共查询到20条相似文献,搜索用时 156 毫秒
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锅炉汽包水位是现代电厂锅炉安全运行的重要监控参数。本文在综合分析了以往汽包蒸发区数学模型的基础上,针对锅炉蒸发区的特点,依据质量和能量平衡,建立了汽包水位非稳态数学模型,并提出了水位控制的方法,对汽包锅炉的安全运行和控制具有参考意义。 相似文献
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邓小明 《自动化与仪器仪表》2011,(4):120-121
M7 0 1 F联合循环发电机组的余热锅炉由于中压汽包设计容量偏小,在机组启停阶段,中压汽包水位控制难以投入自动。本文对M701F联合循环两班制(DSS)运行机组的余热锅炉中压汽包水位控制的特点和难点进行了解析;同时对余热锅炉在汽包上水、启动升负荷和正常运行分三阶段的中压汽包水位控制进行了详细的分析。最后提出的改进和解决方案,可为同类兄弟电厂作参考和借鉴。 相似文献
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确保锅炉汽包水位处于正常范围当中,是保证锅炉安全稳定运行非常重要的指标之一。但是在多种因素影响下会导致汽包水位出现频繁波动,进而对火电厂安全稳定运转造成不利影响。对某电厂中的2#机组存在的汽包水位波动原因进行分析,锅炉燃烧情况对于汽包水位波动具有直接的影响作用,通过采取有效的措施能够控制波动发生,最后提出部分改善波动情况的措施,以期对相关的技术人员有所借鉴作用。 相似文献
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Li-Gang Zheng Hao Zhou Ke-Fa Cen Chun-Lin Wang 《Expert systems with applications》2009,36(2):2780-2793
NOx emissions from power plants pose terrible threat to the surrounding environment. The aim of this work is to achieve low NOx emissions form a coal-fired utility boiler by using combustion optimization. Support vector regression (SVR) was proposed in the first stage to model the relation between NOx emissions and operational parameters of the utility boiler. The grid search method, by comparing with GA, was preferably chosen as the approach for the selection of SVR’s parameters. A mass of NOx emissions data from the utility boiler was employed to build the SVR model. The predicted NOx emissions from SVR model were in good agreement with the measured. In the second stage, two variants of ant colony optimization (ACO) as well as genetic algorithm (GA) and particle swarm optimization (PSO) were employed to find the optimum operating parameters to reduce the NOx emissions. The results show that the hybrid algorithm by combining SVR and optimization algorithms with the exception of PSO can effectively reduce NOx emissions of the coal-fired utility boiler below the legislation requirement of China. Comparison among various algorithms shows the performance of the well-designed ACO outperforms those of classical GA and PSO in terms of the quality of solution and the convergence rate. 相似文献
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WANG Li-rong 《数字社区&智能家居》2008,(12)
提出了一种新的RBF神经网络的设计方法,采用遗传-K均值聚类算法对RBF神经网络的隐层节点中心值进行优选,用遗传算法训练RBF神经网络的权值。以锅炉燃烧为实例,通过从现场采集的数据建立神经网络模型,并用遗传算法寻找最优输入变量组合,实现锅炉燃烧优化。 相似文献
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基于改进PSO算法的过热汽温神经网络预测控制 总被引:1,自引:0,他引:1
将改进粒子群优化算法(MPSO)融合到神经网络预测控制中,提出了基于MPSO-RBF混合优化策略的模型预测器,以及基于MPSO算法的非线性优化控制器.针对过热汽温的控制,构造了基十神经网络预测控制的串级控制系统,并就该系统在实现时所涉及到的预测模型、滚动优化算法、反馈校正、仿真参数设置问题等进行了分析,给出了MPSO算法的粒子编码、操作设计和混合优化算法步骤.对某超临界600 MW直流锅炉高温过热器的过热汽温控制,进行了仿真试验,结果表明该方法具有良好的性能指标和应用前景. 相似文献
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一种基于GA优化模糊推理神经网络的新方法 总被引:1,自引:0,他引:1
通过对已有的一些基于遗传算法(GA)优化模糊系统方法的分析,指出了它们存在的一些缺陷,提出了一种新颖的基于GA优化模型推理神经网络的方法,并给出了相应的优化算法,这种方法可以对模糊推理系统中的所有结构和参数同时或分别进行优化。在此基础上,还讨论了模糊推理神经网络的精简问题,如无用模糊规则的删除,最后通过实例验证了该方法是一种很有效的方法,具有易理解,精度高,收敛快,泛化能力好且能全局收敛的优点。 相似文献
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复杂工业过程的遗传模糊神经网络控制 总被引:3,自引:0,他引:3
本文提出一种基于遗传算法和监督学习方法的有效模糊神经网络控制,这种控制器采用并行处理的推理网络,具有两个重要特点:自适应和学习性,所提方法经过仿真和温控验证表明控制性能良好。 相似文献
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This paper presents a highly effective and precise neural network method for choosing the activation functions (AFs) and tuning the learning parameters (LPs) of a multilayer feedforward neural network by using a genetic algorithm (GA). The performance of the neural network mainly depends on the learning algorithms and the network structure. The backpropagation learning algorithm is used for tuning the network connection weights, and the LPs are obtained by the GA to provide both fast and reliable learning. Also, the AFs of each neuron in the network are automatically chosen by a GA. The present study consists of 10 different functions to accomplish a better convergence of the desired input–output mapping. Test studies are performed to solve a set of two-dimensional regression problems for the proposed genetic-based neural network (GNN) and conventional neural network having sigmoid AFs and constant learning parameters. The proposed GNN has also been tested by applying it to three real problems in the fields of environment, medicine, and economics. Obtained results prove that the proposed GNN is more effective and reliable when compared with the classical neural network structure. 相似文献
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This paper reports a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. A mathematical model is established to illustrate the sensor network optimization based on fault-sensor dependence matrix. The crossover and mutation operators of genetic algorithm (GA) are introduced into the update strategy of shuffled frog leaping algorithm (SFLA) and a new ID-SFLA is systematically developed. Numerical simulation results show that the ID-SFLA has an excellent global search ability and outstanding convergence performance. The ID-SFLA is applied to the sensor’s optimal selection for a gearbox. In comparison with GA and discrete shuffled frog leaping algorithm (D-SFLA), the proposed ID-SFLA not only poses an effective solving method with swarm intelligent algorithm, but also provides a new quick algorithm and thought for the solution of related integer NP-hard problem. 相似文献
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Jirapun Pongfai Xiaojie Su Huiyan Zhang Wudhichai Assawinchaichote 《Expert Systems》2020,37(2):e12489
A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems. 相似文献