Analysis Of Chaos Character Of Pressure Signal In Gas-solid Fluidized Bed And Flow Regime's Identification Method

Gas-solid fluidized bed is the vital installations of the chemical industry, electric power, oil and other's production process. To obtain the information of the flow pattern and flow conversion is essential to improve the performance of the fluidized bed and heat transfer, as well as mass transfer efficiency. The different flow patterns, the performance of the heat transfer and flow mechanism are not the same too. Therefore, to identify flow pattern accurately is an important component of the detect parameters of the two-phase flow. However, as the bubble movement's behavior of the gas-solid fluidized bed is very complex and dynamically non-linear, the study of characteristics the fluidized bed pressure fluctuation signal and flow pattern's identification have brought great difficulties.This paper is based on large amounts of data, will apply second generation wavelet, chaos theory, multi-fractal technology, artificial fish-swarm optimization algorithm, neural network theory to the signal analysis and the flow pattern's recognition. Both from the theory and the experiment we discuss the signal characteristics and the flow pattern's recognition.Firstly, on the gas-solid fluidized bed experimental system platform, we get all the flow pattern of the pressure fluctuation signals. Secondly, to eliminate noise of the pressure fluctuation signals to use the second-generation wavelet which is caused by the fan vibration, the instability of the experimental equipment and so on. Then, we analyze chaotic characteristic for the various flow patterns with the chaos theory and the multi-fractal, calculated the characteristics parameters of its flow pattern, including Hurst index, Lyapunov exponent, correlation dimension, D2 entropy, as well as the multi-fractal parameters. We compare chaos-type features and the different mechanisms of flow patterns under the different flows. Finally we use artificial fish-swarm algorithm for optimizing BP neural network, and take the parameters of chaotic characteristics as characteristic input quantities to identify, computing recognition efficiency, and compare with BP and Elman neural network, get the results of the optimal recognition.This paper is the first use the combination of chaos theory and artificial fish-swarm algorithm to analyze the chaotic characteristics of the pressure fluctuation signal and flow Identification in fluidized bed , it provides a new and effective tools for online diagnosis in order to describe the flow-type conversion mechanism of the fluidized-bed and quantitative identify flow pattern better, it provides a new way to identify the flow pattern of the fluidized bed base on theoretically and technically , at the same time it makes a foundation for the follow-up research work and practical applications.