用机器学习分析炼油优化结果，发现数据错误与业务机会
From Data to Action: Accelerating Refinery Optimization with AI
- 用改进的异常检测方法分析高维炼油数据
- 识别出真实存在的数据供应错误和运营优化点
- 适合炼油企业决策支持系统开发者参考
当前炼油优化依赖海量数据，虽可通过现代线性规划（LP）软件处理，但结果解读与应用仍具挑战。大型石化企业使用包含数十万输入矩阵元素的复杂模型，尽管LP解在数学上正确，但模型简化和数据误差可能导致不可信结果。由于LP求解器无记忆能力，通过分析历史数据并与当前计划对比，可获得额外洞察。为此，提出结合机器学习支持决策，尤其采用异常检测工具辅助LP输出。本文采用改进的ECOD方法，提出高维数据中选择最具信息量变量对的新策略，并结合两种二维异常检测算法，在MOL炼油调度与规划架构中成功发现多个业务优化机会及数据供应错误。
原文摘要 · Abstract (English)
Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting and applying the results remains challenging. Large petrochemical companies use massive models, with hundreds of thousands of input matrix elements. The LP solution is mathematically correct, but simplifications are made in the model, and data supply errors may occur. Therefore, further insight is needed to trust the results. The LP solver does not have a memory, so additional understanding could be gained by analyzing historical data and comparing it to the current plan. As such, machine learning approaches were suggested to support decision making based on the LP solution. Among these, Anomaly Detection tools are proposed to be used in tandem with the LP output. A transformed version of the popular ECOD methodology is applied. New methods are proposed to handle high-dimensional data: choosing the most informative pairs. Then, this is used alongside two 2D Anomaly Detection algorithms, revealing several business opportunities and data supply errors in the MOL refinery scheduling and planning architecture.
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