将数据库与专家系统相耦合，可以使用该系统对电厂运行和性能进行预测；这种能力是对数据库和专家系统用于诊断支持和知识修复的传统用途的补充。通过使用回归分析或人工神经网络对数据结构建模，可以确定预期的化学剂量。对于输入很少的简单系统，回归和人工神经网络方法提供了类似的结果，回归分析在预测偏移方面更有效。基于剂量不足的风险，回归模型可以预测所需的化学剂量，以确保在所有情况下都有足够的化学剂量。在数据的另一种用途中，通过对特定水厂的记录进行简单的统计分析，可以使本文中描述的专家系统更加具体。 对于诊断组件，通过将描述符与数据范围联系起来，启发式/定性描述符可以与定量术语相关联。教程组件还可以基于类似的简单统计信息确定执行选项。本文讨论了耦合系统的发展，重点是回归分析和人工神经网络。

Coupling databases to expert systems allows the system to be used to make predictions about plant operations and performance; this capability is in addition to the traditional uses of databases and expert systems for diagnostic support and knowledge remediation. By using either regression analysis or artificial neural networks to model the data structure, expected chemical doses can be ascertained. For simple systems with few inputs, the regression and ANN approaches provide similar results, with the regression analysis more effective at predicting excursions. Based on a risk of underdosing, the regression model can predict the required chemical dose to virtually assure adequate chemical dosing under all circumstances. In another use of the data, the expert system described in this paper can be made more site specific by simple statistical analysis of a particular water plant's records. For the diagnosis component, heuristic/qualitative descriptors can be related to quantitative terms by linking the descriptors to data ranges. The tutorial component can also identify execution options based on similar simple statistics. This paper discusses the development of a coupled system, focusing on regression analysis and artificial neural networks.