Production-Line Wide Dynamic Bayesian Network Model For Quality Management In Papermaking

T he quality parameters of paper are managed with rather independent decisions made by many process operators through the production line. Improving one quality parameter typically deteriorates another, and hence incoherent decisions tend to lead to suboptimal overall quality. Vast amount of laboratory measurements data support these operator decisions, yet how this information is utilized in practice, is not well known and appears to vary from production line to production line and operator to operator. We aim at coherent quality management of a paper production line through both optimizing the operator actions and scheduling the measurements of quality management optimally. We have chosen a Bayesian network formalism to integrate qualitative human knowledge and the measurement data about quality. We present an application with a Bayesian network as a model within stochastic dynamic programming. We demonstrate our modeling approach in a realistic case study, yet not in full-scale production-line wide quality management case.