Data and Trend Analysis of Wastewater Treatment in Pulp and Paper Industry

Temporal reasoning is a very valuable tool to diagnose and control slow processes. For control system software it is a difficult problem to detect such patterns including trend extraction and similarity analysis. In this study, an intelligent trend index is developed from scaled measurements of a wastewater treatment plant. The scaling is based on monotonously increasing, nonlinear functions, which are generated with generalised norms and moments. The monotonous increase is ensured with constraint handling. Triangular episodes are classified with the trend index and the derivative of it. Severity of the situations is evaluated by a deviation index which takes into account the scaled values of the measurements. Modelling and simulation of biological wastewater treatment in pulp and paper industry requires hybrid models since the operating conditions can fluctuate drastically. A compact dynamic simulation is realized with linguistic equation (LE) models, which consist of two parts: interactions are handled with linear equations, and nonlinearities are taken into account by membership definitions. The same scaling approach is used in trend analysis and modelling. The LE based trend episodes and deviation indices provide good tools for detecting changes in operating conditions to be used in data and model selection and model adaptation.