Process dynamics is widely presented in industrial processes, which can be perceived as temporal correlations. Negligence of dynamic information may result in misleading monitoring results. Therefore, explicit exploration of dynamic information is crucial to process monitoring. In this paper, a new data-driven algorithm called enhanced canonical variate analysis with slow feature (ECVAS) and corresponding

In this paper, design issues of data-driven optimal dynamic fault detection systems for stochastic linear discrete-time processes are addressed without precise distribution knowledge of unknown inputs and faults. Concerning a family of faults with different distribution profiles in mean and covariance matrix, we introduce a bank of parameter vectors of parity space and construct the parity relation

A robust switched predictive control method is designed to deal with the multi-phase batch processes with the inevitable uncertainties, time-varying delay and unknown disturbances. Firstly, the output tracking errors are augmented into the state variables and then the improved robust switched predictive control law is established using the new augmented state variables. To this end, the novel switched

Two tuning techniques are proposed to design decentralized PID controllers for weakly coupled and general MIMO systems, respectively. Each SISO loop is designed separately, and the controller parameters are obtained as a solution of a linear programming optimization problem with constraints on the process stability margins. Despite the SISO approach, loop interactions are accounted for either by Gershgorin

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through

In this paper, the problem of time-varying output tracking is studied in discrete-time descriptor systems. The descriptor system is subject to practical constraints such as input saturation and exogenous disturbances and consists of algebraic equations besides the difference equations in system modeling. The control problem is solved by proposing an observer-based composite nonlinear feedback controller

In this work, a stoichiometry-driven heuristic feedforward control strategy is proposed for controlling the oxygen supply to a biological gas desulfurization process that treats biogas, landfill, and high-pressure natural gas containing H2S and volatile organic sulfur compounds (VOSC). Traditionally, PI or PID feedback control is used when the feed gas contains H2S only. Because the oxidation–reduction

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent

As the deep learning technology develops, many process monitoring methods based on auto-encoder (AE) are designed for the nonlinear industrial processes. However, these methods mainly focus on process variables and ignore the quality indicator which is crucial for the final production. To extract the latent variables which represent both process information and quality information, this paper proposes

This contribution is concerned with the stability problems occurring during the operation of continuous fluidized bed spray granulation processes with external sieve mill cycle. These processes are in general operated by a mass controller, which guarantees that the overall mass of particles in the granulation chamber stays in well-defined bounds. It is well-known that, depending on the milling diameter

The multiple–input multiple–output (MIMO) output feedback (OF) control problem of an exothermic multi-jacket tubular open-loop unstable reactor is addressed. Over its axial length, the reactor has several equally sized cooling jackets. The controller must adjust the jacket temperatures on the basis of per jacket temperature measurements so that the closed-loop system is robustly stable. The problem

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the

Since most real-world processes exhibit both nonlinear and time-varying characteristics, there exists a need for accurate and efficient models that can adapt in nonstationary environments. Also for adaptive control purpose, it is vital that an adaptive model has a fixed small model size. In this paper, we propose an adaptive tunable gradient radial basis function (GRBF) network for online modeling

This paper presents a new approach to building an interval model for an industrial process with uncertainty that employs an interval neural network (INN), which can solve problems such as model structure demands and complexity limitations in the conventional unknown but bounded (UBB) errors method. A new architecture for an interval random vector functional-link network (IRVFLN) and its learning algorithm

Processes in fed-batch using biomass as substrate can be more advantageous, presenting a higher productivity and a lower consumption of enzyme. However, in some cases, processes operating in fed batch may represent a problem in the way the substrate should be fed. In this article, a prototype for solids feeding is developed, which can implement different types of feeding profiles and is controlled

In part I of this series of articles, the concept of overall system reliability was presented for two applications: reliable estimation of variables for steady state linear flow processes, and reliable fault detection and diagnosis for any process. In this part, systematic generation of the proposed system-wide reliability expression is discussed. In particular, an approach for generating the system

Stable pressure control of coke oven gas collectors is difficult due to the problems of nonlinearity, couplings, time-variation, disturbances and data-dropout. This paper proposes a novel chattering-free model free adaptive sliding mode control scheme for the gas collection process of coke ovens. Unlike the conventional data-driven sliding mode control approaches, the proposed controller is based on

Often industrial variables can be difficult to measure due to such factors as extreme conditions or complex compositions. In such cases, soft sensors have been developed that use available system information and measurements to estimate these difficult-to-obtain variables. In practice, the measurements that are to be estimated by a soft sensor are often infrequently measured or delayed. Occasionally

Conventional adaptive monitoring strategies detect anomalies in time-varying process by frequently updating models, which requires high computation complexity and may falsely include abnormal samples. Cointegration analysis (CA) based monitoring strategies can be implemented with less model updating since they are developed based on the extracted long-term equilibrium relationship. However, once the

So far, problems of intermittent fault (IF) detection and detectability have not been fully investigated in the multivariate statistics framework. The characteristics of IFs are small magnitudes and short durations, and consequently traditional multivariate statistical methods using only a single observation are no longer effective. Thus in this paper, moving average T2 control charts (MA-TCCs) with

A recent paper explored the separation of the close-boiling mixture 1,2 propanediol (PDO) and ethylene glycol (EG). The optimum operating pressure using a traditional single vessel was 282 kPa with a reboiler duty of 399 kW. However, the vapor–liquid equilibrium shifts with pressure such that higher pressure is favorable in the PDO-rich region. An interesting steady-state design was developed that

The high computational requirements of nonlinear model predictive control (NMPC) are a long-standing issue and, among other methods, learning the control policy with machine learning (ML) methods has been proposed in order to improve computational tractability. However, these methods typically do not explicitly consider constraint satisfaction. We propose two methods based on learning the optimal control

With data in industrial processes being larger in scale and easier to access, data-driven technologies have become more prevalent in process monitoring. Fault classification is an indispensable part of process monitoring, while machine learning is an effective tool for fault classification. In most practical cases, however, the number of fault data is far smaller than normal data, and this imbalance

Hydrogen production systems supplied by photovoltaic solar energy have nonlinear dynamics and discontinuities which must be taken into account when a control system is applied. The main purpose of the control system is to maintain the electrolyzer current at the desired operating point and, at the same time, to optimize the grid energy consumption despite the solar energy variability. Classic controllers

A non-linear model is developed to capture the transient flow and pressure behaviour of methane-rich gas (MRG) in industrial pipelines for the use in simulation and control applications. Hyperbolic partial differential equations describe the pipe pressure and flow profiles, and composition analyses are used to develop the physical properties of the gas appearing in the pipe segment equations. The spectral

This work presents a pulsatile Zone Model Predictive Control (pZMPC) for the control of blood glucose concentration (BGC) in patients with Type 1 Diabetes Mellitus (T1DM). The main novelties of the algorithm – in contrast to other existing strategies – are: (i) it controls the patient glycemia by injecting short duration insulin boluses for both, the basal and bolus infusions, in an unified manner

A model-based method is presented for the simultaneous selection of tests and sensors in fault detection and isolation (FDI) of systems subject to uncertainty. Tests and sensors are selected out of a continuous or discrete set of options based on their contribution to information gain with respect to fault identifiability. The objective of the optimization of the tests designed is to maximize the sensitivity

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from

In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC scheme uses an online optimized artificial periodic orbit to ensure recursive feasibility and constraint satisfaction despite unpredictable changes in the economic

This paper proposes a hybrid Gaussian process (GP) approach to robust economic model predictive control under unknown future disturbances in order to reduce the conservatism of the controller. The proposed hybrid GP is a combination of two well-known methods, namely, kernel composition and nonlinear auto-regressive. A switching mechanism is employed to select one of these methods for disturbance prediction

This paper studies design and implementation of an enhanced multivariable adaptive control scheme for an uncertain nonlinear process exposed to actuator faults. For adaptive fault compensation, a model reference adaptive control (MRAC) strategy is utilized as main controller. A new adaptation algorithm making possible to improve transient performance of adaptive control is integrated to the controller

The potential of electricity-intensive chemical plants to engage in demand response (DR) initiatives in support of power grid operations has been the subject of many conceptual studies. In this work, using an industrially-relevant model of an air separation unit, we undertake an extensive simulation study of moving horizon (MH) scheduling approaches, where we “close the scheduling loop” based on updated

This work presents a prediction-based scheme to control a class of unstable delayed (bio)-chemical processes. The main characteristic of the control scheme is that it is based on a predicted future state that is estimated using a prediction-observation protocol that allows compensating large time-delays. The convergence of the prediction error is shown through standard stability arguments. The stability

In this paper, we present a computationally efficient economic NMPC formulation, where we propose to adaptively update the length of the prediction horizon in order to reduce the problem size. This is based on approximating an infinite horizon economic NMPC problem with a finite horizon optimal control problem with terminal region of attraction to the optimal equilibrium point. Using the nonlinear

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE

Stacked auto-encoder (SAE)-based deep learning has been introduced for fault classification in recent years, which has the potential to extract deep abstract features from the raw input data. However, SAE cannot ensure the relevance of deep features with the fault types due to its unsupervised self-reconstruction in the pretraining stage. To overcome this problem, a stacked supervised auto-encoder

Move blocking is an input parameterization scheme that fixes the decision variables over arbitrary time intervals, commonly referred to as blocks, and it is widely implemented in model predictive control (MPC) to reduce the computational load during on-line optimization. Since the blocking position acts as the search direction in the solution space, selection of the blocking structure has a significant

Economic model predictive control (EMPC) is a model-based control scheme that integrates process control and economic optimization, which can potentially allow for time-varying operating policies to maximize economic performance. The manner in which an EMPC operates a process to optimize economics depends on the process dynamics, which are fixed by the process design. This raises the question of how

Process monitoring and quality prediction are crucial for maintaining favorable operating conditions and have received considerable attention in previous decades. For majority complicated cases in chemical and biological industrial processes with particular nonlinear characteristics, traditional latent variable models, such as principal component analysis (PCA), principal component regression (PCR)

Variable static pressure is the main strategy for air volume adjustment in variable air volume air-conditioning system and can offer huge energy savings compared with constant air volume systems. When parameter set-points reset with load, dynamic processes are excited. Some processes take a long time to reach a new stable state with large overshoots. Improved iterative learning control (ILC) algorithm

In this paper the decomposition of the integrated cyclic scheduling and dynamic optimization problem is analyzed using community detection. Different production systems are examined and based on the results of the community detection and the centrality of the constraint unipartite graph, a two level hierarchical structure is identified, with the scheduling problem in the first level and the dynamic

Hydrogen production from Proton Exchange Membrane Water Electrolysis (PEMWE) system is attracting attention due to its ability to circumvent the effect of the intermittent nature of variable renewal energy. However, the relatively high operating temperatures of the PEMWE system exacerbates the degradation of polymer membranes in PEMWE system. This paper describes the development of an optimal thermal

Each mixing valve is characterized by its kvs-value and the valve characteristics. In mounted condition, the valve characteristics are deformed and the degree of the deformation is determined by the valve authority. Considering already installed mixing valves, neither the valve authority nor the course of the undeformed valve characteristics are exactly known. The lack of information poses a challenge

Reduced models enable real-time optimization of large-scale processes. We propose a reduced model of distillation columns based on multicomponent nonlinear wave propagation (Kienle 2000). We use a nonlinear wave equation in dynamic mass and energy balances. We thus combine the ideas of compartment modeling and wave propagation. In contrast to existing reduced column models based on nonlinear wave propagation

This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled

Split range control is used to extend the steady-state operating range for a single output (controlled variable) by using multiple inputs (manipulated variables). The standard implementation of split range control uses a single controller with a split range block, but this approach has limitations when it comes to tuning. In this paper, we introduce a generalized split range control structure that

We propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals

In this paper, we develop three methods to achieve reliable closed-loop, tool face control for directional drilling operations. This is a necessary step to achieve closed-loop, automated directional guidance. Our algorithms combine existing industry top-drive controllers with new control approaches. The torsional model we use for the drill string has been field validated and takes into account the

Recent studies on modular and distributed manufacturing have introduced a new angle to the traditional economies of scale that claim that large plants exhibit better efficiencies and lower costs. A modular design has several advantages, including higher flexibility of decisions, lower investment costs, shorter time-to-market, and adaptability to market conditions. While design flexibility is a widely

A multivariate statistical process monitoring scheme should be able to describe multimodal data. Multimodality typically arises in process data due to varying production regimes. Moreover, multimodality may influence how easy it is for process operators to interpret the monitoring results. To address these challenges, this paper proposes an on-line monitoring framework for anomaly detection where an

In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven