新型人工智能技术研究及其在锅炉燃烧优化中的应用
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摘要
人工智能技术是人们从生物进化机理和一些自然现象中受到启发而提出的一类新方法，因其能很好的解决复杂系统的建模和优化问题，故受到各领域的广泛关注和应用。针对极其复杂的物理化学变化的电站锅炉燃烧过程，传统方法很难描述出燃烧特性模型，不易实现锅炉燃烧优化。因此，本文针对人工智能领域中的启发式优化算法、神经网络等重点问题进行了专题研究，并将它们应用于锅炉燃烧过程中，以便于实现电站锅炉的高效低污染燃烧。本文的研究内容具有理论意义和实用价值，其主要内容描述如下：
首先，本文针对人工蜂群算法(Artificial Bee Colony,ABC)的不足，提出了两类改进策略：为了提高ABC的收敛精度和运行稳定性，同时降低ABC的收敛迭代次数，本文提出了一种改进的人工蜂群算法(ImprovedArtificial Bee Colony, I-ABC)，在该算法中引入了三个全新的变量——当前最优解、惯性权值和加速系数，另外还提出了一种具有预测选择能力的混合人工蜂群算法(Artificial Bee Colony with the abilities ofPrediction and Selection, PS-ABC)，其兼具ABC、I-ABC和Gbest-guided ABC(GABC)三种算法的优化性能。在尽量保证或尽可能提高PS-ABC算法精度的同时降低PS-ABC的运行时间和收敛迭代时间的前提下，本文还提出了另一种改进的方法，称之为PS-ABCⅡ，该算法与PS-ABC主要不同在于：1)种群初始化不同；2)候选解的选择方式不同；3)雇佣蜂变成侦查蜂的模式不同。为了验证上述改进算法的有效性，本文通过23个基准优化问题对其进行测试，仿真结果说明了改进算法具有很好的全局搜索能力和收敛速度。
其次，本文提出了一种具有快速学习功能的新型人工神经网络——快速学习网(Fast Learning Network, FLN)。它是一种双并联型前馈神经网络,其不仅接收来自隐层神经元的信息，而且还可以直接从输入层接收相关信息。本文采用了9个经典的回归数据集验证了快速学习网的学习能力和测试反应能力。另外本文针对快速学习网的不足，给出了两种改进型的学习算法：优化型快速学习网和最小二乘快速学习网(Least Square Fast Learning Network, LSFLN)，并将这两种算法应用到了两台煤粉炉(吉林某300MW和江苏某330MW)的燃烧系统的特性建模之中，结果表明其具有很好的辨识性能和普适性。
为了使快速学习网具有在线学习能力，本文给出了两种在线学习算法——在线快速学习网(Online Fast Learning Network, OFLN)和在线最小二乘快速学习网(OnlineLeast Square Fast Learning Network, OLSFLN)，并将其应用到了300MW煤粉炉燃烧热效率的在线模拟建模过程中，仿真实验证明OLSFLN能准确地预测出热效率，同时也说明了其具有很好的在线学习能力、泛化能力、稳定性和可重复性。
另外，本文还提出了一种离线型的组合建模方法，其主要是由主模型和补偿模型两部分组合而成。随后将其应用到了吉林某300MW煤粉炉的热效率建模过程中，仿真实验证明该组合方法能高精度的预测锅炉热效率，同时具有良好的泛化能力。
最后，本文在所建立的两台电站锅炉的离线和在线模型的基础上，首先针对从300MW和330MW煤粉炉所采集数据特点，确定各自的优化目标函数。然后采用改进的人工蜂群算法对两台电站锅炉的可调运行参数进行优化，寻找最佳锅炉运行参数组合，使煤粉炉在最佳参数下运行，从而达到燃烧优化的目的。
Artificial intelligence consists of methods inspired by the mechanisms of biologicalevolution and some natural phenomena, which could solve complex systems’ modelingand optimization. Therefore, it could attract more and more attentions of specialist andscholar. For the extremely complex physical and chemical changes of boilers’ combustionprocess in power plant, it is very difficult to set up models of combustion process and toachieve the combustion optimization objective. Therefore, a great deal of research wouldbe done on heuristic optimization methods and neural networks that would be applied tothe combustion process in order to make a boiler combust at high efficiency and lowpollution.
Firstly, for the shortage of Artificial Bee Colony (ABC), this paper proposes twoimproved strategies: To improve the convergence precision and stability and reduce theconvergence iterations of ABC, this paper propose two improved methods, an ImprovedArtificial Bee Colony(I-ABC) in which the best-so-far solution, inertia weight andacceleration coefficients are introduced, and an Artificial Bee Colony with the abilities ofPrediction and Selection(PS-ABC) which inherits the bright sides of ABC, I-ABC andGbest-guided ABC(GABC); To reduce the runtime and the convergence time andsimultaneously guarantee or improve the optimization accuracy of PS-ABC, this paperproposes another improved method called PS-ABCⅡwhich has three major differencesfrom PS-ABC:1) population initialization;2) the technique that chooses candidatesolutions;3) the mode that employed bees become scouts. In order to test the validity ofimproved methods, they are applied to23benchmark optimization problems. Experimentsshow they have very good global search ability and convergence speed.
Secondly, this paper presents a novel artificial neural network with a fast learningability called Fast Learning Network (FLN). The FLN is a Double Parallel ForwardNeural Network, whose output nodes not only receive the recodification of the externalinformation through the hidden nodes, but also receive the external information itselfdirectly through the input nodes. This paper adopts9classical regression data sets to validate its learning and generalization abilities. In addition, for the shortage of FLN, thispaper also presents two improved learning methods: Optimized Fast Learning Network;Least Square Fast Learning Network (LSFLN), which are employed to set up models ofthe combustion processes of two pulverized coal fired boilers (a300MW and a330MW).Experimental results show that they have very good training precision, stability andgeneralization performances.
In order to make FLN have online learning ability, this paper presents two onlinelearning algorithms: Online Fast Learning Network (OFLN) and Online Least Square FastLearning Network (OLSFLN), and adopts them to set up the online model of thecombustion thermal efficiency of the300MW coal-fired boiler. Simulation results showthat OLSFLN could accurately predict the thermal efficiency, and simultaneously stateOLSFLN has very good online learning, generalization abilities, stability and repeatability.
Thirdly, this paper presents an offline combination modeling method, which iscomposed of a global model and a compensation model, to model the thermal efficiencyof the300MW pulverized coal boiler. Experimental results show that this combinationmodel could predict the thermal efficiency at a high-precision level, and have very goodstability and generalization performances.
Finally, based on the offline and online models of two coal-fired boilers, this paperdetermines their optimization objective functions according to the respectivecharacteristics of the300MW and the330MW coal-fired boilers. Then improved artificialbee colony algorithms are employed to optimize the adjustable operational parameters ofthe two boilers, and search the optimum boiler operational parameter combination.Provided the pulverized coal boilers could combust at the optimum parameters, they couldachieve aims of the combustion optimization.
首先，本文针对人工蜂群算法(Artificial Bee Colony,ABC)的不足，提出了两类改进策略：为了提高ABC的收敛精度和运行稳定性，同时降低ABC的收敛迭代次数，本文提出了一种改进的人工蜂群算法(ImprovedArtificial Bee Colony, I-ABC)，在该算法中引入了三个全新的变量——当前最优解、惯性权值和加速系数，另外还提出了一种具有预测选择能力的混合人工蜂群算法(Artificial Bee Colony with the abilities ofPrediction and Selection, PS-ABC)，其兼具ABC、I-ABC和Gbest-guided ABC(GABC)三种算法的优化性能。在尽量保证或尽可能提高PS-ABC算法精度的同时降低PS-ABC的运行时间和收敛迭代时间的前提下，本文还提出了另一种改进的方法，称之为PS-ABCⅡ，该算法与PS-ABC主要不同在于：1)种群初始化不同；2)候选解的选择方式不同；3)雇佣蜂变成侦查蜂的模式不同。为了验证上述改进算法的有效性，本文通过23个基准优化问题对其进行测试，仿真结果说明了改进算法具有很好的全局搜索能力和收敛速度。
其次，本文提出了一种具有快速学习功能的新型人工神经网络——快速学习网(Fast Learning Network, FLN)。它是一种双并联型前馈神经网络,其不仅接收来自隐层神经元的信息，而且还可以直接从输入层接收相关信息。本文采用了9个经典的回归数据集验证了快速学习网的学习能力和测试反应能力。另外本文针对快速学习网的不足，给出了两种改进型的学习算法：优化型快速学习网和最小二乘快速学习网(Least Square Fast Learning Network, LSFLN)，并将这两种算法应用到了两台煤粉炉(吉林某300MW和江苏某330MW)的燃烧系统的特性建模之中，结果表明其具有很好的辨识性能和普适性。
为了使快速学习网具有在线学习能力，本文给出了两种在线学习算法——在线快速学习网(Online Fast Learning Network, OFLN)和在线最小二乘快速学习网(OnlineLeast Square Fast Learning Network, OLSFLN)，并将其应用到了300MW煤粉炉燃烧热效率的在线模拟建模过程中，仿真实验证明OLSFLN能准确地预测出热效率，同时也说明了其具有很好的在线学习能力、泛化能力、稳定性和可重复性。
另外，本文还提出了一种离线型的组合建模方法，其主要是由主模型和补偿模型两部分组合而成。随后将其应用到了吉林某300MW煤粉炉的热效率建模过程中，仿真实验证明该组合方法能高精度的预测锅炉热效率，同时具有良好的泛化能力。
最后，本文在所建立的两台电站锅炉的离线和在线模型的基础上，首先针对从300MW和330MW煤粉炉所采集数据特点，确定各自的优化目标函数。然后采用改进的人工蜂群算法对两台电站锅炉的可调运行参数进行优化，寻找最佳锅炉运行参数组合，使煤粉炉在最佳参数下运行，从而达到燃烧优化的目的。
Artificial intelligence consists of methods inspired by the mechanisms of biologicalevolution and some natural phenomena, which could solve complex systems’ modelingand optimization. Therefore, it could attract more and more attentions of specialist andscholar. For the extremely complex physical and chemical changes of boilers’ combustionprocess in power plant, it is very difficult to set up models of combustion process and toachieve the combustion optimization objective. Therefore, a great deal of research wouldbe done on heuristic optimization methods and neural networks that would be applied tothe combustion process in order to make a boiler combust at high efficiency and lowpollution.
Firstly, for the shortage of Artificial Bee Colony (ABC), this paper proposes twoimproved strategies: To improve the convergence precision and stability and reduce theconvergence iterations of ABC, this paper propose two improved methods, an ImprovedArtificial Bee Colony(I-ABC) in which the best-so-far solution, inertia weight andacceleration coefficients are introduced, and an Artificial Bee Colony with the abilities ofPrediction and Selection(PS-ABC) which inherits the bright sides of ABC, I-ABC andGbest-guided ABC(GABC); To reduce the runtime and the convergence time andsimultaneously guarantee or improve the optimization accuracy of PS-ABC, this paperproposes another improved method called PS-ABCⅡwhich has three major differencesfrom PS-ABC:1) population initialization;2) the technique that chooses candidatesolutions;3) the mode that employed bees become scouts. In order to test the validity ofimproved methods, they are applied to23benchmark optimization problems. Experimentsshow they have very good global search ability and convergence speed.
Secondly, this paper presents a novel artificial neural network with a fast learningability called Fast Learning Network (FLN). The FLN is a Double Parallel ForwardNeural Network, whose output nodes not only receive the recodification of the externalinformation through the hidden nodes, but also receive the external information itselfdirectly through the input nodes. This paper adopts9classical regression data sets to validate its learning and generalization abilities. In addition, for the shortage of FLN, thispaper also presents two improved learning methods: Optimized Fast Learning Network;Least Square Fast Learning Network (LSFLN), which are employed to set up models ofthe combustion processes of two pulverized coal fired boilers (a300MW and a330MW).Experimental results show that they have very good training precision, stability andgeneralization performances.
In order to make FLN have online learning ability, this paper presents two onlinelearning algorithms: Online Fast Learning Network (OFLN) and Online Least Square FastLearning Network (OLSFLN), and adopts them to set up the online model of thecombustion thermal efficiency of the300MW coal-fired boiler. Simulation results showthat OLSFLN could accurately predict the thermal efficiency, and simultaneously stateOLSFLN has very good online learning, generalization abilities, stability and repeatability.
Thirdly, this paper presents an offline combination modeling method, which iscomposed of a global model and a compensation model, to model the thermal efficiencyof the300MW pulverized coal boiler. Experimental results show that this combinationmodel could predict the thermal efficiency at a high-precision level, and have very goodstability and generalization performances.
Finally, based on the offline and online models of two coal-fired boilers, this paperdetermines their optimization objective functions according to the respectivecharacteristics of the300MW and the330MW coal-fired boilers. Then improved artificialbee colony algorithms are employed to optimize the adjustable operational parameters ofthe two boilers, and search the optimum boiler operational parameter combination.Provided the pulverized coal boilers could combust at the optimum parameters, they couldachieve aims of the combustion optimization.
引文
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