“…Thereby, proving the usefulness of control theory in improving the energy efficiency for supercritical generation units and contributing in satisfying the future climate targets without need for the complexity of and in-depth analysis as commonly made in the other models and controllers. 6,13,37 The control system basic idea is realized in Figure 9.Figure 9.The proposed control strategy.…”
Section: The Proposed Control Systemmentioning
confidence: 99%
“…The deep belief neural networks (DBN) have brought considerable improvements to prediction and control for a 1000 MW ultrasupercritical unit. 36 Znad et al 37 have implemented model predictive control to an identified linear model of 600 MW SC unit in order to speed-up the startup process, however, the study doesn’t involve reduction of emissions and has been has been conducted with linear models, which doesn’t guarantee adequate performance for the full range of operation. Haddadin et al 12 have compared Elman Neural Network (ENN) with Generalized Regression Neural Network (GRNN) for modeling-based performance prediction of a 600 MW SC plant, without targeting control strategy development.…”
Coal power plants have been a major source of undesirable emissions. Despite the technological advancements in renewable energies, coal units are still in-service in many developed and developing countries due to their reliability, adequacy, and flexibility for power delivery. There are some promising technologies for cleaner operation during power production from coal, including supercritical boiler (SC) design and carbon capture and storage (CCS), however, the challenging in innovating effective methods is still open to expand the boundary of knowledge in this speciality. This paper introduces a novel and simple method for reducing CO2 emissions and improving the dynamic responses of a 600 MW SC coal power plant by Artificial Neural Network (ANN) technique. A wide-range data-driven feedforward ANN model has been identified and verified for the various operations recorded as closed-loop data-sets, which covers all situations of startup, once-through mode, and even emergency shutdown of the unit. The closed-loop SC plant model has been augmented with an inverse multivariable coordinate NN controller, developed by analogous learning algorithm to improve the plant automation. With precisely selected setpoints, as operational rules, of temperature, pressure, and earliest possible power demand signals, the automated SC plant has been capable to operate with lower coal consumption - and thus lower emissions – than the existing operation strategy during startup, normal operation, and emergency shutdown modes. The improvement in dynamic responses have been quantified through simulations with comparison with existing performance, which have resulted in an overall average reduction of 2.143 Kg/s in coal consumption.
“…Thereby, proving the usefulness of control theory in improving the energy efficiency for supercritical generation units and contributing in satisfying the future climate targets without need for the complexity of and in-depth analysis as commonly made in the other models and controllers. 6,13,37 The control system basic idea is realized in Figure 9.Figure 9.The proposed control strategy.…”
Section: The Proposed Control Systemmentioning
confidence: 99%
“…The deep belief neural networks (DBN) have brought considerable improvements to prediction and control for a 1000 MW ultrasupercritical unit. 36 Znad et al 37 have implemented model predictive control to an identified linear model of 600 MW SC unit in order to speed-up the startup process, however, the study doesn’t involve reduction of emissions and has been has been conducted with linear models, which doesn’t guarantee adequate performance for the full range of operation. Haddadin et al 12 have compared Elman Neural Network (ENN) with Generalized Regression Neural Network (GRNN) for modeling-based performance prediction of a 600 MW SC plant, without targeting control strategy development.…”
Coal power plants have been a major source of undesirable emissions. Despite the technological advancements in renewable energies, coal units are still in-service in many developed and developing countries due to their reliability, adequacy, and flexibility for power delivery. There are some promising technologies for cleaner operation during power production from coal, including supercritical boiler (SC) design and carbon capture and storage (CCS), however, the challenging in innovating effective methods is still open to expand the boundary of knowledge in this speciality. This paper introduces a novel and simple method for reducing CO2 emissions and improving the dynamic responses of a 600 MW SC coal power plant by Artificial Neural Network (ANN) technique. A wide-range data-driven feedforward ANN model has been identified and verified for the various operations recorded as closed-loop data-sets, which covers all situations of startup, once-through mode, and even emergency shutdown of the unit. The closed-loop SC plant model has been augmented with an inverse multivariable coordinate NN controller, developed by analogous learning algorithm to improve the plant automation. With precisely selected setpoints, as operational rules, of temperature, pressure, and earliest possible power demand signals, the automated SC plant has been capable to operate with lower coal consumption - and thus lower emissions – than the existing operation strategy during startup, normal operation, and emergency shutdown modes. The improvement in dynamic responses have been quantified through simulations with comparison with existing performance, which have resulted in an overall average reduction of 2.143 Kg/s in coal consumption.
“…In order to speed up the starting process of an SCPP, Abu Znad et al (2022) [26] developed an improved control approach based on classical MPC. The plant's start-up process was modeled using a state-space model MISO structure.…”
Sliding pressure control is a well-known method of controlling supercritical power plants that improves energy efficiency and reduces pressure dynamic stresses. This paper presents a novel approach for developing a supercritical cleaner coal power plant’s sliding pressure control strategy. First, using Whale Optimizer, a nonlinear identified transfer matrix model was created (WO). By comparing simulations and errors, the WO clearly outperforms the GA and Grey-Wolf Optimizer (GWO) techniques on parameter identification. The model also includes a multivariable PI/PD controller for improved plant operation. Again, WO controller tuning outperformed GA and GWO in terms of pressure deviations, power deviations, rise time, and fuel usage. It is now argued that the WO is superior to other techniques in modeling and controlling system dynamics, energy efficiency, and cleaner operation.
“…Another recent linear model was implemented with subspace identification to develop an H ∞ multi‐variable controller for underground coal gasification (UCG) unit, 30 as well as previously proposed models based on the robust linearized multi‐objective H 2 /H ∞ controller that is intended to keep the heating value of syngas constant for UCG 31,32 . Znad et al 33 have presented a novel model predictive control strategy to speed up the start‐up process of a 600 MW SCPP based on a subspace state‐space identified linear model with a multi‐input single‐output structure which proved to help in more savings in fuel and water flows, and hence, fewer emissions. Furthermore, artificial neural networks (ANNs) have been extensively used to capture the SC and USC units' processes, which can be used as validated control‐oriented models 34‐36 .…”
Summary
With the affirmative advancements in clean coal technologies toward more environmentally friendly power systems, it has become critical to gain a comprehensive understanding of the modeling philosophies of such technologies. Supercritical (SC) and ultra‐supercritical (USC) technologies are among the leading options of clean coal technologies, and it is widely acknowledged that they provide flexible power generation for grid electrical demand while sustaining cleaner operations. This paper presents detailed qualitative and quantitative comparisons of the most salient modeling methodologies of a 600 MW SC generation unit which study the once‐through operation to provide future researchers with a better understanding of this field and be used as a reference for more advanced strategies and decision‐making problems, such as efficiency improvements, emissions reduction, and control design for industry personnel, by determining the key characteristics and drawbacks of each modeling technique. These methods are first‐principles or physics‐based modeling, state‐space system identification (SI) modeling using prediction error minimization, and artificial neural networks (ANN) modeling using the non‐linear autoregressive exogenous input (NARX). The comparison was found to be effective in recognizing the diversity in the computational capabilities of the three developed models. The simulation errors for the adopted output responses are presented to quantify the comparison and justify the superiority and imperfection of each method in terms of accuracy. Other qualitative factors for comparison include simplicity and physics‐based sagacity. It has been eventually deduced that in terms of accuracy, the ANN‐based model using NARX is the most accurate model and has the best performance for black‐box modeling, whereas the linearized state‐space model using SI modeling is the most suitable one for designing linear controllers, and the physical model is superior for physical interpretation and stability studies. The control applications of each technique have been highlighted as well.
Highlights
Three different models are presented for a 600 MW cleaner SC boiler‐turbine‐generator unit that covers the once‐through mode.
A comprehensive realization of the three modeling techniques is presented.
Simulation results have shown the suitability of the three methods for this specific application with different capabilities, computational burdens, and possible control methods.
A comparative study is then reported and discussed in detail, which can be used as a future educational guide for future researchers.
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