延迟焦化开工线腐蚀机理及温度模型预测

Corrosion Mechanism and Temperature Model Prediction of Delayed Coking Start-up Pipeline

以某延迟焦化装置为研究对象,明确了延迟焦化装置开工线发生的低温湿硫化氢(H2S)腐蚀问题并分析了腐蚀机理,在此基础上确立了开工线温度作为腐蚀失效的表征参量;由于开工线温度直接测量有延迟、成本高,且延迟焦化生产中各环境变量具有较强的非线性、时变性和复杂性,在高斯过程回归(GPR)模型的基础上,提出了基于引力搜索算法(GSA)优化的复合核函数高斯过程回归(GSA-CKGPR)模型,实现了开工线温度的软测量。通过对实际延迟焦化过程数据的训练预测分析,表明该预测模型相比于单核GPR模型、支持向量回归机(SVR)模型以及其他启发式优化算法具有更好的预测精度和稳定性,相对GPR模型均方根误差降低了47.3%,有利于延迟焦化开工线温度的精准预测,可为该装置的工艺操作参数优化及安全稳定运行提供理论支撑。

Low-temperature wet hydrogen sulfide corrosion on the start-up pipeline of a delayed coking unit was identified and corrosion mechanism was discussed.Based on the above results,start-up pipeline temperature was chosen as the corrosion failure indication parameter.Direct start-up pipeline temperature measurement is expensive and always time-delayed,and environmental variables in the delayed coking process is characterized with strong nonlinearity,time-variation and complexity.Therefore,based on the traditional Gaussian process regression(GPR)model,a composite kernel function Gaussian regression model based on gravity search algorithm(GSA)optimization(GSA-CKGPR)was proposed to indirectly measure the start-up pipeline temperature.Through prediction analysis of the delayed coking process data,compared with the support vector regression machine(SVR)model,single-kernel GPR model and other heuristic optimization algorithms,the proposed GSA-CKGPR model is more accurate and stable.Compared with the traditional GPR model,the root mean square error can reduce 47.3%.The proposed model can accurately predict the temperature of delayed coking start-up pipeline and also can be used as a tool to support process optimization,and improve safety and reliability of the unit.