自动化仪表
自動化儀錶
자동화의표
PROCESS AUTOMATION INSTRUMENTATION
2014年
12期
88-93
,共6页
煤调湿%模糊核C均值聚类%最小二乘支持向量机(LSSVM)%贝叶斯证据框架%多模型
煤調濕%模糊覈C均值聚類%最小二乘支持嚮量機(LSSVM)%貝葉斯證據框架%多模型
매조습%모호핵C균치취류%최소이승지지향량궤(LSSVM)%패협사증거광가%다모형
Coal moisture control%Fuzzy kernel C-means clustering%Least squares support vector machines(LSSVM)%Bayesian evidence framework%Multi-model
煤调湿工艺是炼焦过程的关键技术，建立煤调湿系统的蒸汽消耗量软测量模型对节约生产成本、减少环境污染和提高焦炭质量具有促进作用。针对某炼铁厂煤调湿装置的非线性、强耦合、工况波动大等特点，提出了一种多模型建模方法。对模糊核C均值聚类引入了基于密度的聚类中心初始化方法和聚类数目自适应策略，并将其用于生产工况最优划分；使用最小二乘支持向量机对每个子工况进行数据驱动建模，并利用贝叶斯证据框架优化最小二乘支持向量机的超参数。检验结果表明，所提出的煤调湿系统蒸汽消耗量多模型具有良好的跟踪性能与较高的预测精度。
煤調濕工藝是煉焦過程的關鍵技術，建立煤調濕繫統的蒸汽消耗量軟測量模型對節約生產成本、減少環境汙染和提高焦炭質量具有促進作用。針對某煉鐵廠煤調濕裝置的非線性、彊耦閤、工況波動大等特點，提齣瞭一種多模型建模方法。對模糊覈C均值聚類引入瞭基于密度的聚類中心初始化方法和聚類數目自適應策略，併將其用于生產工況最優劃分；使用最小二乘支持嚮量機對每箇子工況進行數據驅動建模，併利用貝葉斯證據框架優化最小二乘支持嚮量機的超參數。檢驗結果錶明，所提齣的煤調濕繫統蒸汽消耗量多模型具有良好的跟蹤性能與較高的預測精度。
매조습공예시련초과정적관건기술，건립매조습계통적증기소모량연측량모형대절약생산성본、감소배경오염화제고초탄질량구유촉진작용。침대모련철엄매조습장치적비선성、강우합、공황파동대등특점，제출료일충다모형건모방법。대모호핵C균치취류인입료기우밀도적취류중심초시화방법화취류수목자괄응책략，병장기용우생산공황최우화분；사용최소이승지지향량궤대매개자공황진행수거구동건모，병이용패협사증거광가우화최소이승지지향량궤적초삼수。검험결과표명，소제출적매조습계통증기소모량다모형구유량호적근종성능여교고적예측정도。
Coal moisture control technology is the key technique of the coking process. Establishing the soft sensing model for vapor consumption in coal moisture control system may save production costs, reduce environmental pollution and improve the quality of coke. In accordance with the features of the coal moisture control equipment in certain ironworks, e. g. , nonlinearity, strong coupling and large fluctuations of working condition, the multi-model modeling method is proposed. The density-based clustering center initialization method and number of clusters adaptive strategy are integrated into the fuzzy C-means clustering to get optimal division of the production working conditions. The least squares support vector machines ( LSSVM ) is applied to conduct data-driven modeling for each sub-working conditions, and the parameters of the LSSVM is optimized by adopting Bayesian evidence framework. The checked results indicate that the proposed multi-model possesses good tracking performance and higher prediction accuracy.
