铝电解行业面临资源、能源、环保和高素质人员短缺等方面的重大挑战，企业常常处于临界盈亏状态，对生产过程精细化控制和工况自动准确识别提出了迫切需求，而目前仍依赖操作人员进行电解槽工况识别方式。实现工况自动准确识别的难点主要在于铝电解过程领域知识复杂，知识关系描述困难，缺乏有效的数据-知识融合机制和智能识别方法。为此，本课题基于知识自动化的思想，将工艺知识库、数据-知识协同方法、推理识别方法相结合，构建具有后验关联性的分层跨域知识关系网络模型，研究异构关联数据的个性化特征提取与知识协同方法，提出基于联合推理的知识网络溯因分析与工况识别方法，实现个性化槽况智能识别验证，从而形成数据与知识协同的铝电解生产过程个性化工况溯因识别方法。本课题将在工况识别方面为实现铝电解生产过程精细化、个性化、智能化控制提供有力的理论方法支撑，丰富知识自动化的科学研究内容，对于实现铝电解工业精细化绿色化生产具有重要意义。

Aluminum electrolysis industry is facing the austere issues, such as overcapacity, high cost and environmental protection, in addition, aluminum electrolysis enterprises are often in a critical state of profit and loss. Therefore it is urgent to put forward the fine control and precise identification of the production process for aluminum electrolysis industry. However, traditional manual analysis and operation are unable to cope with the challenge. The precise and automatic identification is restricted by knowledge relationship hard to describe, inefficient method of data-knowledge fusion and lack of automatic identification algorithms. To this end, according to the ideology of knowledge automation, combining with the process knowledge solidification and extraction of characteristics of individualized conditions from data for reasoning, to build the relationship network model of knowledge with posterior relevance and layered cross-domain. Research on the collaborative method of characteristics of individualized and knowledge for heterogeneous data, to propose the root cause analysis and cell condition identification method for knowledge network model based on joint reasoning, in addition, it will get application validation of cell condition identification based on root cause analysis, resulting in getting the identification method and system of individualized cell conditions for the aluminum electrolysis process based on the synergy between data and knowledge in this subject. This subject provides a effective and scientific way to support the fine, personalized and intelligent control for the aluminum electrolysis process in the cell condition identification, and it is the enrichment of knowledge automation theory and methods. Moreover, the subject plays an important role in the improvement of production efficiency, promoting energy-saving emission reduction and the green production model changing of aluminum electrolytic industry.