深度学习在流程工业的软测量领域已经得到了应用.然而,深度神经网络 (DNN)的结构和参数需要人工调整,这需要扎实的机器学习知识基础和丰富的参数调整经验,烦琐的调整过程限制了深度学习在化工领域的推广应用.在大量实验的基础上,对 DNN 的每个关键参数的选取过程进行了系统化的分析,提出了几乎无须人工干预的基于 DNN 软测量的结构和参数自动调整方法,极大地简化了参数调整过程,能够给工程技术人员学习及应用深度学习提供参考.对原油蒸馏装置及煤气化装置的案例分析验证了所提出方法的有效性和通用性.%Deep learning has been applied to the field of soft sensing in process industries. However, the structure and parameters of deep neural network (DNN) have to be tuned manually, which require solid fundamental knowledge about machine learning and rich experiences on parameters tuning. Complicated tuning procedure restricts generalization application of deep learning in chemical industries. A structure and parameters tuning method for DNN soft sensor with little manual intervention was proposed by systematic analysis on selection process of each essential DNN parameter from massive experiments. The presented method could greatly simplify the tuning procedure and offer a reference for engineers to study and use deep learning. Studies on crude-oil distillation and coal gasification process verified effectiveness and generality of the proposed method.
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