Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine

NIU Peifeng; WANG Xiaofei; LIU Nan; WANG Yuanning; CHANG Lingfang; ZHANG Xianchen

doi:10.11949/j.issn.0438-1157.20180293

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Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine

更新时间：2025-12-10

- Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine
- CIESC Journal Issue 9, Pages: 3924-3931(2018)
- 作者机构：
  
  1. 燕山大学电气工程学院,河北,秦皇岛,066004
  2. 燕山大学,河北,秦皇岛,066004
  3. 大连海事大学环境科学与工程学院,辽宁,大连,116026
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20180293
  CLC： K267
- Online First：05 September 2018，
  
  Published：05 September 2018
- 稿件说明：
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NIU Peifeng, WANG Xiaofei, LIU Nan, et al. Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine[J]. CIESC Journal, 2018, (9): 3924-3931.
DOI：

NIU Peifeng, WANG Xiaofei, LIU Nan, et al. Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine[J]. CIESC Journal, 2018, (9): 3924-3931. DOI： 10.11949/j.issn.0438-1157.20180293.

摘要

针对极限学习机（ELM）不能准确地预测汽轮机热耗率的问题，结合群智能优化算法，提出一种改进的共生生物搜索算法和极限学习机（ASOS-ELM）综合建模的方法。该方法利用改进的共生生物搜索（ASOS）算法优化ELM隐层激活函数的参数，求得最优的ELM模型。再将ASOS-ELM模型应用到热耗率建模中，首先用ELM初始化热耗率预测模型，以输出热耗率的均方根误差（RMSE）作为算法的适应度值，然后通过ASOS算法找到合适的ELM参数，从而得到准确的热耗率预测模型。并将热耗率预测的结果与传统的ELM模型、ASOS算法优化支持向量回归（SVR）模型、改进的粒子群算法（PSO）和基本的共生生物搜索算法（SOS）优化的ELM作对比。结果表明，ASOS-ELM模型在处理复杂的数据模型中，具有精确的预测能力与快速的收敛速度，为汽轮机热耗率建模提供了新思路。

Abstract

The extreme learning machine (ELM) problem can not quickly and accurately predict heat rate. Combined with swarm intelligence optimization algorithm

an ameliorated symbiotic organisms search algorithm and extreme learning machine (ASOS-ELM) comprehensive modeling method is proposed. This method uses the ameliorated symbiotic organisms search (ASOS) algorithm to optimize the parameters of the ELM hidden layer activation function to obtain the optimal ELM model. Firstly

the initial heat rate prediction model is established with ELM

and the root mean square error (RMSE) of the output heat rate is used as the fitness value of the algorithm. Then the appropriate ELM parameters are found through the ASOS algorithm to obtain an accurate heat rate prediction model. The performance of the heat rate prediction is compared with the traditional ELM model

support vector regression (SVR) model optimized by the ASOS algorithm

ELM optimized by improved particle swarm optimization (PSO) and basic symbiotic organisms search algorithm (SOS). The results show that the ASOS-ELM model has a precise forecasting ability and rapid convergence speed when dealing with complex data models

which provides a new idea for modeling the heat rate of a steam turbine.

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