This paper presents a new state estimation method to ease the heavy computational loads of the Kalman filter (KF) when applied for the processes of large dimensions. The key idea of the proposed methodology is to divide the whole high-dimensional state vector into multiple low-dimensional blocks and suppress the errors introduced by minimizing the corresponding Kullback–Leibler (KL) divergence. Without

Wastewater treatment is a problem known to humankind for centuries. The quality of treated sewage determines the condition of reservoirs around the world. Control of such a complex and nonlinear system as a wastewater treatment plant requires thorough knowledge of the process. The paper presents a hierarchical control system of a Sequencing Batch Reactor (SBR) in Wastewater Treatment Plant (WWTP) taking

Aluminum electrolysis cells are characterized by harsh environments where several measurements have to be done manually. Due to the operational costs related to manual sampling, the sampling rates of these measurements are low. Therefore, the information in the data can be limited, making it challenging to develop robust data-driven methods for aluminum electrolysis process. A broad array of physics-based

In this work, an identification based fault detection method is proposed. The idea is to identify a dynamic process model from test data and to generate residuals using the identified model for fault detection. The method intends to improve fault detection performance while taking disturbance and model error into account. To this end, a fault detection performance index is introduced in a statistical

In order to deal with the generalized uncertainty of the process variable, under the framework of Dempster–Shafer theory of evidence (DST), a data-driven approach without any probabilistic assumption is presented via the dynamic form of the evidence reasoning (ER) rule. Firstly, the process variable is transformed into the corresponding alarm evidence according to referential evidential matrix constructed

Modern industrial processes are large-scale, highly complex systems with many units and equipment. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significant cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both

For efficient process control and monitoring, accurate real-time information of quality variables is essential. To predict these quality (or slow-rate) variables at a fast-rate, in the industry, inferential/soft sensors are often used. However, most of the conventional methods for soft sensors do not utilize prior process knowledge even if it is available. The prediction accuracy of these inferential

In order to recognize the coupling faults of complex industrial processes effectively, this paper proposed an AdaBoost ensemble (AdBE) model based on the reconstruction of local tangent space alignment (RLTSA). First, to obtain the low-dimensional manifold structure embedded in original data space, RLTSA algorithm is designed by constructing the tangent space in the neighborhood of each data point

In tandem hot strip rolling mills, the roll speed of the mill stands is synchronized to ensure a uniform mass flow. A uniform mass flow is desirable to keep the strip tension constant and thus avoid large movements of the loopers which are located between the mill stands in order to control the strip tension. This also improves the general thickness quality and prevents the strip from necking in case

We present a data-driven model predictive control (MPC) framework for systems with high state–space dimensionality. This work is motivated by the need to exploit sensor data that appears in the form of images (e.g., 2D or 3D spatial fields captured by thermal cameras). We propose to use dynamic mode decomposition (DMD) to directly build a low-dimensional model from image data and we use such model

This paper focuses on modeling and optimizing design for continuous conversion of glycerol to 1,3-propanediol by Klebsiella pneumoniae, wherein dilution rate of the feed medium is constantly changed with fermentation time. A nonlinear three-phase hybrid system is first established to describe this bioconversion process. With mean productivity of 1,3-propanediol as objective function and dilution rate

We focus on the Lyapunov-based output feedback control problem for a class of distributed parameter systems with spatiotemporal dynamics described by input-affine linear and semilinear dissipative partial differential equations (DPDEs). The control problem is addressed via model order reduction. Galerkin projection is applied to discretize the DPDE and derive low-dimensional reduced order models (ROMs)

This work proposes a hybrid observer based on an asymptotic observer combined with an extended Kalman filter applied to high-density cultures of S. cerivisiae. The observer estimates the concentrations of biomass and ethanol, and the specific growth rate based on measurements of dissolved oxygen, carbon dioxide, and substrate (glucose). Computational simulations showed that the proposed hybrid observer

This paper is concerned with the robust H∞ model predictive control problem for a class of non-linear systems subject to state and input constraints and with norm bounded disturbances. It is well known that the disturbance degrades the control performance remarkably and can cause system instability. As well as, design the H∞ model predictive control based on the non-linear model is much more challenging

Reinforcement learning (RL) for continuous state/action space systems has remained a challenge for nonlinear multivariate dynamical systems even at a simulation level. Implementing such schemes for real-time control is still of a difficulty and remains largely unanswered. In this study, several critical strategies for practical implementation of RL are developed, and a multivariable, multi-modal, hybrid

Czochralski (Cz) silicon single crystal growth process is a large delay, nonlinear dynamic time-varying system with complex physicochemical reactions, multi-field and multi-phase coupling, and its modeling and control has always been a problem in the automatic control field. In this paper, a hybrid modeling method based on the data-driven model and mechanism model is proposed, and applied to the Cz

The observer design problem for state estimation in a continuous (or semi-continuous) yeast fermentation using Sonnleitner’s bottleneck model is addressed. The system’s detectability and partial observability properties are rigorously established and an observer for on-line state reconstruction is designed as a combination of a geometric and a reduced order asymptotic observer which robustly converges

The multi-sensor data fusion based data-driven fault diagnosis method is a promising approach to detect faults of complex systems. However, in the actual industrial environment, the sampling rate of different sensors is often inconsistent. In order to apply this kind of data to fault diagnosis, the traditional methods are to preprocess it and convert it into single sampling rate data. However, these

Computer-aided fault diagnosis based on the dynamometer card (DC) of the sucker-rod pumping system (SRPS) is a crucial technology to reduce operating costs and increase yield. Currently, the conventional method to implement this technology is the pattern recognition of the DC features. However, the training set of DC that determines the diagnostic accuracy of the method is difficult to obtain because

Minimum variance (MV) benchmark is a quantification that is widely applied to compare actual performance of a control loop against its optimal performance. For linear systems, MV benchmark has been well studied, and several well-practiced techniques have been proposed to evaluate the performance of the systems. However, these techniques may not provide satisfactory performance in some situations. For

This article proposes a liquid level control scheme for a quadruple tank process (QTP) plant. A robust control is proposed using higher order sliding mode (HOSM) control. The four tank setup has opposite tanks actuated by a single pump and unmeasured liquid outflow from upper tanks affecting the levels in the lower tanks. This renders nonlinearity in the dynamics with uncertain behavior. A novel experimentation

In the present work, a fuzzy supervisory control approach combined with an adaptive model predictive controller (AMPC), has been proposed to maximize the productivity of an anaerobic digestion (AD) process, while keeping the operation stable. In the proposed hierarchal control strategy, the set-point of the inner loop is provided by a supervisory controller. In the inner loop an AMPC has been applied

Explicit model predictive control is an established methodology for the offline determination of the optimal control policy for linear discrete time-invariant systems with linear constraints. Nevertheless, nonlinearities in the form of quadratic constraints naturally appear in process models or are imposed for stability purposes in model predictive control formulations. In this manuscript, we present

For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It would result in an abrupt performance decrease. It could be an effective manner to make local monitoring model remember the features of previous modes.

In the industrial flotation process, the operational adjustment is still manual, which mainly relies on the operator’s observation of the flotation froth. Due to limited experience or operation lag, technical indexes such as concentrate grade are difficult to control within the qualified range. In the era of big data, case-based reasoning (CBR) and Bayesian network (BN) are two advanced technologies

Continuous fluidized bed spray granulation processes with external screen-mill cycle are known to be susceptible to nonlinear oscillations. As has been shown recently, mass control may induce periodical oscillation of the particle size distribution in fine grinding regimes, which requires the design of additional stabilizing control. In this contribution a data-driven control approach is investigated

This study addresses the problem of scheduling tank trucks at a fuel distribution terminal. The plant was modeled in the max-plus algebra, applying machine learning to determine process times. With this model, and based on a just-in-time approach, we have developed a predictive controller with two operation modes. This control system aims to prevent the excess of tank trucks inside the loading yard

In this work, control and estimation problems have been studied for a catalytic reversal flow reactor (CFRR). A stabilizing compensator is developed on the basis of the infinite-dimensional state-space description of the CFRR. Linear–quadratic technique is used to design both an optimal state-feedback controller and an output injection operator. The later is developed based on the duality fact between

The nonlinear stochastic dynamics of a class of two-state bioreactors with isotonic or nonisotonic kinetics is analytically characterized with Fokker–Planck (FP) theory, with emphasis on: (i) the spatiotemporal geometry of the two-state probability density function (PDF) motion, (ii) conditions for metastability-based bio-extinction/revival (inexistent in deterministic systems), and (iii) state PDF

Scheduling therapies is a central paradigm in personalized medicine. Modeling and control theoretical approaches are opening an avenue for linking interactions between drugs and diseases in the host. In this paper, considering an inverse optimal approach, we formulate a framework based on a bounded input impulsive control to schedule therapies. Results for stability with a bounded input are introduced

Geological drilling process is operating under complex geological conditions, which may lead to a high risk of downhole incidents and thus compromise the drilling efficiency. To achieve prompt detection of downhole incidents and prevent them from developing to serious drilling accidents, this paper proposes a new data-driven detection method for downhole incidents based on the amplitude change detection

With the increase of drilling depth, complicated geological environments lead to a high risk of loss and kick. Fault diagnosis plays an essential role in minimizing the financial and environmental losses of the drilling process. On account of the temporal correlation of drilling parameters, a fault diagnosis method based on feature clustering of time series data for loss and kick of the drilling process

In this study, a nonlinear robust control policy is designed together with a state observer in order to manage the novel coronavirus disease (COVID-19) outbreak having an uncertain epidemiological model with unmeasurable variables. This nonlinear model for the COVID-19 epidemic includes eight state variables (susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible

The performance of model predictive controllers (MPCs) strongly depends on the precision of the prediction model. Nonlinear systems, such as neutralization reactors, provide special challenges to MPC design. Linear prediction models may be inadequate to describe the process at all operating points. One alternative is the use of artificial neural networks (ANNs) as prediction models. ANNs are nonlinear

Data based approaches have recently gained extensive attention in modern process industries. Accordingly, data based soft sensing technology used for making online predictions of quality variables plays currently an important role in process control and monitoring. Drifts in operating conditions and process characteristics, however, demand novel online learning methods to be developed for maintaining

Fault diagnosis(FD) is vital for monitoring of industrial processes. Actually, both the multiple fault variables and minor faults are inclined to bring wrong diagnosis results. In this paper, a fault variable identification method is proposed for multivariate/minor fault diagnosis. The deviation factor is adopted as the characteristic of the sample, and Bayesian decision theory is adopted to calculate

Control performance assessment (CPA) is vital to ensure the safety of control systems. However, most multivariate CPA methods are limited to the system with explicit knowledge. Recently, it has been recognized that high predictability of closed-loop outputs implies poor control performance. This paper proposes a probabilistic CPA (PCPA), which is compatible with the above promising idea. This paper

Industrial processes usually exhibit great nonlinearity generated from the effects of complex mechanisms, system integrations and multiple working conditions. Although a variety of dictionary learning algorithms have been proposed in recent years for industrial process fault diagnosis, most of them only model the process data via a linear combination of a few dictionary atoms, which cannot effectively

This paper concerns with the JY-KPLS model based transfer learning for the operation optimization of nonlinear batch processes. Due to problems of data insufficiency and uncertainties in a new nonlinear batch process that has just been put into production, the model-(new) process mismatch is usually inevitable, which is also the main reason for the poor performance of the batch process. To solve this

Industrial processes often show typical nonstationarity, resulting from frequent variation of working condition. It is rather challenging to assess the operating performance of nonstationary processes, since the process property is time-variant and the standard of performance levels may differ from working conditions. Besides the nonstationarity, nonlinearity widely exists in industrial processes,

The accurate prediction of rate of penetration (ROP) has a crucial role in improving efficiency and minimizing cost in geological drilling process. Considering the drilling characteristics of strong nonlinearity, complexity, multiple variables and drilling conditions in drilling process, an online hybrid prediction model based on the drilling data is developed to achieve high accuracy prediction of

Decision support tools in the process industry have been gaining relevance, especially for operation under uncertain conditions. This study describes real-time reconciled simulation (RTRS), and analyzes its usefulness as decision-making tool for process operators, especially under unexpected process changes. The proposed methodology is implemented in two case studies in the context of an oil refinery

In the multivariate system, a fault is often caused only by a few variables. However, in traditional fault detection and diagnosis (FDD) methods, all of variables are included in the fault detection index. In this case, the effect of fewer faulty variables on the fault detection index may be weakened by the introduction of a larger number of fault-free variables. Consequently, the FDD performance is

This paper presents a two-layer control structure to address parameter uncertainty within a plant. The lower layer is formulated as a nominal MPC that computes control actions to regulate the underlying plant, and the upper layer computes optimal set-point trajectories for the lower level to track. The upper layer is formulated as an optimization problem that takes into account the closed-loop behavior

Bayesian network is a frequently-used uncertainty reasoning method, which systematically describes relations between random variables. Dynamic Bayesian network is an extension of Bayesian network, which contains the relations between variables at different times. Soft sensor is an important industrial application, in which feature variables are selected to predict the value of the target variables

The structure of the sensor network installed in the plant strongly influences the performance of state estimation techniques. One of them, the Unscented Kalman Filter (UKF), provides significant improvement over other filtering methods. It approximates the true mean and covariance of random variables that undergo nonlinear transformations correctly up to the third order with low computational effort

A robust warning generation method for non-linear systems is presented for forecasting abnormal situations in process systems. Predictive signals were used to issue warning in contrast to traditionally used measured states of the process. Potential unsafe conditions were identified when the most aggressive controller is unable to keep the system within normal operating range, and one or more safety

Isolation of abnormal changes in process variables is an integral component of fault diagnosis, as it provides evidential information for determining the root cause of a detected abnormal event. This task is challenging when the approach to diagnosis does not incorporate knowledge of the process’ nominal behavior, but is instead established solely on historical process data. Though isolation of abnormal

In the refining and chemical industry, distillation is one of the prime processes and is a multivariable control problem, which involves interaction amongst several control loops and exhibit dead time in the dynamic behavior. The analysis and controller design of processes inheriting dead time is a challenging task, and the presence of plant uncertainties and disturbances makes it challenging to exert

Air-to-water heat pumps are one of the most common and energy efficient heating systems for buildings, particularly floor-heating plants. One way to further improve their effectiveness is to control the heat pump exploiting the dependence of its coefficient of performance (COP) on the external temperature and temperature of the return water from the load. In particular, it is possible to exploit the

In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation method based on structured sparsity modelling. The developed method relies on the reconstruction based contribution analysis and the process structure information can be incorporated into the reconstruction objective function in the form of structured sparsity regularization

Abstract Orthogonal collocation on finite elements (OCFE) has been used universally to approximate ODEs to date. For PDEs, this contribution presents a novel discretization scheme applying the methodology of OCFE to discretize both space and time domain simultaneously, named as space–time orthogonal collocation on finite elements (ST-OCFE). Due to the existence of boundary conditions, the selection

This work regards the development of a distributed model predictive control strategy for large-scale systems, as centralized implementations often suffer from non-scalability. The decomposition of the overall system into minimally coupled subsystems as well as their coordination are based on optimality condition decomposition (OCD) and community detection. The OCD approach allows to solve the associated

This article presents a procedure, which is formulated in three stages, to simplify the problem of localizing a single leak in a pipeline with multiple branches. The first two stages involve the fulfillment of generic conditions deduced from head loss and flow rate balances for detecting and locating the specific section where the leak is present. The fulfillment of these conditions allows finding