浅析锅炉水包数学模型及控制方法

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。     

横河DCSCENTUMCS3000在汽包液位三冲量控制中的应用

锅炉是化工企业主要的热能生产设备,而汽包水位又是锅炉运行的重要工艺指标之一。本文主要介绍了基于横河DCSCS3000系统实现对锅炉汽包水位的优化控制,论述了汽包三冲量控制的原理及相关工艺流程,着重描述了三冲量控制回路和基于横河CS3000系统软件的三冲量控制的组态。生产实践表明,该控制系统稳定可靠,操作方便,控制效果良好。     

M701联合循环发电机组余热锅炉中压汽包水位控制的分析和改进

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。     

基于神经网络的加氢反应器温度优化控制

根据加氢反应器的特点，提出了一种加氢反应器出口温度神经元网络优化控制方法，给出了作为模型预估器的神经网络GA－BP算法流程及GA算法实现，提出了最优控制指标选择原则及控制指标表达式，经计算机对四床层一段加氢裂化装置进行仿真研究表明，该控制方法具有良好的跟踪性能及抗干扰能力。     

基于DeltaV系统的PID控制算法的实时性能优化

以DeltaV系统中的比例积分微分（Proportional-Integral-Derivative,PID）控制算法为研究对象，以锅炉汽包液位调节为例，针对当前算法的实时性能进行评估与改进。提出了一种改进方法，包括参数优化和控制策略调整。通过提高控制器的响应速度和精度，旨在更好地满足锅炉汽包液位调节的实时要求。实验结果表明，改进后的PID控制算法在锅炉汽包液位调节中具有更好的实时性能，能够更准确地控制液位，提高系统的稳定性和可靠性。     

汽包水位波动原因分析

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。     

基于模糊控制的发电厂汽包水位控制系统

依托模糊控制系统的基本思想,结合汽包水位以及汽包压力对锅炉汽包水位进行控制,使得锅炉的给水量适应锅炉汽包水位要求,实现了锅炉汽包给水的稳定性和鲁棒性.     

粒子群算法在汽包锅炉水位控制系统中的应用研究

锅炉汽包水位控制系统是一种典型的过程控制系统,水位过高容易烧坏过热器,水位过低会造成水冷壁破裂,因此汽包水位的控制是锅炉控制的一个难点。由于锅炉水位控制系统的不确定性和非线性,传统的PID控制方法很难得到满意的控制效果,将利用粒子群算法对PID控制器参数进行优化,通过MATLAB工具,进行仿真研究,利用粒子群算法能够极大地缩短调节时间,减小超调量,增加结果准确度,对过程控制系统的参数优化提供一定的理论指导意义。     

锅炉汽包水位控制系统的研究

锅炉汽包水位是一种非线性、时变大、强耦合的多变量系统。在建直了锅炉汽包水位调节对象的数学模型的基础上，利用模糊控制理论设计锅炉水位控制系统，运用模糊控制与PID控制分别对汽包水位进行控制。介绍了控制器的设计和仿真分析，并应用MATLAB软件对汽包水位控制系统进行验证和仿真，给出了模糊控制方法与传统方法的比较结果，表明了模糊控制明显地改善了汽包水位控制系统的静、动态特性，从而实现了对锅炉汽包水位的最件实时控制。     

基于灰狼优化算法的自抗扰控制在汽包水位中的应用

针对余热锅炉汽包水位控制系统具有非线性、大滞后、强干扰、大惯性等特点,采用自抗扰（ADRC）控制器来改善其控制效果,提高汽包水位控制系统的控制精度及鲁棒性。针对控制器参数过多,引入灰狼算法（GWO）对控制器参数进行整定。利用SIMULINK构建汽包水位控制系统,用灰狼算法调节ADRC-PI控制器与PID-PI控制器参数,对比不同控制器对汽包水位控制的效果。结果表明：GWO-ADRC-PI控制效果具有超调量小、鲁棒性强和抗干扰能力强等优点。     

A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler

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.     

智能优化算法的研究及其在锅炉燃烧中的应用

本文提出用智能优化算法模型来指导工业锅炉燃烧过程控制。首先通过免疫遗传算法来优化锅炉燃烧过程控制参数,利用优化结果调节锅炉燃烧过程控制。而后利用BP神经网络来模拟工业锅炉燃烧过程,建立工业锅炉燃烧过程输入输出之间的非线性模型。结果表明,本文所提出的智能优化算法模型具有较好的效果。     

遗传算法在电站锅炉燃烧优化中的应用

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。     

基于改进PSO算法的过热汽温神经网络预测控制

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.     

一种基于GA优化模糊推理神经网络的新方法

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。     

基于禁忌遗传优化的网络拥塞控制算法

为解决网络拥塞问题,提出一种基于禁忌遗传优化的网络拥塞控制算法TSGA。该算法将禁忌搜索和遗传算法相结合,以网络资源消耗最小化和网络负载均衡分布为目标,建立多约束QoS路由优化数学模型。仿真实验结果证明,该算法可实现网络拥塞控制,有效提高网络性能。     

复杂工业过程的遗传模糊神经网络控制

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。     

Design of highly effective multilayer feedforward neural network by using genetic algorithm

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.     

Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm

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.     

A novel optimal PID controller autotuning design based on the SLP algorithm

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.