提出了运用Elman型回归神经网络来建立裂解深度软测量模型．解决控制参数测量中在线分析仪表的滞后等问题。基于裂解深度软测量模型．在DCS(Distributed Control System)系统中开发并实施了裂解深度智能控制系统．通过其与已有的裂解炉...提出了运用Elman型回归神经网络来建立裂解深度软测量模型．解决控制参数测量中在线分析仪表的滞后等问题。基于裂解深度软测量模型．在DCS(Distributed Control System)系统中开发并实施了裂解深度智能控制系统．通过其与已有的裂解炉出口温度先进控制系统的集成．实现裂解炉裂解深度的平稳控制。现场运行结果表明．该系统的投用有效提高了裂解炉裂解产物中乙烯和丙烯的收率．进一步提高了乙烯裂解装置的经济效益。展开更多
A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low ...A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.展开更多
文摘提出了运用Elman型回归神经网络来建立裂解深度软测量模型．解决控制参数测量中在线分析仪表的滞后等问题。基于裂解深度软测量模型．在DCS(Distributed Control System)系统中开发并实施了裂解深度智能控制系统．通过其与已有的裂解炉出口温度先进控制系统的集成．实现裂解炉裂解深度的平稳控制。现场运行结果表明．该系统的投用有效提高了裂解炉裂解产物中乙烯和丙烯的收率．进一步提高了乙烯裂解装置的经济效益。
基金This work is supported by National Natural Science Foundation of China （50776005）.
文摘A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.
