Abstract:

In view of the problem that objective function is difficult to calculate online, a process-data-based online economic performance grading assessment method is proposed. Autoregressive projection to latent structure algorithm (AR-PLS) is used to decompose input data matrix. Then, offline models of different performance grades are established in the subspace related to output latent variable, and thus the unrelated-output variation is eliminated. Afterwards, a similarity-grid model is designed using strategy of “calibration zoning, then comparing the similarity of adjacent grades”. The method can divide process performance into steady performance grade state and transition state. Performance variations caused by factors excluded in offline model can be identified to enrich the offline database further. When the evaluation result is nonoptimal, the cause of performance variation can be diagnosed by the variable contribution. Finally, ethylene cracking process data test shows the method can help to detect performance deviation in time and accurately.

Key words: economics, performance grade, model, steady state, transition, variable contributions

摘要：

针对经济性能评估方法中目标函数难以在线计算问题提出一种基于过程数据的在线经济性能分级评估方法。采用自回归潜结构映射（AR-PLS）算法对输入数据矩阵进行分解，在与输出潜变量相关的子空间上建立不同性能等级的离线模型，从而排除无关变化的干扰。然后采用“先标定分区，再对比邻级相似度”的策略设计一个相似度网格模型，将过程性能分为稳定性能级状态和过渡状态，并对离线模型中未出现过的因素造成的性能变化进行识别，以进一步丰富离线数据库。对于不属于最优性能级的过程数据，能够根据变量贡献度诊断造成性能变差的原因。乙烯裂解过程的现场数据测试实验表明本方法可以及时、准确地检测到经济性能的偏移。

关键词: 经济, 性能等级, 模型, 稳态, 过渡, 变量贡献度