A Real Time Optimization (RTO) algorithm is proposed to optimize the reactors’ propane content for the Spheripol Polypropylene process. This algorithm improves the present approach of arbitrarily selecting “optimum” propane values, based on monthly/yearly average balances. The optimum propane content depends on factors such as the costs of offgas and catalysts, the propane content in the feedstock

Sludge bulking is very common in wastewater treatment process (WWTP), which will degrade the operation performance or even destroy the process. In order to diagnose sludge bulking accurately, a data-knowledge-driven diagnosis (DKD) method is proposed to identify the occurrence and cause variable in this paper. This proposed DKD method contains the following advantages. First, a data-driven detection

The paper presents a novel model calibration and validation strategy of membrane bioreactor (MBR) for wastewater treatment. The approach is based on a dynamic model of the activated sludge process and it consists simultaneously on estimating the model’s parameters and computing the dissolved oxygen control input. Activated sludge model No. 1 (ASM1) has been widely used to describe the biological process

For large-scale process monitoring, traditional decentralized monitoring methods fail to discriminate real faults from normal operation deviations. This paper proposes a novel decentralized method for monitoring large-scale industrial processes by exploring serial correlations and local manifold structures of the data. A block division strategy based on maximal information coefficient-spectral clustering

The uncertainties in system and model parameters arising from the volatility of market, weather and operating conditions pose a major challenge to the optimal short-term operation of cascaded hydropower systems. The dynamic operating environment resulting from the fluctuating parameters greatly impacts the scheduling of power generation and generating unit commitment in such systems. This article focuses

In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation framework based on structured sparsity modeling. 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

An online adaptive deep neural network (DNN) scheme has been introduced for the tracking control of a nonlinear bioprocess with uncertain internal dynamics. First, a detailed controllability analysis is conducted for the Lutein bioprocess to represent the bioprocess as a nonlinear system in affine form. Next, a controller consisting of a DNN-based function approximator is designed for the nonlinear

In this work, a discrete-time output regulator design is proposed for a class of gas pipeline networks to meet various operating requirements in energy scheduling. Based on the isothermal Euler equations, linearized continuous-time gas pipeline network models with boundary actuation and sensing in the infinite-dimensional space are established, with consideration of Rankine–Hugoniot conditions at junction

Soft-sensor technique is often used to estimate key variables in industrial manufacturing, of which the commonly used approaches as the mechanism modeling and data-driven modeling both have their limitations. To take full advantage of the modeling methods and overcome the problems of nonlinearity, unmodeled dynamics and unlabeled data in industrial manufacturing, a hybrid modeling method combining

Researchers keep trying to find a way to reduce the requirement knowledge of Multivariate Process for performance assessments. Till now, the knowledge of interactor matrix or first several Markov parameter matrices are at least required to obtain the minimum variance benchmark in the performance assessment of Multivariate Process. In this paper, a novel minimum variance performance assessment technique

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 of discrete

With the increasing demands on strip product quality, conventional control methods cannot break the cold rolling production bottleneck The development of advanced control algorithms provides theoretical support for the improvement of strip accuracy and product stability of the tandem cold rolling process. Considering the complex characteristics of the system, a distributed model predictive control

Distributed real-time optimization (RTO) enables optimal operation of large-scale process systems with common resources shared across several clusters. Typically in distributed RTO, the different subsystems are optimized locally, and a centralized master problem is used to coordinate the different subsystems in order to reach system-wide optimal operation. This is especially beneficial in industrial

This paper presents the novelty RS (Risk-Sensitive) H2 optimal control problem with criterion J2, showing the energy of the control spent into the process, H∞ RS control approach with level of attenuation λ1 is obtained, with cost criterion J∞. Robustness is added when hybridized H2∕H∞ RS sub-optimal control problem is solved, as optimization approach, taking J2 as cost function criterion, with H∞

Shifting away from the traditional mass production approach of the process industry, towards more agile, cost-effective and dynamic process operation, provides motivation for next-generation smart plants. The control system for smart plants needs to be capable of dynamically handling a wide range of operating conditions, whilst minimising operation costs during transitions, in addition to efficiently

In the zinc roasting process, the stability of the roasting temperature directly affects the product quality. However, the stabilizing control of the temperature inside a large-scale zinc roaster encounters complex process characteristics, fluctuating working conditions and delayed detection of product quality. The complex concentrate supply gives rise to fluctuations of zinc concentrate composition

Many industrial model predictive control applications, called non-square systems, have more variables to be controlled than manipulated variables available. At these cases, the control objectives are related to keep the controlled ones within a range, instead in reference value (setpoint). Assessing the model quality of these controllers is fundamental, however, most available MPC assessment methods

This work introduces a multi-objective optimization strategy to handle conflicting set-point tracking and economic objectives in a two-layer hierarchical control framework. A dynamic multi-objective real-time optimizer (DMO), incorporated in the upper layer, handles multiple control objectives with set-point tracking being the higher priority objective and computes optimal plant trajectories. This

This paper is concentrated on two new distributed data-driven optimal fault detection approaches in large-scale systems using a group of sensor blocks, each of which accesses part of the process variables. Towards this end, an optimal fault detection problem is first formulated and solved, which lays a foundation for further distributed studies. Based on it, the first distributed data-driven optimal

Nonlinear model predictive control (NMPC) can directly handle multi-input multi-output nonlinear systems and explicitly consider input and state constraints. However, the computational load for nonlinear programming (NLP) of large-scale systems limits the range of possible applications and degrades NMPC performance. An NLP sensitivity based approach, advanced-step NMPC, has been developed to address

Many continuous industrial processes operate in different steady states with different grades or products. The switching between two steady states is called transition. Transition consists of a series of operation changes that should be carried out in proper order, within certain magnitudes and time region. Since faulty operation may lead to increase in inferior products or even hazard events, monitoring

The abnormal operating mode of the iron ore sintering process will produce sinter ore with low yield and poor quality. It is of high economic value to ensure that the sintering process runs under normal operating mode. An intelligent decision-making strategy based on the forecast of the abnormal operating mode for the iron ore sintering process is presented in this paper. First, a fuzzy rule-based

In this paper, the stochastic optimization blending operation is applied to the alumina production in this paper. A new binomial distribution based stochastic scenario optimization used together with the sample selection approach is utilized to design the optimal set point for control, under which the probability of quality indices of the raw slurry being within the tolerance region is high enough

State estimation is an important problem in distributed parameter system especially with nonlinear dynamics in industrial process. An extended Luenberger observer based on the eigen-spectrum of the system operator is developed in this paper to handle this problem. The distributed parameter system is projected into a finite-dimensional subspace where a low-order ordinary differential equation describing

This manuscript introduces an optimal detuning approach for designing a centralized PI control system for multi-input multi-output (MIMO) processes. Based on the approach, two multivariable PI controller designs are proposed in this manuscript. The proposed approach formulates the centralized PI controller design problem in an optimization framework and transforms it into an equivalent detuning parameter(s)

Most modern chemical processes consist of a number of process units interconnected with mass and energy flows, often with energy integration and materials recycle loops. As such, faults (process faults, actuator faults, or sensor faults) often propagate to multiple process units (subsystems), causing significant difficulties in fault diagnosis for plantwide systems. In this paper, a general distributed

In this paper, a novel data-driven model building method is proposed for predicting one-dimensional product crystal size distribution (CSD) or chord length distribution (CLD) of batch cooling crystallization processes, based on only batch run data. The proposed model relating the manipulated variable of cooling rate to the product CSD are constructed by two classes of basis functions, one is the wavelet

We revisit two-step mass-balance models of biological processes as met to describe numerous biological systems including the anaerobic digestion or the nitrification process in view of its global stabilization. We show that when a bi-stability occurs, it can be possible to globally stabilize the dynamics toward an unique positive equilibrium by increasing the dilution rate. We give sufficient conditions

Multivariate statistical analytical methods, such as principal component analysis (PCA) and canonical correlation analysis (CCA), are widely used to extract useful information from data collected in modern industrial processes. Their dynamic extensions are also designed intensively in the literature to exploit dynamic variations in the processes. However, none of these algorithms consider the inevitably

Root cause diagnosis is an important step in process monitoring, which aims to identify the sources of process disturbances. The primary challenge is that process disturbances propagate between different operating units because of the flow of material and information. Data-driven causality analysis techniques, such as Granger causality (GC) test, have been widely adopted to construct process causal

Ammonium removal is a key step in wastewater treatment which can be accomplished biologically. An interesting process option for this purpose is coupling partial nitritation with the Anammox process. The goal of the partial nitritation process is to convert half of the ammonium in the influent stream into nitrite, so both can be later converted into dinitrogen gas by the Anammox reaction. To obtain

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