A dynamic process model based on a base metal refinery including critical control layers has been developed. The critical control layers include sensors, actuators, regulatory controllers, alarm systems, safety interlocks and supervisory control. With the help of expert knowledge, a fault (abnormal event) library was incorporated into the dynamic model. Fault diagnosis methods are used to detect and identify abnormal process conditions. With the use of the dynamic process model, fault detection and identification methods can be more rigorously and accurately evaluated for hydrometallurgical industry use. An economic performance function is developed from expert knowledge. Once the fault diagnosis is complete, an economic impact analysis based on the fault diagnosis results is completed. A possible economic case for fault diagnosis is concluded from the results.