电容层析成像技术(Electrical Capacitance Tomography, ECT)是一种基于电容敏感场的过程层析成像技术，该技术通过传感器测量所需电容数据，实现充填管道流型检测，从而满足管道流型可控性。传统ECT流型辨识方法识别速率较低、运算较为复杂，成像结果也存在误差。为了改善传统方法存在的问题，提出一种基于粒子群优化算法(Particle Swarm Optimization, PSO)优化深度置信网络—极限学习机(Deep Belief Networks-Extreme Learning Machine, DBN-ELM)的流型识别算法，电容数据采集模块采集电容数据并制作相应数据集，经过DBN网络提取电容数据特征，在DBN网络顶层添加ELM完成对抽象电容流型数据的辨识；DBN隐含层神经元个数影响着整个模型的学习能力和信息处理能力，因此引入PSO优化算法首先计算出每层玻尔兹曼机(Restricted Boltzmann Machine, RBM)的最优神经元个数。与其他流型辨识算法相比，所需时间短，成像效果较好，对加快工业智能化发展有着重要的意义。

Electrical Capacitance Tomography (ECT) is a process tomography technique based on capacitance-sensitive field, which can achieve the flow pattern detection of filled pipes by measuring the required capacitance data with sensors to meet the controllability of pipe flow patterns. The traditional ECT flow pattern recognition method has the disadavntages of low recognition rate, more complicated operation and error in imaging effect. In order to slove the problems of traditional methods, this paper proposes a flow pattern recognition algorithm based on PSO (Particle Swarm Optimization) optimized DBN-ELM (Deep Belief Networks-Extreme Learning Machine), in which the capacitance data acquisition module collects data and creates the corresponding data set, extracts the capacitance data features through DBN network, and adds ELM at the top layer of DBN identify the abstract capacitive data to complete the number of neurons in the hidden layer of DBN affects the learning ability and information processing ability of the whole model, so the PSO optimization algorithm is introduced to calculate the optimal number of neurons for each layer of Restricted Boltzmann Machine (RBM) first, which takes less time and has better imaging effect compared with other flow pattern recognition algorithms. It is of vital importance to accelerate the development of industrial intelligence.