in most cases the satisfactory glycemic control can not be achieved unless the insulin therapy is also considered. Finding the most efficient treatment regimen for each individual is a try and error procedure requiring the performance of several tests on the patients. In...

Diabetes mellitus is one of the leading diseases in the developed world. In order to better regulate blood glucose in a diabetic patient, improved modelling of insulin-glucose dynamics is a key factor in the treatment of diabetes mellitus. In the current work, the insulin-glucose dynamics in type II diabetes mellitus can be modelled by using a stoc...

This study proposes a strategy for detecting possible dysfunction of organs such as the liver, pancreas, muscles and adipose tissues in a group of type II diabetic patients. Several in silico clinical trials are performed on a previously developed type II diabetes model. Since the pancreatic insulin secretion rate and glucose metabolic rates of dif...

We utilize the particle filter algorithm to develop a fault isolation approach based on general observer scheme (GOS) in nonlinear and non-Gaussian systems. The proposed fault isolation scheme is based on a set of parallel particle filters each sensitive to all faults except one. The performance of the proposed approach is compared to an alternativ...

This paper presents a new approach to input design for closed-loop identification. The idea is to maximize the trace of the Fisher information matrix associated with the plant model, while enforcing explicit constraints on both inputs and outputs. The result is the richest possible excitation signal for which the operation of a running closed-loop...

Modulating autophagy provides a new method to increase CHO cell protein production. A fed-batch protocol using the autophagy inhibitor 3-methyl adenine (3-MA), developed for a tissue-plasminogen activator (t-PA) expressing DHFR based CHO cell line, was successfully adapted to a monoclonal antibody (MAb) expressing CHOK1-SV based CHO cell line. By o...

One of the major challenges facing society is our ability to produce food, energy, fibre and other materials efficiently and sustainably to meet growing resource demands without harming the environment. There is an increasing need, use and reliance on geospatial information in assessing supply-chain uncertainty and industrial production risks. More...

This paper considers the identification of kinetic parameters associated with the dynamics of closed biochemical reaction networks. These reaction networks are modeled by chemical master equations in which the reactions and the associated concentrations/populations of species are characterized by probability distributions. The vector of unknown kin...

The performance of a model-based controller depends inextricably on the quality of the corresponding model. The performance of such a controller is optimal with respect to the model. Therefore any model-plant mismatch can be expected to result in poor performance. The objective of this work is to study the sensitivity of commonly used performance i...

Diabetes mellitus is one of the leading diseases in the developed world. In order to better regulate blood glucose in a diabetic patient, improved modelling of insulin-glucose dynamics is a key factor in the treatment of diabetes mellitus. In the current work, the insulin-glucose dynamics in type II diabetes mellitus can be modelled by using a stoc...

The identification of high fidelity models is a critical element in the implementation of high performance model predictive control (MPC) applications in the industry. These controllers can vary in size with input–ouput dimensions ranging from 5 × 10 to 50 × 100. Identifying models of this scale accurately is a time consuming and demanding exercise...

This paper presents a new method for controlling the height of a mineral processing dry‐surge ore pile between specified upper and lower levels by manipulating the outlet ore flow. The upper level should not be exceeded for safety, while the height should always be higher than the lowest allowable level to maintain a reserve for continuous operatio...

Non-linear state filters of different approximations and capabilities allow for real-time estimation of unmeasured states in non-linear stochastic processes. It is well known that the performance of non-linear filters depends on the underlying numerical and statistical approximations used in their design. Despite the theoretical and practical inter...

This is a tutorial paper on industrially successful approaches for nonlinear process monitoring. Online process monitoring is essential to continuous operation of plants at high efficiency. The problem of process monitoring is one where an impending but undesirable process condition has to be identified and used to alert process engineers. This pro...