Process operation performance optimality assessment and cause identification based on PCA-DCD

Making precise assessment on the optimality degree for process operation performance and giving correct operating instructions for non-optimality, contributes to the adjustment of production to keep the process stay at optimal states. Better operation performance helps the factory obtain more benefit. Traditional monitoring approaches determine if the process is working normally, but cannot tell if it is running well. Therefore, establishing effective operation performance optimality assessing mechanism is of great significance. Data driven method, Principal Component Analysis (PCA), deals with linearly related variables and extracts the main information from data. However, PCA lacks in interpretability. Process knowledge based technique, Dynamic Causality Diagram (DCD), intuitively reveals causal relations between variables. But the discretization in DCD decreases calculation precision. A novel approach to realize process operation performance assessment and corresponding cause identification for non-optimal performance is proposed in this article. The proposed method is finally utilized in gold hydrometallurgy process operation performance assessment to illustrate its effectiveness.