Showing papers in "Journal of Process Control in 2011"

TL;DR: In this paper, a set of tuning rules for standard (integer-order) PID and fractional-order PID controllers is presented, based on a first-order plus-dead-time model of the process, in order to minimize the integrated absolute error with a constraint on the maximum sensitivity.

TL;DR: The Droop model, which has been widely used to predict microalgal behaviour under nutrient limitation, is reviewed, and a model for raceways or planar photobioreactors, when both light and nutrients are limiting is details.

TL;DR: The role RTO serves in the hierarchy of control and optimization decision making in the plant is discussed, and the key steps of the RTO layer and the coordination with MPC are outlined.

TL;DR: In this paper, an analysis on how the characteristics of flotation processes, the quality of measurements of key variables, and the general lack of realistic dynamic models, are delaying the appropriate use of predictive control is presented.

TL;DR: In this article, two approaches are developed for constructing the maximum likelihood estimates (MLE) of the state and measurement noise covariance matrices from operating input-output data when the states and/or parameters are estimated using the extended Kalman filter (EKF).

TL;DR: In this paper, the authors proposed two economically oriented nonlinear model predictive control (NMPC) formulations and proved nominal stability for both formulations, and showed that the asymptotic stability of the transformed system is equivalent to that of the original system.

TL;DR: In this paper, five existing diagnosis methods are analyzed and it is shown that they can be unified into three general methods, making the original diagnosis methods special cases of the general ones.

TL;DR: The objective of this paper is to design and implement in a four-tank process several distributed control algorithms that are under investigation in the research groups of the authors within the European project HD-MPC.

TL;DR: In this paper, the authors consider the control of several subsystems coupled through the inputs by a set of independent agents that are able to communicate and assume that each agent has access only to the model and the state of one of the subsystems.

TL;DR: A novel nonlinear nonconvex optimizer is proposed that improves the objective function and is feasible at every iterate and hence the controller is truly distributed.

TL;DR: In this article, a two-layer architecture for dynamic real-time optimization with an economic objective is presented, where the solution of the dynamic optimization problem is computed on two time-scales.

TL;DR: This paper analyzes several existing methods to incorporate measurement delays and reinterpret their results under a common unified framework (for Extended Kalman Filter) and presents extensions to handle time-varying and uncertain delays, as well as out of sequence measurement arrival.

TL;DR: This paper introduces an efficient one-class classification method for batch process monitoring, called support vector data description (SVDD), which has no Gaussian assumption of the process data, and is also effective for nonlinear process modeling.

TL;DR: The paper presents a systematic framework for exploiting the potential of the decomposition structures as a way to obtain different parallel algorithms, each with a different tradeoff among convergence speed, message passing amount and distributed computation architecture.

TL;DR: In this paper, a thermodynamic pseudo-Hamiltonian formulation of CSTR was proposed, where Gibbs free energy and the opposite of entropy can be chosen as Hamiltonian function respectively, and the so-called Interconnection and Damping Assignment Passivity Based Control method was applied to stabilize the system at a desired state.

TL;DR: Inclusion of the detection delay in the alarm design makes the design more reliable and provides better insight to the consequences.

TL;DR: Results obtained when used several simulation scenarios show the effectiveness of both the partitioning approach and the DMPC strategy in terms of the reduced computational burden and the admissible loss of performance in contrast to a centralised MPC strategy.

TL;DR: In this article, a probability-based identification method is proposed to identify a nonlinear process which operates over several working points with consideration of transition dynamics between the working points, where only excitation signals around each operating point are required to identify linear models of the local dynamics, and then the local models are synthesized with transition data to construct a global LPV model.

TL;DR: In this paper, the authors describe a generalized control loop for controlling strongly disturbed, poorly modeled, and difficult to measure processes, such as those involved in the mineral processing industry, the peripheral tools of the control loop (fault detection and isolation system, data reconciliation procedure, observers, soft sensors, optimizers, model parameter tuners).

TL;DR: In this paper, a completely event-based two-degree-of-freedom proportional-integral controller is presented, which is based on decoupled solutions for the set-point following and the load disturbance rejection tasks.

TL;DR: In this paper, an extension of the inverted decoupling approach that allows for more flexibility in choosing the transfer functions of the decoupled apparent process is presented, highlighting that the complexity of decoupler elements is independent of the system size.

TL;DR: In this paper, a P-type steady-state iterative learning control (ILC) scheme is applied to the boundary control of a class of nonlinear processes described by partial differential equations (PDEs), which cover many important industrial processes such as heat exchangers, industrial chemical reactors, biochemical reactors, and biofilters.

TL;DR: In this paper, a novel online algorithm that deals explicitly with model errors for distributed model predictive control (DMPC) is proposed, which requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem.

TL;DR: In this paper, a number of new approaches that have been shown to overcome the limitations of existing calibration/modelling methodologies and describes a practical system which would enhance robustness of the closed-loop process control system and overall control strategy.

TL;DR: The proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non- Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution.

TL;DR: A new distributed model-predictive control method is introduced, which is based on a novel distributed optimization algorithm, relying on a sensitivity-based coordination mechanism, and an analysis of the method with respect to its convergence properties is provided.

TL;DR: In this article, a decentralized model predictive control (DMPC) scheme for large-scale dynamical processes subject to input constraints is proposed, where the global model of the process is approximated as the decomposition of several (possibly overlapping) smaller models used for local predictions.

TL;DR: Results show that the proposed adaptive model with the ALS-SVM method is able to track the time-varying characteristics of a boiler combustion system.

TL;DR: In this paper, the authors proposed a new method using internal model control (IMC) to design Smith delay compensation decoupling controller for multivariable non-square systems with transfer function elements consisting of first order + time delay.

TL;DR: In this article, a conceptual distributed control framework for electrical grid integrated with distributed renewable energy generation systems is proposed to enable the development of the so-called smart electrical grid, and two supervisory predictive controllers via model predictive control are designed to operate the integrated system taking into account short-term and long-term optimal maintenance and operation considerations, respectively.