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Combustion power plants currently face major performance challenges, which require robust control design methods. Extensive relative gain analysis was conducted in this paper to generate plantwide control structures for a full-scale once-through circulating fluidized bed boiler. No such study has been reported before for steam boilers. The partial relative gain was employed to generate decentralized control structures based on integral controllability with integrity. The approach provided feasible control structures and verified that basic turbine-following boiler control is preferable in terms of controllability. The steady-state results were extended with the dynamic relative gain array for higher frequencies, which revealed that boiler-following control becomes feasible for faster disturbances. The results highlight the complex interactions between steam pressure and output electrical power control, as well as the loop interactions caused by the feedwater flow in the once-through steam path.

Integrated control and process design (ICPD) practices focus on an interaction between process and control design. The paper investigates ICPD design in circulating fluidized bed (CFB) power plants, which face increasing load change, efficiency and emission requirements. The state of ICPD research is examined and a classification of its methodologies is provided. The applicability of ICPD to large-scale CFB boilers is discussed for the first time based on this classification. Two ICPD case studies with a simple steam path mass storage model are presented for an industrial CFB boiler, with the aim of illustrating possibilities and challenges related to boiler ICPD. The steam mass storage amounts of the boiler superheating and evaporation sections are modified based on the dynamic relative gain array and closed-loop process optimization to generate processes with improved constant pressure mode output power setpoint tracking performance.

Oxy combustion has been introduced in power plants for facilitated CO2 capture and storage (CCS). Existing process controls for air-fired circulating fluidized bed (CFB) boilers are not necessarily optimal for oxy-firing. New degrees of freedom and alternative structures for control should be carefully analyzed for performance and flexibility. In this paper, the application of self-optimizing control (SOC) and partial relative gain (PRG) methods is presented for the early phases of control structure design. Controlled variable (CV) candidate sets are studied using the SOC concept. For the proposed CV sets, PRG analysis is used to reveal possible SISO loops with manipulated variables (MVs). Structures with promising results from both analyses were discovered.

The problem of curse-of-dimensionality in finite state and action Markov decision processes is considered using iterative clustering of closed-loop data and repeated discretization of the state space process model. The performance of the control design approach is demonstrated using a multi-input van der Vusse continuous stirred tank reactor control benchmark. It is demonstrated that the finite state description provides a simple implementation of Bayesian state estimation using cell filters, and dynamic programming gives means to conduct optimization of closed-loop performance of a nonlinear stochastic multidimensional chemical plant.

Integrated control and process design is considered for a power plant to obtain improved load changes in output electrical power (MWe). Fast load transitions are increasingly needed in conventional power plants, which calls for a deeper integration between the boiler and its control system. An integrated design methodology is applied to an industrial boiler steam path in this paper; no past reports of such an application exist in the literature. The methodology utilizes dynamic optimization together with performance relative gain array and closed-loop disturbance gain controllability analysis. The aim is to optimize the boiler steam storage distribution, the turbine valve operation, and the electrical power and main steam pressure controllers during different MWe ramp reference trajectories. The methodology was successful in defining closed-loop designs with excellent MWe setpoint tracking, small steam pressure disturbances and minimal steam throttling. The results also highlighted the challenges related to integrated design in power plants.

Model-based optimization tools for demand side management using supermarket refrigeration systems are considered. A model for medium-sized supermarket freezer and cooler temperatures is developed, as well as for the CO 2 compressor cycle power consumption. Model parameters are estimated from measured data. The formulation of an optimization problem vs. various electricity markets is briefly discussed, and an illustrative numerical simulation of demand side management optimization in a supermarket is provided. The developed model provides a feasible tool for including the supermarket thermal capacity dynamics into the optimization problem.

This paper contributes to the field of Lyapunov stability theory where positive definite radially unbounded functions – which can be interpreted as generalized energy for the system of interest – play a central role. Given a discrete–time LTI system subject to hard constraints, which are affine in state and control variables, upper and lower barriers are developed which bound the fastest possible decay of generalized energy over the set of admissible control policies for which the closed–loop system is asymptotically stable on a compact polyhedral set including the origin. It is shown that the lower barrier is a greatest feasible lower bound of the fastest possible decay of generalized energy over any set of admissible control policies. A simple example is given to illustrate the main results.

A wealth of control designs and experience are available for traditional air combustion in circulating fluidized bed (CFB) boilers. For the novel process of oxy combustion (for facilitated CO 2 capture) input gas compositions and flows can be adjusted independently, which decouples fluidization and oxygen carrying tasks and introduces new degrees of freedom and alternatives for control. The self-optimizing control approach (as formulated by Skogestad and colleagues in the 2000s) was used with steady-state approximations of a validated dynamic model for a pilot-size CFB combustor to study how the added degrees of freedom should be used. Instead of centralized online optimization of setpoints, self-optimizing control searches for a set of controlled variables which can be kept at constant setpoints despite disturbances and measurement errors, resulting in performance with acceptable steady-state loss. Results for air firing support method validity by suggesting the common practice in control; keeping power, flue gas O 2 and primary air/fuel feed ratio constant. For oxy firing, various control structures could satisfactorily compensate for studied disturbances and errors. Results suggest direct oxidant O 2 % control or simpler feed-forward solutions in line with current industrial CFB control, or alternatively using the added degrees of freedom for controlling variables such as furnace temperatures. Differences in, e.g. controllability, dynamic performance and implementation cost are relevant in further studies. The results serve as a first step in oxy-CFB control studies, suggesting candidate structures for dynamic analysis.

Simulation-based non-linear active suspension control design for MacPherson systems is considered. A nonlinear dynamic model for the MacPherson suspension system is derived. The model nonlinearities and the dynamic behaviour of the system is illustrated by simulations. The design of controllers and state estimators using finite state Markov models is briefly outlined, and applied for nonlinear active suspension control system. The study illustrates the potential of the finite Markov chains approach in non-linear active suspension control, emphasizing the possibility to move computational load due to simulations to off-line design.

The choice of a pump schedule has a great influence on the cost efficiency of a system, when the energy consumption charge changes during the day. The pump scheduling problem for a sub-system of a regional water network is considered, and the formulation of the problem as a MDP is given. The system under stochastic demand is modelled and an optimal controller is designed. Two implementations are considered: a direct application of MDP control actions, and local PI-controllers with a feedforward part. The experimental section shows computer simulations illustrating the considered approaches.

Energy efficiency is one of the main aspects in the Kraft pulping process. In Kraft pulping, the delignification of the pulp is mainly controlled by temperature and alkali. However, the quality is usually measured only in the blowline of the digester resulting in a control delay several hours. This affects to the quality control and also the energy efficiency of the plant. In this study, energy efficiency in the continuous Downflow Lo-Solids ® cooking application is studied using model-based approach. Temperature and alkali controls are considered and some suggestions are proposed.

The choice of a pump schedule has a great influence on the cost efficiency of a system, when the energy consumption charge changes during the day. The pump scheduling problem for a simplified regional water network is considered, and the formulation of the problem as a Markov decision process is given. The experimental section shows computer simulations and compares the results with a novel evolutionary approach based on neutral genetic algorithm techniques.

Cognition and control processes share many similar characteristics, and decisionmaking and learning under the paradigm of multiple models has increasingly gained attention in both fields. The controlled finite Markov chain (CFMC) approach enables to deal with a large variety of signals and systems with multivariable, nonlinear, and stochastic nature. In this article, adaptive control based on multiple models is considered. For a set of candidate plant models, CFMC models (and controllers) are constructed off-line. The outcomes of the CFMC models are compared with frequentist information obtained from on-line data. The best model (and controller) is chosen based on the Kullback–Leibler information. This approach to adaptive control emphasizes the use of physical models as the basis of reliable plant identification. Three series of simulations are conducted: to examine the performace of the developed Matlab-tools; to illustrate the approach in the control of a nonlinear nonminimum phase van der Vusse CSTR plant; and to examine the suggested model selection method for the adaptive control.

This paper deals with regulation control on the basis of genealogical decision trees (GDTs). GDTs are a population-based random search technique for solving sequential multimodal and multivariable trajectory tracking problems, when gradient information is not available or does not exist. A direct application of GDT results in an open-loop control. In this paper, feedback regulation based on GDT is considered. In the proposed scheme, GDTs are used for solving off-line a number of predictive control problems; a finite set of initial states is then constructed from these simulations, for each of which an optimal control sequence has been computed. Natural handling of missing state vector measurements is provided. Numerical examples dealing with the van der Vusse CSTR illustrate the feasibility and the efficiency of this feedback control algorithm. A discussion on alternative approaches and a numerical comparison with the Markov-decision-process-based optimal policy are provided. Copyright © 2008 John Wiley & Sons, Ltd.

Controlled finite Markov chain (CFMC) approach can deal with a large variety of signals and systems with multivariable, non-linear and stochastic nature. In this paper, adaptive control based on multiple models is considered. For a set of candidate plant models, CFMC models (and controllers) are constructed off-line. The state transitions predicted by the CFMC models are compared with frequentist information obtained from on-line data. The best model (and controller) is chosen based on the Kullback–Leibler distance. This approach to adaptive control emphasizes the use of physical models as the basis of reliable plant identification. Three series of simulations are conducted: to examine the performance of the developed Matlab-tools; to illustrate the approach in the control of a non-linear non-minimum phase van der Vusse CSTR plant; and to examine the suggested model selection method for the adaptive control.

Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented.

The goal of this paper is to design a new control algorithm for open-loop control of complex systems. This control approach is based on a genealogical decision tree for both regulation and tracking control problems. The idea behind this control strategy consists of associating Gaussian distributions to both the norms of the control actions and the tracking errors. This stochastic search model can be interpreted as a simple genetic particle evolution model with a natural birth and death interpretation. It converges on probability. A numerical example dealing with the control of a fluidized bed combustion power plant illustrates the feasibility and the performance of this control algorithm.

This paper considers identification of a multiple-input single-output Wiener model of a new type of superheater integrated to a CFB boiler. The static part is based on energy- and mass balances. Simultaneous estimation of model parameters in both static and dynamic parts was based on the industrial data from an 80 MW CFB boiler. The results indicate that the approach can be satisfactorily used to capture and describe the steam temperature in a wide range of variation in the input variables.