Neural Network-Based Modeling for A Large-Scale Power Plant

Transcription

1 1 Neural Network-Based Modeling for A Large-Scale Power Plant Kwang Y. Lee, Fellow, IEEE, Jin S. Heo, Jason A. Hoffman, Sung-Ho Kim, and Won-Hee Jung Abstract-- A large-scale power plant, specifically, a 500MW, once-through type, super-critical boiler plant, requires investigation for the development of a control system. Using data from the power plant, a model can be realized using intelligent techniques. In this paper, a Neural Network-based Model (NNM) is presented as an alternative methodology to expand the modeling techniques for developing a new power plant. The developed Neural Network-based Combined Model (NNCM) consists of many processes which include air/flue gas, pulverizer, water/steam, and turbine/generator systems. The major inputs/outputs of the processes will be mass flow rate, temperature, pressure, and enthalpy of fluid. Moreover, control variables are utilized for driving the plant to desired states. For validation of the proposed model, a comparison of Rankine cycles between actual data and the output of the NNCM will be shown. The results of the NNCM will also be compared to actual plant data for major outputs. Index Terms-- Once-through type boiler, super-critical boiler, neural networks, modeling, power plant control, distributed large-scale power plant. I I. INTRODUCTION N recent years, the development of large capacity power plants requires new approaches to analyze plant dynamics for control purposes. In practice, many utility companies utilize simulation programs, such as Modular Modeling Systems (MMS) [1] or their own simulation tools for modeling. However, it is a challenge to design a new model without specification of components, and to extend current models to model larger capacity plants. In order to design a control system for a power plant, it is necessary to develop a model in advance. Recently, the study of Artificial Neural Networks (ANN) has become an important aspect in designing system identification and control systems. With power plant data, the ANN can be trained to implement a nonlinear approximator to provide an analyzable model for developing a control system. Since the ANN strongly depends on the input/output data but not on the structure of the system, it is flexible and can easily be adapted to different types of power plants. K. Y. Lee, J. S. Heo, and J. A. Hoffman are with the Department of Electrical Engineering, The Pennsylvania State University, University Park, PA ( kwanglee@psu.edu, juh138@psu.edu, and jah1051@psu.edu). S.-H. Kim and W.-H. Jung are with Corporate R&D Institute, Doosan Heavy Industries and Construction Company, Ltd. ( and There have been many applications of NN in power systems [2]-[11]. The ANN can be classified according to the structure of the network. A feedforward neural network is often utilized to approximate a steady-state model [2],[9]. A recurrent neural network is well suited for dynamic input/output mappings since the recurrent neurons represent state variables [2],[4],[5]. In order to preserve stability during the search for optimal control inputs, the ANNs have been used as identifiers for small-scale power plants [6]-[9]. The ANN will be implemented using recurrent neural networks or feedforward neural networks depending on the characteristics of the data patterns. For control purposes in a large-scale power plant, ANNs can be applied to realize each subsystem in the power plant [11]. Moreover, ANNs can be applied to develop a nominal model which can be used in fault diagnosis [9]. Although there are many applications of NN, they have primarily been applied to small-scale plants and the number of inputs and outputs has been limited. The small-scale power plant models are expressed by a low order nonlinear Multiple- Input and Multiple-Output (MIMO) systems, which represent major inputs and outputs of the plants. Thus, the small-scale power plant models cannot provide detailed characteristics and relationships between the subsystems in the real power plant. Moreover, few attempts have been made to model a system using a hierarchical structure where an output of an ANN model becomes an input to a different ANN model. For validation of a single ANN model, the responses of inputs and outputs for the overall power plant have been investigated in many publications [14]-[15]. However, the combined model, which consists of many ANN models, has not been introduced, and an appropriate validation method is necessary for the new approach. A large-scale power plant, specifically, a 500MW, oncethrough type, super-critical boiler plant, requires investigation for the development of a control system. Using data from the power plant, a model can be realized using intelligent techniques. In this paper, a Neural Network-based Model (NNM) is presented as an alternative methodology to expand the modeling techniques for developing a new power plant. The developed Neural Network-based Combined Model (NNCM) consists of many processes which include air/flue gas, pulverizer, water/steam, and turbine/generator systems. The major inputs/outputs of the processes will be mass flow rate, temperature, pressure, and enthalpy of fluid. Moreover, /07/$ IEEE.

2 2 control variables are utilized for driving the plant to desired states. For validation of the proposed model, a comparison of Rankine cycles between actual data and the output of the NNCM will be shown. The results of the NNCM will also be compared to actual plant data for major outputs. Following the introduction, the 500 MW once-through type super-critical boiler power plant is described in Section II. Section III describes the design of the Neural Network-based Combined Model (NNCM) for a large-scale power plant. Section IV shows simulation results to demonstrate the feasibility of the proposed approach. The final section draws some conclusions and presents future works. II. ONCE-TROUGH TYPE BOILER POWER PLANT A. Development of Power Plant Model A power plant is a large-scale system and is governed by the Unit Load Demand (ULD) signal from the central dispatch center. It may also be controlled by the Automatic Generation Control (AGC) signal when the unit is participating in loadfrequency control. A power plant is a MIMO system. The dimension of the overall system is very high even if a simple model is used for each subsystem. The dynamics of each subsystem are represented by a set of nonlinear differential equations, and are coupled with nonlinear algebraic equations for interconnection to other subsystems. Operating conditions vary continuously with time, and faults may occur anywhere within the plant or in the network of sensors/actuators at any time. The majority of current Super-Critical (SC) boiler power plants are once-through high pressure steam power plants. Depending on the generation demand, throttle temperature, and net efficiency, the throttle pressure ranges from 16.6 MPa to 31 MPa [12]. The fuel is Pulverized Coal (PC). In this paper, the target SC boiler power plant consists of four processes which are air/flue gas, pulverizer, water/steam, and turbine/generator. However, for modeling purposes, the number of detailed subsystems will be twenty. Fig. 1 shows a 500 MW SC boiler power plant. Most blocks are subsystems, which will be represented by a NN-based subsystem model. The proposed scheme is generic and will be applicable to other types of plants, including plants with ultra-supercritical boilers, or nuclear and fuel cell plants. B. Description of Power Plant The power plant under investigation is a 500 MW, coalpulverized, once-through type, boiler-turbine-generator unit. The once-through type boiler is capable of delivering steam at a pressure of 35 MPa and a temperature of 595 C. Two forced draft fans supply air to burner and furnace, two primary fans provide air to the pulverizers, and two induced draft fans are controlled to maintain the furnace pressure at a desired value. Two split economizers are arranged before and after a Selective Catalytic Reduction (SCR) to improve denitrification and net efficiency. The superheater consists of three parts, division, platen, and finish. The reheater reheats the steam after a High Pressure (HP) turbine using primary reheater and reheater finish. There is a separator on top of the furnace which supplies high pressure steam to the superheater division. The waterwall is around furnace vertically and spirally. Flue gas is supplied to the furnace through the pulverizers and burners. Finally, the turbine generates power from the tandem compound triple turbines, which consist of three parts: a HP turbine, an Intermediate Pressure (IP) turbine, and Low Pressure (LP) turbine. The model will be focused on boiler, turbine, and generator parts. Each subsystem inside the furnace has common inputs and outputs: mass flow rate, temperature, pressure, and enthalpy of fluid. In addition to these inputs, there are control variables which are used to drive each subsystem to the Fig. 1. A 500 MW once-through type boiler power plant.

3 3 desired state. The proposed model, which is based on the ANN, will use the predefined control action as feedforward control. With the proposed approach, the utility company is able to investigate the dynamic characteristics of power plants with different capacities. III. DESIGN OF NEURAL NETWORK-BASED MODEL FOR A LARGE-SCALE POWER PLANT A. Structures and Training of Neural Networks In many engineering applications, ANN has been used as the best nonlinear approximator [2]-[4]. Most problems use the feedforward neural network (FNN), combined with tapped delays, and the backpropagation training algorithm to solve dynamic problems. However, FNN is a static mapping, and without tapped delays it cannot represent a dynamic response in the time domain. On the other hand, Recurrent Neural Networks (RNN) can naturally represent dynamic systems. In other words, RNN can capture the dynamic behavior of a system. It needs fewer weights and neurons than the FNN, and may need fewer training iterations. However, the Fullyconnected Recurrent Neural Network (FRNN), where all neurons are coupled to one another, is difficult to train and to converge in a short time [2]. In this research, the Diagonal Recurrent Neural Network (DRNN) will be applied to the large-scale power plant. Since it is not fully connected, the number of weights of the DRNN is small, therefore it can be used for real-time applications [4],[5]. Fig. 2 shows an ii ( k) I w ij sj ( k) O w j overview of the DRNN. In Fig. 2: i i (k) is the i th I input at time step k, w is the ij weight from the i th input to the j th o sigmoid neuron, w is the jl weight from the j th sigmoid neuron to the l th linear output neuron, s j (k) is the j th sigmoid neuron, o l (k) is the l th linear D output neuron, and D is the delay with weight w, through j which the output of the sigmoid neuron is fed back to itself. There are a few training methods for the ANN, which are Gradient Descent Training (GDT), Conjugate Gradient Training (CGT), and Levenberg-Marquardt Training (LMT) [13]. Depending on the method, the performance and training D ok ( ) x j ( k) Fig. 2. Structure of the Diagonal Recurrent Neural Network. = Linear neuron Delay D D w j sigmoid neuron time are different. The GDT is a standard backpropagation algorithm, which utilizes the steepest descent approach. The CGT is the Polak-Ribiere method that was developed to keep the descent directions properly aligned to speed up the convergence of the steepest descent approach. The CGT method provides a faster convergence rate and smaller errors than GDT. The main algorithm of LMT is a parameter updating method which updates step size for decent direction moves toward the gradient. After investigation of GDT, CGT, and LMT, the LMT is found to have the best performance for the DRNN. Thus, in this research, the DRNN and LMT are applied to the Neural-Network-based Model for a large-scale power plant to obtain the fastest and the most accurate results. B. Subsystem Modeling for SC Boiler Power Plants The large-scale power plant consists of many subsystems as shown in Fig. 1. The total number of subsystems is twenty. For control purposes, the overall system can be divided into four process models, which are the water and steam model, air and flue gas model, pulverizer model, and turbine and generator model. Each process model is constructed with a TABLE I COMPOSITION OF FOUR PROCESS MODELS AND TWENTY SUBSYSTEMS Water & steam model Feedwater Economizer1 Economizer2 Waterwall Separator Superheater platen Superheater finish Superheater division Primary reheater Reheater finish Air & flue gas model Primary Air Forced draft fan Induced draft fan Air preheater Furnace flue gas Gas recirculation Pulverizer model Pulverizer Burner Turbine & Generator model Intermediate pressure turbine High pressure turbine multiple number of subsystems. Table I shows compositions of four process models and twenty subsystems. 1) Water & Steam Model: The subsystems of the water and steam model are located inside the boiler, where they can be supplied with heat by gas recirculation. The distribution of steam and water in the waterwall depends on the heat supplied to the surface of the waterwall. In a once-through boiler, the outflow from the waterwall passes through a separator to separate the steam from the water to prevent the water from entering the superheater subsystem. There are eight heat exchangers, which are economizer1, economizer2, waterwall, superheater division, superheater platen, superheater finish, primary reheater, and reheater finish. In this research, each heat exchanger is modeled with inputs and outputs of enthalpy (H), temperature (T), pressure (P), and mass flow rate (M & ). The superheater and reheater subsystems have additional control inputs, which operate the sprays to control fluid

4 4 temperature. Fig. 3 shows the structure of a generic model for heat exchangers. The feedwater system has water/control inputs and water output to the sprays and economizer1. The ignite and increase the temperature of the flue gas and steam. Fig. 5 shows the pulverizer and burner subsystems.. T hot air, P hot air, M hot air. T coal T hot air, P hot air, M hot air P. coal Pulverizer Mcoal Burner.. Tfuel, Pfuel, Mfuel Tcold air, Pcold air, Mcold air Control inputs ( hot and cold air dampers, pulversizer) Fig. 5. Structure of the pulverizer and burner models. Fig. 3. Structure of the model for heat exchangers. control inputs operate the boiler feedpump to provide water to the boiler. 2) Air & Flue Gas Model: The main roles of air and flue gas model are to supply air and extract the flue gas to maintain the furnace pressure at a desired value. The induced draft fans extract hot flue gas from the furnace through the air preheater, which is also a heat exchanger, to the stack. Moreover, hot air is supplied to the furnace and pulverizers by the operation of primary air, forced draft fan, and air preheater subsystems. From the burner, the hot flue gas is recirculated through the heat exchangers inside of the furnace. In this research, each air and flue gas subsystem is modeled with temperature (T), pressure (P), and mass flow rate (M & ). The primary air subsystem has control inputs for the fan inlet vane. Forced draft fan and induced draft fan subsystems have control inputs for the fan blade pitch to pass the air and flue gas. Fig. 4 shows the structure of the model for air and flue gas subsystems. The air subsystem is a clustered system Fig.4. Structure of the model for air and flue gas subsystems. which includes primary air, forced draft fans, induced draft fans, and air preheater. 3) Pulverizer Model: There are six pulverizers and burners in the power plant. Coal entering the pulverizers is controlled by the coal feeder demand, while the hot and cold air dampers control the air-fuel ratio in the pulverizers. The pulverized coal and hot air move to the furnace through the burner to 4) Turbine and Generator Model: The turbine and generator transform thermal energy to electrical energy. The turbine consists of a High Pressure (HP) turbine, Intermediate Pressure (IP) turbine, and a Low Pressure (LP) turbine. In this research, the models of IP and LP turbines are combined. The high pressure turbine has a control valve, called the governor valve. The output steam from the HP turbine goes to the Fig. 6. Structure of the turbine and generator model. reheaters, before entering the IP-LP turbine. Fig. 6 shows the turbine and generator model. C. Neural Network-based Subsystem Model In order to obtain training data for each subsystem, a wide range of operational data for the SC power plant is needed. It is also necessary to define an appropriate sampling time for the training data. In this research, the data for a wide range of operation are extracted by changing the power set-point (demand). The power set-point comes from the dynamic unit load demand, which in this report varies between 100% and 50% of the Maximum Generation Rate (MGR), in an increment of 5%. Since the data of the SC power plant are generated every 1 second, the amount of data is considerably large. Moreover, the amount of input/output data for each subsystem is also large. Thus, the data of the SC power plant are sampled every 30 seconds. to train the ANNs. The data is extracted from the plant simulator to provide the input/output data for each subsystem. Because the data from the plant simulator is noisy, the input/output data were filtered so that the NNs would not learn the noise patterns. The inputs/outputs are pre-scaled up/down to [-1, 1] for the tangent-sigmoid threshold function of the neurons. The ANN consists of many neurons, which utilize a sigmoid function in each hidden layer. There are simulation parameters, which are the number of neurons in the hidden layer of the ANN, and the number of epochs to train the ANN. Since the proposed DRNN has only one hidden layer, the performance is

5 5 dependent on the number of neurons in the hidden layer and epochs. After experimenting with different numbers of neurons and epochs, the optimal number of neurons is found for each subsystem, and the number of epochs is fixed for all subsystems. The optimal number of hidden neurons depends on the number of inputs and outputs of each subsystem as well as the input/output data pattern. To select the optimal number of neurons in the hidden layer, the cost function, which is the mean square error for each epoch, is utilized. Table II shows the number of inputs, outputs, and optimal neurons for each subsystem. After training the NN-based subsystems, the outputs of the NN-based subsystems need to be post-scaled with min/max values used for prescale to obtain the actual outputs. Fig. 7 shows the major outputs of trained NN-based subsystems compared with filtered data from the plant simulator: the power output in the turbine/generator, the pressure output in the superheater finish, the temperature output in the superheater finish, and the temperature output in the reheater finish. The errors between outputs of NN-based subsystems and original data are small. (a) Power output in the turbine/generator TABLE II NUMBER OF INPUT, OUTPUT, AND OPTIMAL NEURONS FOR EACH SUBSYSTEM Subsystems Number Number of input of output Feedwater Economizer Economizer Furnace/Waterwall Separator Superheater platen Superheater finish Superheater division Primary reheater Reheater finish Gas recirculation Pulverizer Burner Intermediate pressure turbine High pressure turbine Air systems (Primary Air, Forced draft fan, Induced draft fan, Air preheater) Number of neuron (b) Pressure output in the superheater finish (c) Temperature output in the superheater finish D. Neural Network-based Combined Power Plant Model Once the NN-based subsystems are successfully trained, they can be combined to develop a NN-based combined model (NNCM). To reduce the complexity and provide better information, the primary air, forced draft fan, induced draft fan, and air preheater are combined into a single subsystem, called air systems. The waterwall and furnace flue gas are also clustered into the furnace/waterwall subsystem. The resulting sixteen subsystems will be connected with corresponding subsystem inputs and outputs; in addition, there are several external inputs for air, water, coal, oil, and control actions. Fig. 8 shows an overview of the NN-based Combined Model (NNCM). The NNCM can be constructed by using generated (d) Temperature output in the reheater finish Fig. 7. Major outputs of trained NN-based subsystems.

6 6 Fig. 8. NN-based Combined Model. outputs of subsystems as inputs to other subsystems, as shown in Fig. 1. The coal pulverizing process uses hot and cold air from the air systems to provide the pulverized coal to the burners. The hot air is provided by the air preheater using the hot flue gas to heat the cold air. The burners are provided with additional hot air from the air preheater to generate the fuel used in the furnace to produce the hot flue gas. The gas recirculation subsystem provides the hot flue gas to all heat exchangers inside the furnace before the flue gas exits to the stack through the air preheater. In the water and steam process, the feedwater subsystem is supplied with water from the boiler feedpump. This water is passed on to economizer1 and the superheater control sprays. The water continues from economizer1, through economizer2 and into the waterwall before it is provided to the separator. In the separator the water is separated from the steam, and the high pressure and temperature steam continues to the superheaters. After the superheaters, the steam enters the high pressure turbine where thermal energy is released and power is produced. The reduced energy steam next recovers some of its thermal energy from the two reheaters before it is provided to the intermediate and low pressure turbine, where most of the remaining thermal energy is used to generate power. IV. SIMULATION RESULTS In the following simulation, the results of the NNCM for a large-scale power plant will be shown. The inputs to the NNCM are the unit load demand and control actions from the plant simulator. The primary output of the NNCM is power, but major intermediate processes are also compared to the plant simulator data for validation. A. Rankine Cycle For validation of the NNCM, simulation was performed to obtain the Rankine cycle for different unit load demand levels, which are 100%, 75%, and 50% MGR. The Rankine cycle implies thermodynamics, which can provide information on efficiency. There are many stages in the Rankine cycle corresponding to power plant subsystems. The Rankine cycle will be a good method to verify the model for large-scale power plants. Fig. 9 shows the Rankine cycles for 100% MGR, 75% MGR, and 50% MGR. By checking the error between data provided from the plant simulator and the proposed NNCM, the model can be approved for a 500 MW once-through type boiler power plant. B. Individual Subsystems Output As a secondary validation of the NNCM, major outputs from various subsystems are compared with filtered data from the plant simulator. Fig. 10 shows the major outputs of NNCM: the power in the turbine/generator, the steam pressure in the superheater finish, the steam temperature in the superheater finish, and the steam temperature in the reheater finish. By observing the error between the filtered data provided by the plant simulator and the outputs of the proposed NNCM, the modeling is successful for the 500 MW once-through type boiler power plant. V. CONCLUSION A new concept of NN-based Modeling (NNM) is presented for a large-scale thermal power plant. In order to realize the NN-based Combined Model (NNCM), the individual subsystem models are implemented with NNs. The NN-based subsystem models are developed using data from a plant

7 7 (a) 100% (a) Power in the turbine/generator (b) 75% (b) Steam pressure in the superheater finish (c) 50% Fig. 9. Rankine cycles for 100%, 75%, and 50% MGR. (c) Steam temperature in the superheater finish simulator. For validation of the proposed model, the major outputs of subsystem models and the Rankine cycle for different demands are compared with data from the plant simulator. The overall simulation results show the proposed methodologies can be applied well for analyzing the dynamic characteristics of large-scale power plants. For future work, with the developed NNCM, an efficient conventional control algorithm will be developed to control the NNCM. Moreover, applicability to larger capacity power plants will be investigated based on the proposed methodology. (d) Steam temperature in the reheater finish Fig. 10. Major outputs of NN-based combined model.

8 8 VI. ACKNOWLEDGEMENT This research work has been performed under a project awarded by Doosan Heavy Industries & Construction Company, Ltd., Seoul, Korea. The authors are pleased to express their appreciation to Dr. Chang-Ho Cho of the Corporate R&D Institute of Doosan for his support of the project. VII. REFERENCES [1] G. Leavesley, The Modular Modeling System (MMS) - A Modeling Framework for Multidisciplinary Research and Operational Applications, [2] K. Y. Lee, (M. EL-Sharkawi and D. Niebur, Editors), Chapter 12, Control of Power Systems, Tutorial on Artificial Neural Networks with Applications to Power Systems, IEEE Power Engineering Society, Publication #96TP112-0, pp ,1996. [3] K. S. Fu, Learning Control Systems and Intelligent Control Systems: an Intersection of Artificial Intelligence and Automatic Control, IEEE Transactions on Automatic Control, vol. 16, pp , [4] C. C. Ku and K. Y. Lee, Diagonal Recurrent Neural Networks for Dynamic System Control, IEEE Trans. On Neural Networks, vol. 6, no. 1, pp , Jan [5] C. C. Ku, K. Y. Lee, and R. M. Edwards, Improved Nuclear Reactor Temperature Control using Diagonal Recurrent Neural Networks, IEEE Trans. on Nuclear Science, vol. 39, no. 6, pp , Dec [6] H. Ghezelayagh and K. Y. Lee, Intelligent Predictive Control of A Power Plant with Evolutionary Programming Optimizer and Neuro-Fuzzy Identifier, Proc Congress on Evolutionary Computation, vol. 2, pp [7] H. Ghezelayagh and K.Y. Lee, Neuro-Fuzzy Identifier of a Boiler System, Engineering Intelligent Systems, vol. 4, pp , [8] H. Ghezelayagh and K. Y. Lee, Application of Self-Organized Neuro- Fuzzy Identifier in Intelligent Predictive Control of a Power Plant, Engineering Intelligent Systems, vol. 13, no. 2, pp , [9] J. S. Heo and K. Y. Lee "A multi-agent system-based intelligent identification system for control and fault-diagnosis for a large-scale power plant," Proc. IEEE Power Engineering Society General Meeting, Montréal, Québec Canada, [10] J. S. R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. on Systems, Man and Cybernetics, vol. 23, no. 3, pp , [11] J. S. Heo and K. Y. Lee, Multi-agent system-based intelligent steadystate model for a power plant, Proc. the 13th International Conference on Intelligent Systems Application to Power Systems (ISAP05), Washington, D.C., [12] S. Kaneko, M. Hisatome, and M. Hishida, High efficiency supercritical sliding pressure units for oil/gas firing, Proc. of International Conference on Energy Management and Power Delivery, vol. 1, pp , 1995, Singapore. [13] P. J. Antsaklis and K. M. Passino, eds., An introduction to intelligent and autonomous control, Kluwer Academic, MA., [14] H. Branover, A. El-Boher, E. Greenspan, and A. Barak,. Promising applications of the liquid metal MHD energy conversion technology, Proc. the 24th Intersociety Conference of Energy Conversion Engineering, vol. 2, pp , Washington, D.C.. [15] T. Inoue, H. Taniguchi, and Y. Ikeguchi, A model of fossil fueled plant with once-through boiler for power system frequency simulation studies, IEEE Trans. on Power Systems, vol. 15, no. 4, pp BIOGRAPHIES Kwang Y. Lee received his B.S. degree in Electrical Engineering from Seoul National University, Korea, in 1964, M.S. degree in Electrical Engineering from North Dakota State University, Fargo, in 1968, and Ph.D. degree in System Science from Michigan State University, East Lansing, in He has been with Michigan State, Oregon State, Univ. of Houston, and the Pennsylvania State University, where he is a Professor of Electrical Engineering and Director of Power Systems Control Laboratory. His interests include power system control, operation, planning, and intelligent system applications to power systems. Dr. Lee is a Fellow of IEEE, Associate Editor of IEEE Transactions on Neural Networks, and Editor of IEEE Transactions on Energy Conversion. He is also a registered Professional Engineer. Jin S. Heo received his B.S. and M.S. degrees in Electronics Engineering from Inje University, Korea, in 1999 and 2001, respectively. As a candidate, he is currently pursuing the Ph.D. degree in Electrical Engineering at the Pennsylvania State University. His interests are multiobjective optimization in control systems, intelligent distributed control, multiagents systems, modeling and control of fuel cell power plants, and real-time embedded system. Jason A. Hoffman received his B.S. degree from Bucknell University in He is currently pursuing his M.S. degree in electrical engineering from the Pennsylvania State University. His interests are modeling and control of power plants, neural networks, intelligent control systems, and alternative energy systems. Sung-Ho Kim received his B.S. degree in Mechanical Engineering from Yeungnam University, Korea, in 1991, and M.S. degree in Mechanical Engineering from Kyungpook National University, Korea, in He has been with Doosan Heavy Industries & Construction, Co., Ltd., Korea, where he is a Principal Engineer of Plant Control System Team in the Corporate R&D Institute. His interests are control, operation and modeling of fossil power plant. Won-Hee Jung received his B.S. and M.S. degrees in Mechanical Engineering from Pukyung National University, Korea, in 1995 and 1997, respectively. He has been with Doosan Heavy Industries & Construction, Co., Ltd., Korea, where he is a Senior Engineer of Plant Control System Team in the Corporate R&D Institute. His interests are control algorithm, optimization, simulation technique, artificial intelligence for fossil power plant