Mathematical Problems in Engineering

Volume 2015, Article ID 542182, 7 pages

http://dx.doi.org/10.1155/2015/542182

## Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

^{1}The School of Electric and Control Engineering, Xi’an University of Science and Technology, Shaanxi, Xi’an 710054, China^{2}Department of Information Engineering, Sichuan Vocational and Technical College of Communications, Sichuan, Chengdu 611130, China

Received 19 May 2015; Accepted 22 July 2015

Academic Editor: Alessandro Gasparetto

Copyright © 2015 Xue-cun Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

#### 1. Introduction

The coal slurry as one waste is transported through pipelines to circulating fluidized bed boiler for mixed burning in gangue power plant; on the one hand, the low calorific value energy can be made full use of, and the coal slurry can be processed; on the other hand, pollution is prevented, which brings good economic and social benefits for the coal mine enterprises. At present, the coal slurry is transported through pipelines and wet transportation technology is applied, which can solve the secondary pollution problem caused by transportation process [1]. If the moisture content of coal slurry is too low, or in the process of coal slurry transfer and storage, miscellaneous articles or objects enter into coal slurry, then it can make the coal quality unstable and causes the pipeline blockage. When the blockage happens, in the upstream of choke point, compared with the normal pressure, pipeline pressure at every point is higher, and in the downstream of choke point, pipeline pressure is lower, and even to zero. Therefore, the pressure of pipeline measuring point can be a feature of blockage prediction; in this paper, pressure prediction research of coal slurry pipeline will be made, which lays a foundation for blockage prediction.

Artificial neural network and support vector machine (SVM) can deal well with nonlinear regression problem and have wide application in the future data prediction. But much study has found that feedforward neural network has some problems such as having slow learning speed, being easy to fall into local minimum, and being sensitive to parameter selection [2–4], of which BP algorithm acts as a representative. Support vector machine (SVM) works as a kind of small sample learning algorithm, although there is a certain advantage in the small sample learning, but it also has slow learning speed, and its performance is more sensitive to the selection of kernel parameter. When SVM meets large data, its computational complexity is very high and consumed time is long [5–8]. In this paper, based on the present research of forecasting methods, combined with the advantages of particle swarm optimization (PSO) algorithm and extreme learning machine, pressure prediction method based on kernel function extreme learning machine optimized by particle swarm algorithm is proposed to predict the coal slurry pipeline pressure.

#### 2. Kernel Function Extreme Learning Machine

Guangbin Huang has proposed a new learning algorithm called extreme learning machine (ELM) [9]; it is based on the single hidden layer feedforward neural network. Firstly, all parameters of hidden layer nodes for single hidden layer feedforward neural network are randomly selected (input weights and thresholds of hidden layer nodes); then the network output weights are analyzed and calculated. All the parameters of the hidden layer nodes in the ELM are independent of the objective function and the training samples and do not need network iterative adjustment. In theory, this algorithm can get good generalization performance at an extremely rapid learning speed.

ELM has shown great potential in solving problem such as data regression and classification, which overcomes some challenging limits when feedforward neural networks or other intelligent algorithms are used. Compared with the more popular Back-Propagation (BP) and Support Vector Machine (SVM), ELM not only inherits the various advantages of neural network and support vector machine (SVM) but also has a lot of outstanding characteristics.(1)ELM is easy to use, requiring less human intervention.(2)ELM has faster speed of learning. Some training learning can be done in a few seconds or minutes.(3)ELM has better generalization performance. In most cases, ELM can obtain a better generalization performance than BP and get close or better generalization performance than the SVM.(4)ELM applies to most of the nonlinear activation functions.(5)This algorithm is simpler. ELM is a simple algorithm with three steps that does not need to be adjusted, and simple mathematics is enough for use.(6)ELM cannot meet problems such as local minimum, inappropriate learning speed, and the overfitting, where traditional classical learning algorithm can be faced with.

Figure 1 is the structure of extreme learning machine.