自适应Kernel学习网络在TE过程组分仪建模中的应用

Adaptive Kernel learning networks with application to modeling of analyzer in TE process

化工产品终端质量的测量往往具有较大的延迟,且相应的测量仪价格昂贵,易发生故障。基于统计学习理论和Kernel方法,提出一种自适应Kernel学习(AKL)网络,用于TennesseeEastman(TE)过程中产品组分仪的建模和故障监测。给出了AKL网络在两种情况下的递推算法,只需极少量的学习样本,即可建立软组分仪的动态模型。且AKL网络可以监测故障的发生,通过模型的自动切换,确保在各种工况下,所得到的软组分仪均具有足够的精度。

Industrial end-product qualities, e.g. , the composition fraction and molecular weight etc, are usually measured by using corresponding analyzers with considerable delay. The analyzer system, moreover, is expensive, unreliable and difficult to maintain. An adaptive Kernel leaning （AKL） network was proposed to build the soft sensor model for industrial analyzer and meanwhile to monitor its potential faults. The network utilized Kernel function and geometric angle to build an adaptive network topology. Two forms of learning strategies for the AKL network were obtained and their corresponding recursive algorithms are developed, respectively. Numerical simulations for analyzer of the Tennessee Eastman （TE） process showed that the soft composition analyzer developed by using the proposed AKL networks could achieve satisfying estimation precision under both normal and fault-existing operating conditions.