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In this paper, the effects of variation in ambient temperature, flue gas temperature and condenser pressure over performance of boiler and turbine cycle is presented. The study is carried out with EES software. The change in ambient temperature is seriously deteriorating the boiler exergetic performance as its exergy efficiency reduces by 2.5% with increase in environmental temperature from 27C to 45C while the boiler total energy loss reduces almost 1% for same increase in ambient temperature. The turbine second law efficiency is affected slightly by ambient temperature. Increase in temperature of exhaust flue gas has adverse effect over boiler energy efficiency, which reduces by almost 1% with flue gas temperature variation from 110C to 130C. The increase in condenser pressure is reducing the turbine energy efficiency to more than 3% with variation from 0.05bar to 0.3bar. Condenser exergy efficiency is decreasing sharply with increase in its pressure. The effect of variation in condenser pressure over net output of the boiler-turbine cycle has also been studied and it is found to be decreasing with increase in condenser pressure. Keyword: Ambient temperature, Boiler-turbine cycle, Condenser pressure, Flue gas, Plant performance.

A robust model predictive control (MPC) with bi-level optimization is proposed for nonlinear boiler-turbine system. The nonlinear dynamics are described by multiple local models linearized at distinct operating points. A global linear parameter varying (LPV) model is constructed by combining the linearized local models. In order to combine the local models smoothly, an exponential weighting coefficient determined by the system states is applied. The bi-level optimization is proposed to optimize the control moves and control policy respectively. A controller model is designed as the inner optimization to calculate the suitable control policy under different operating conditions. The closed-loop robust MPC is designed to optimize the control moves to improve economic performance. Simulations under wide operating conditions have demonstrated the effectiveness of the proposed robust MPC method, by applying which the economic performance of the nonlinear boiler-turbine system is improved.

Coordinated controllers for coal-fired ultra-supercritical (USC) boiler-turbine power units in the new flexible-operation mode face many challenges, such as faster load-following rate over wider-ra...

A controller-weighted multi-model predictive control (MMPC) strategy based on local model network (LMN) is proposed to address the nonlinearity and wide operating range of the boiler-turbine (B-T) system with constraints. The LMN model of the nonlinear plant is identified off-line based on data-driven modeling method. Since each local model is valid only in local regime, different local constraints are considered in designing local predictive controllers corresponding to different local models. The local controllers are run in parallel and each controller is assigned with a weight by the implicit scheduling unit. The weighted sum of the outputs of local controllers is taken as a global control signal and applied to the plant. The efficacy of the proposed MMPC is validated by simulations on a boiler-turbine system.

With recent development of renewable energy, the once-through boiler-turbine (OTBT) units in China are required to improve operating flexibility in order to absorb more renewable energy integrated into power grid. However, it is a challenge for control systems to provide load following capability while maintaining safe operation. To this end, this paper proposes a dynamic nonlinear model of OTBT units by incorporating superheated steam temperature dynamics. Model structure is derived from mass and energy conservation laws by using a lumped parameter method, and parameters and functions in the structure are identified by using an optimization algorithm with running data. Besides, the model proposed contains the influence of total spray water flow on steam enthalpy in separator and on superheated steam temperature. Validation results show that the proposed model has acceptable steady-state and dynamic accuracies as well as a proper physical structure, and it can be used for controller design to improve control performance.

The present work takes up the boiler turbine process for control system design and its optimization. The control system is designed and optimized, to exhibit the best control performance, using two different optimization techniques, called simulated annealing (SA) and genetic algorithm (GA). Their control performance is also compared with that of controller designed, for the same process, using Ziegler–Nichol (ZN) technique. All the simulations have been done on MATLAB software.

Improving the technical, economic, and environmental performance of the power plant is the most important direction for improving the characteristics of the ship’s propulsive complex. This issue can be solved most effectively by improving the design, repair, and maintenance of the fuel supply system, which significantly affects the quality of the fuel equipment and determines the working process of the steam boiler. Performance indicators of fuel equipment are determined not only by the stability of its design and adjustment parameters and parameters of the fuel supply process, but also by the physical and chemical composition of the fuel.

It is extremely challenging to control ultra-supercritical boiler–turbine unit on account of its nonlinearity, coupling among multi-variables, hard input constraints, and unknown disturbances. An offset-free output-feedback stable model predictive control (MPC) (OFOF-SMPC) for nonlinear constrained ultra-supercritical boiler-turbine units is investigated. It is shown that the OFOF-SMPC is composed of a disturbance and state observer, a steady-state target calculator as well as a stable MPC, which can remove unknown disturbances from output channels and ensure the stability of the closed-loop system with input constraints. Simulation results demonstrate the effectiveness of the proposed algorithm.

In order to optimize the economic objectives while regulating the ultra-supercritical boiler-turbine unit, an improved utopia tracking based multiobjective fuzzy model predictive control is proposed in this paper. This method uses a hierarchical structure, in which the quasi-infinite horizon fuzzy model predictive control is designed for the steady-state compromise point in the upper layer and the utopia tracking based multiobjective control is devised in the lower layer. To ensure the closed-loop stability, a constraint with respect to the optimal value function about the compromise point is enforced. The simulation results on a 1000MW USC boiler-turbine unit model verify the merits of the proposed strategy in achieving less fuel consumption and less throttling loss during the load regulation.

This paper proposes a compact boiler-turbine control scheme for the fossil-fuel power plants. Generally, control strategies used in boiler-turbine units are implemented as the hierarchical structures consisting of an upper economic optimization layer and a separate lower feedback control layer. The deficiency of such hierarchy is that the tracking of the steady-state set-points, that are computed by the upper layer, is accomplished by the lower layer in an economically non-optimal way. To improve the economic performance of boiler-turbine units, the proposed control scheme integrates plant economic optimization into the dynamic feedback controller output computation process. The stability (and also feasibility) of the proposed control scheme is proven. The effectiveness of the latter is also demonstrated through the 160 MW oil-fired boiler-turbine unit simulation.

The coordinated control system of a boiler–turbine unit plays an important role in maintaining the balance of energy supply and demand, optimizing operational efficiency, and reducing pollutant emissions of the coal-fired power generation unit. The existing challenges (the fast response to wide-scaled load changes, the matching requirements between a boiler and a turbine, and cooperative operation of a large number of distributed devices) make the design of the coordinated controller for the boiler–turbine unit be a tough task. In this paper, based on a typical coal-fired power unit model, using the linear-quadratic regulator (LQR), a coordinated control scheme with $H_{\infty }$ performance is proposed: the $H_{\infty }$ method is used to ensure control performance on the basis of reasonable scheduling of distributed equipment; the LQR is applied to limit the control actions to meet the actuator saturation constraints. Case studies for a practical 500 MW coal-fired boiler–turbine unit model indicate that the designed control system has satisfactory performance in a wide operation range and has a very good boiler–turbine coordination capacity.

It is challenging and interesting to establish a precise dynamic model of an OTB (once-through boiler) power plant unit in order to meet large scale load demands from the power grid. This study proposes to establish such a dynamic mathematical model of an ultra-supercritical OTB unit under dry operating conditions. More precisely, the dynamic model structure was derived from mass and energy conservation laws as well as thermodynamic principles under some reasonable simplifications and assumptions. Then an IGA (immune genetic algorithm) was improved to identify the parameters, combined with running data. After this, to further enhance model performance, the dynamic mathematical model was extended to be the one with different sets of parameters and functions under different monotonous load ranges. Additionally, open- and closed-loop experiments were conducted in order to further validate the developed model. The experimental results show that the model outputs can approach the actual running data over a wide operating range with appropriate accuracy. More importantly, the dynamic model captures the essential dynamic characteristics of the unit. Therefore, the model can be feasible and applicable for simulation analysis and testing control algorithms.

The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.

This chapter deals with the application of the MPC-based reference governor (MPC-RG) to a well-known boiler–turbine system, introduced in Astrom, Eklund, Int J Control 16(1):145–169, 1972, [1]. The aim here is to design such an MPC-based reference governor, which will increase the control performance of lower level closed-loops containing a set of PI controllers. This case study utilizes the theoretical background presented in Sect. 3.2.2 and in Sect. 4.3.

Recently, blackouts have occurred frequently due to various reasons. When the fault in the power grid is repaired, fast cut back (FCB) technology can restore power quickly. Therefore, it is necessary to establish a dynamic mathematical model of thermal power plant to simulate FCB. In this paper, a dynamic mathematical model of 300 MW thermal power plant was built up, which was based on the principles of mass and energy balance. The model considered feed water heater systems, bypass system, and feed water pumps. The heat absorption characteristics of steam-water systems during FCB were considered as well. This model has been verified by the FCB field tests under the conditions of 50% and 100% economize continue rate (ECR) load.

This paper presents an intelligent bio-inspired cooperative decoupling control strategy (IBICDC) for the problems of modeling difficulties and strong coupling in the marine boiler-turbine system (MBTS). First, the model of the main steam pressure control loop is successfully constructed by introducing the Martin-Hou equation, which solves the modeling difficulty caused by the complexity of structure, operation mechanism, and operation conditions, as well as the characteristics of nonlinearity, parameter time-varying, and time-delay in the marine boiler (MB). According to the mathematic method of homeomorphic mapping relationship between the rotational speed and the kinetic energy in the marine steam turbine with propeller (MSTP) and the feedback linearization method, the nonlinear degree of the MSTP rotational speed control loop model is reduced and the infinite point of discontinuity in the rotational acceleration when the rotational speed close to 0 is eliminated. Then, the IBICDC inspired by the internal environment regulation mechanism of human body is applied to the strong coupling problem between the two control loops, namely, to eliminate the large value sudden change of the main steam pressure caused by the change of operation conditions. The conventional decoupling methods are also presented. Finally, detailed numerical simulations are conducted to validate the effectiveness of the IBICDC strategy.

This paper presents an input prediction based active disturbance rejection control scheme for systems with time-delay. The new scheme is motivated by the principle of Smith-predictor and traditional active disturbance rejection control. In order to make the transitive relation between the virtual control input and actual output approach a pure inertial element, an input predictor is adopted. The approximate error forms part of total error as well as the internal uncertainties and external disturbances, which can be easily dealt by traditional active disturbance rejection controller. Numerical examples and simulations based on the fuel dynamics of a boiler turbine unit show the improvement of the new method.

For a typical boiler-turbine model considering both external disturbances and parameter uncertainties, a new robust nonlinear adaptive backstepping coordinated control strategy is proposed. To conquer the effect of parameter uncertainties and avoid the possibly parameter-drift, the sufficiently smooth projection operators are used in the parameter updating laws. At the same time, the nonlinear-damping is added to the control laws to effectively inhibit the impact of external disturbance on the control quality. Finally, several simulation tests prove that the proposed coordinated control system not only can ensure asymptotical stability of the closed-loop system, but also has good effects in disturbance rejection, given value tracing and robustness.

CCS (coordinated control strategy) is widely used for boiler-turbine units to change their steam turbine load. However, it is almost impossible to make a breakthrough in the load change performance just through CCS on account of the boiler's large delay. In order to accelerate the unit load response speed through the CSFA (cold source flow adjustment), the static and dynamic influence model of CSFA on the turbine power output is firstly set up in this paper. Then an improved strategy which combines CCS with CSFA control is brought forward to be used for the flexible load control. Another innovation of the improved strategy is to tie the output of CSFA controller to the measurement signal of the boiler controller, through which CSFA is used to quicken the turbine load response, the coal feeder rate to ensure the steady load accuracy, and the turbine governor valve to stabilize the main steam pressure. Furthermore, the condenser pressure will be recovered to its normal value for the next load dispatch use. Finally, simulation results confirm the effectiveness of the improved strategy compared to CCS, and moreover the extra coal consumption during the control process has been proved to be very small.

This paper presents a feed-forward and feedback control scheme for boiler-turbine coordination control system of 1000MW ultra-supercritical thermal power generating unit. Due to the multi-variable and nonlinear characteristics of control plant with strong couplings between main steam pressure control loop and power control loop, coordination control system is much more challenging to fulfill multiple tasks in practice. In terms of research range of this paper, a model of control plant with 2 inputs and 2 outputs is used to further optimize the performance of boiler-turbine coordination control system. Control tasks of unit load response and main steam pressure maintenance were taken into account to meet the demand in the rated condition. Simulation results show that the proposed boiler-turbine coordination control system could lead to a satisfactory performance on both control tasks.

The boiler-turbine process of the thermal power system is a typical multi-input-multi-output system. There is a strong coupling among the control loops and the process exhibits strong nonlinearities, conventional control strategies cannot achieve satisfactory control performance and meet production demands. The dynamic characteristics of the boiler-turbine systems are further researched in this paper and the globally linearizing input-output feedback linearization control technology is applied. The control method can not only reduce the effects of the nonlinearities on the system but also eliminate the coupling among control loops. Simulation results show that the globally input-output feedback linearization control technology has a better control quality and can improve the control performance compare of conventional feedback linearization method.

This paper proposes a fuzzy predictive control scheme for controlling power output of a boiler–turbine system in the presence of disturbances and uncertainties. A new model of the boiler–turbine system is introduced based on the modeling approaches of hybrid systems, namely, the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized using the piecewise affine approach. To overcome the deficiency of the model predictive control in presence of disturbance and uncertainty, a fuzzy predictive control scheme is proposed in which a fuzzy supervisor is utilized to adjust the main predictive controller. The proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in nominal conditions and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method.

In the present work, a boiler turbine industrial process with two manipulated variables, two controlled variables and some dead time has been considered as a case study. In order to eliminate loop interactions its decoupling is done to two separate SISO systems. Finally, an LQG controller has been designed for it and its performance is compared with that of conventional PID controllers based on Ziegler Nichol’s tuning and Internal Model Control (IMC) techniques.

This paper proposes a generalized active disturbance rejection controller (GADRC) based hierarchical control structure for the boilerturbine unit. In the lower layer, a multivariable extended state observer (MESO) is developed to estimate the values of the lumped disturbances caused by modelling mismatches, fuel quality variation and wide range load variation. The influence of the disturbances is then compensated at the input side as a feedforward control. In the upper layer, the multi-objective optimization is devised to obtain the set-points by removing the plant behaviour variation from the optimized model in a feasible way. The lowpass filter acting on the lumped disturbances is designed to bridge the gap between the lower and upper layer. The impact of the feedthrough item is approximated by a first-order system and a two degree-of-freedom (2-DOF) control strategy is established to illustrate the set-point tracking and disturbance rejection properties of the controller. Simulation studies on a 1000MWe coal-fired ultra-supercritical boiler-turbine unit demonstrate that the proposed control strategy can achieve a satisfactory performance in cases of fuel quality variations, model-plant mismatches and wide range load variation.

SUMMARY Building an appropriate mathematical model that describes the system behaviour with a certain degree of satisfaction is quite challenging owing to the uncertain and volatile nature of thermodynamic constants and geometric parameters. In this paper, we present a technique to approximate and validate the dynamic behaviour of the Astrom–Bell boiler-turbine power plant based on an RBFNN over a large operating range. The proposed RBFNN is applied to solve the parametric identification problem for nonlinear and complex systems using an optimiser based on a hybrid genetic algorithm. This optimiser is composed of the gradient descent optimiser and a genetic algorithm for fast convergence. Two simulations were performed to show the effectiveness of the proposed technique under different situations with several boiler-turbine input variables. The optimal structure and parameters of the obtained RBFNN-based model emulates well the dynamic behaviour of the Astrom–Bell boiler-turbine system. Copyright © 2013 John Wiley & Sons, Ltd.

As more stringent requirements are proposed for the load and frequency regulation in modern power grid, the performance of load tracking and main steam pressure stability becomes more important for the safe and economical operation of thermal power plants. In this paper, a new nonlinear control-oriented model of drum boiler-turbine system is developed with a balance between fidelity and simplicity, and validated with history operating data from a real 300 MW subcritical coal-fired unit over wide range. Then, feedback linearization is applied to the nonlinear model, resulting in two independent linear subsystems. Moreover, a sliding mode tracking controller is constructed for coordinated control based on the linearized model. Simulation results are presented to demonstrate the good tracking performance and strong robustness of the proposed approach.

Performance control of a boiler-turbine unit is of great importance due to demands on the economical operations of power plants and environmental awareness. In this paper, a robust decentralized control of nonlinear multi input-multi output (MIMO) model of a utility boiler-turbine unit is designed. Drum pressure, generator electric output and drum water level (as the output variables) are controlled by manipulation of valves position for fuel, feedwater and steam flows.

The predictive control system decides the discrete moves in manipulated variables on basis of the prediction of output of the process upto the prediction horizon. The predictive controllers show an excellent performance as compared to conventional controllers. In the present work, a boiler turbine process with two manipulated variables and two controlled variables has been considered and a predictive controller has been designed for it. The impact of variation in the value of sampling interval, prediction horizon and control horizon on the setpoint tracking performance and the disturbance rejection performance has been investigated.

In this paper we consider boiler-turb ine's nonlinear dynamics and linearize the nonlinear parts based on the piecewise affine method in order to obtain a mixed logical dynamical model of the system. By using piecewise affine approach for linearization of the system's nonlinear equations, the obtained linearized model switches between different modes based on its parameters, so acquired piecewise affine model can be categorized in the switc hing hybrid system class. We model the linearized boiler-turbine system in a mixed logical dynamical model of the hybrid systems using hybrid system's description language and hybrid toolbox. M ixed logical dynamical model describes system by two linear equ ations and one linear inequality with a reasonable accuracy and considering the constraints in the system. We provide a co mparison between the acquired mixed logical dynamical model using piecewise affine linearizat ion method and the actual boiler-turbine system through simulat ion and show the efficiency of the mixed logical dynamical model to describe the Boiler-Turbine system.

This paper describes the use of the linear H ∞ Theory to design a robust controller for the steam-turbine unit. This work considers issues like time variations, non-linearity, and dynamic coupling of a MIMO nonlinear model of this unit. After a detailed analysis of various operating points, a nominal one is chosen around which the linear model is obtained. Six other operating points are deliberated to test the robustness features of the controller. The proposed controller guarantees the internal stability and satisfies the requirements of both of frequency and time domains. A simulation is conducted in order to demonstrate how these requirements are satisfied when step signals and disturbances are applied simultaneously to the input channels.

A 160 MW drum-type boiler-turbine system is developed in the present research through a multi-multivariable dynamic matrix control (DMC) scheme and a multi-multivariable model approach. A novel intelligence-based decision mechanism (IBDM) is realized to support both model approach and control scheme. In such case, the responsibility of the proposed IBDM is to identify the best multivariable model of the system and the corresponding multivariable DMC scheme to cope with the system at each instant of time in an appropriate manner.

This paper suggests a design method of the model-following H∞ control system having robust performance. This H∞ control system is designed by applying genetic algorithm(GA) with reference modelto the optimal determination of weighting functions and design parameter γ that are given by Glover-Doylealgorithm which can design H∞ controller in the state space. These weighting functions and design parameter γ are optimized simultaneously in the search domain guaranteeing the robust performance of closed-loop system. The effectiveness of this H∞ control system is verified by applying to the boiler-turbine control system. Key words: Model-following H∞ control system, Robust performance, Genetic algorithm (GA) †교신저자(포항대학교 전기소방계열, E-mail:hwang@pohang.ar.kr, Tel: 054-245-1098) 1. 서 론 1980년대 초반 G. Zames[1]에 의해 개발된 H ∞ 제어이론은 가중치 함수(weighting function)와 설계 파라미터 γ를 적절히 선정함으로써 주어진 설계사양을 만족하고 비구조적 불확실성(unstructured uncertainty)[2]에 대한 강인한 안정성(robust stability)[2]을 가지는 제어 시스템을 설계하는 것이다. 이러한 H

The characteristic of supercritical unit is not easily controlled, because the research on the coordinated control system of ultra-supercritical coal-fired (USC) power unit is usually based on a kind of mathematical model, which is used to test the correctness of the design concept and the effectiveness of the control strategy. If the mathematical model cannot reflect the intrinsic nature of the controlled object, the simulation results are difficult to explain that the control strategy designed is successful. In this paper, a simplified non-linear model which is suitable for the controller design in coordinated control system of a once-through boiler-turbine unit is analyzed at first. Then based on the study of the above model, features and improvement suggestions of the non-linear model are proposed. At last, step forced simulation test and comparison test with operation data were done to test the effectiveness and accuracies of the simplified non-linear once-through boiler model. Simulation results show that this model has same dynamic characteristics with the actual unit and can predict output changes with certain accuracy which means that it is fit for the controller design of coordinated control system of ultra-supercritical coal-fired (USC) power unit. This may serve as a reference for engineering analysis and simulator development of ultra-supercritical power units.

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.

In this paper, a discrete-time piecewise affine (PWA) model has been proposed for a nonlinear model of boiler–turbine unit using plant operating points. PWA model is one of the main classes of hybrid systems being equivalent to some other hybrid modeling frameworks such as mixed logical dynamical (MLD) model. In order to control the system, a model predictive control (MPC) strategy in explicit form has been used which calculates the control law as an affine function of system states. In this method, the computation of MPC is moved off-line. The off-line control law is easier to implement reducing to a look-up table in comparison with the on-line approach. Finally, the explicit model predictive control performance has been compared with the linear controller obtained using H∞ approach. The results are illustrated by simulations. They show that the explicit MPC method has suitably improved the system performance, especially the quantity of control efforts is smaller and without saturation compared with that of H∞ control system.

This paper proposes an adaptive Dynamic Matrix Control (DMC) and its application to boiler-turbine system In a conventional DMC, object system is described as a Step Response Model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are prepared. On-line interpolation is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory results with a wide range operation of the boiler-turbine system.

This paper proposes an adaptive Dynamic Matrix Control (DMC) and its application to boilerturbine system. In a conventional DMC, object system is described as a Step Response Model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are prepared. On-line interpolation is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory results with a wide range operation of the boiler-turbine system.

Boiler-turbine units are multivariable nonlinear systems. The control of such systems is not easy considering the practical tuning, implementing and maintaining problems. In this paper, the design of a linear controller for the Dalate No.4 unit is reported. Based on a nonlinear model of the unit, we analyze the nonlinearity of the unit and propose to choose the appropriate operating points so that a linear controller can achieve wide-range performance. Simulation results and field tests show that the designed controller works well for the specific range of load variations.

Objectives: Design and implementation of PI/PID controllers with parameter optimization using different techniques for MIMO systems. Methods: Biogeography Based Optimization based Proportional Integral Derivative (BBOPID) and investigating the robustness of the BBOPID technique applied to boiler turbine unit. Design of a Coordinated PID (CPID) Controller and tuning the CPID parameters using Particle Swarm Optimization (PSO), Bacteria Foraging (BF) and BBO techniques are discussed. Findings: The design of controller is done by using the performance index of Integral Square Error (ISE). Results show that BBO controller gives better performance for both servo and regulatory responses. Application: The electric power is generated using steam driven turbo generators, where the steam is produced by drum boilers as as practices base, reuses and stores the best practices.

Boiler–turbine units constitute a critical component of a co-generation system. During operation, especially the start-up operation, these units are subject to high-temperature variations that aggravate the stress of the material used in their construction and thus a negative effect in their life spans. This paper is about designing a life extending controller (LEC) to obtain a good tradeoff between the life of a boiler–turbine system and control system performance. Because the boiler system is multivariate and there exist some constraints on the inputs to plant, model predictive control theory is used. For practical consideration, the original controller of the boiler system designed for dynamic performance is taken as a pre-compensator. For ease in LEC design, the pre-compensated closed-loop system is reduced in order using the system identification method. Finally, the resulting system is extensively simulated and tested on a computer, using a sophisticated nonlinear model of the boiler system.

A simple two-by-two model for a boiler-turbine unit is demonstrated in this paper. The model can capture the essential dynamics of a unit. The design of a coordinated controller is discussed based on this model. A PID control structure is derived, and a tuning procedure is proposed. The examples show that the method is easy to apply and can achieve acceptable performance.

This paper presents a gain-scheduled approach for boiler-turbine controller design. The objective of this controller design is to achieve tracking performance in the power output and drum pressure while regulating water level deviation. Also, the controller needs to take into account the magnitude and rate saturation constraints on actuators. The nonlinear boiler-turbine dynamics is brought into a linear parameter varying (LPV) form which is a parameter-dependent state-space realization. The LPV form of the boiler-turbine dynamics is characterized by nonlinear dependence on drum pressure, which is naturally the scheduling variable. The controller is designed by utilizing the set-valued method for l1- optimization, which explicitly addresses state constraints and controller saturations in the design process. The overall gain-scheduled design is augmented by a reference governor to avoid performance degradation in the presence of large tracking commands.