Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportio...Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional integral derivative (PID) control scheme. For the characteristics of the main steam pressure in coal-fired power plant boiler, the sliding mode control system with Smith predictive structure is proposed to look for performance and robustness improvement. First, internal model control (IMC) and Smith predictor (SP) is used to deal with the time delay, and sliding mode controller (SMCr) is designed to overcome the model mismatch. Simulation results show the effectiveness of the proposed controller compared with conventional ones.展开更多
The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three m...The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.展开更多
Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lea...Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lead to large simulation times, often exceeding transient real time. Artificial neural networks （ANNs） may be advantageous in this context, the main advantage being the speed of computation, the capability of generalizing from the few examples, robustness to noisy and partially incomplete data and the capability of performing empirical input-output mapping without complete knowledge of underlying physics. In this paper, the simulation of steam generator is considered as an example to show the potentialities of this tool. The data required for training and testing the ANN is taken from the steam generator at Abott Power Plant, Champaign （USA）. The total number of samples is 9600 which are taken at a sampling time of three seconds. The performance of boiler （drum pressure, steam flow rate） has been verified and tested using ANN, under the changes in fuel flow rate, air flow rate and load disturbance. Using ANN, input-output mapping is done and it is observed that ANN allows a good reproduction of non-linear behaviors of inputs and outputs.展开更多
基金Supported by the National Natural Science Foundation of China (61174059, 60934007, 61233004)the National Basic Research Program of China (2013CB035406)Shanghai Rising-Star Tracking Program (11QH1401300)
文摘Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional integral derivative (PID) control scheme. For the characteristics of the main steam pressure in coal-fired power plant boiler, the sliding mode control system with Smith predictive structure is proposed to look for performance and robustness improvement. First, internal model control (IMC) and Smith predictor (SP) is used to deal with the time delay, and sliding mode controller (SMCr) is designed to overcome the model mismatch. Simulation results show the effectiveness of the proposed controller compared with conventional ones.
文摘The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.
文摘Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lead to large simulation times, often exceeding transient real time. Artificial neural networks （ANNs） may be advantageous in this context, the main advantage being the speed of computation, the capability of generalizing from the few examples, robustness to noisy and partially incomplete data and the capability of performing empirical input-output mapping without complete knowledge of underlying physics. In this paper, the simulation of steam generator is considered as an example to show the potentialities of this tool. The data required for training and testing the ANN is taken from the steam generator at Abott Power Plant, Champaign （USA）. The total number of samples is 9600 which are taken at a sampling time of three seconds. The performance of boiler （drum pressure, steam flow rate） has been verified and tested using ANN, under the changes in fuel flow rate, air flow rate and load disturbance. Using ANN, input-output mapping is done and it is observed that ANN allows a good reproduction of non-linear behaviors of inputs and outputs.
