SPOC-process modelling provides on-line quality control and predictive process control in particle and fibreboard production

Abstract

A number of production processes are characterised by having a large number of differing, but interdependent process sections and many complex influential factors. Despite great efforts in process control, fluctuations in the raw material properties often result in inconsistent qualities in the product because the raw material properties often cannot be measured online. Such quality inconsistencies are not detected during production and can only be determined on a random sample much later by laboratory testing. This paper attempts to show that modelling of such a production process and the prediction of quality properties result in reliable online quality control, model-based process optimisation and leads to model-based predictive process control (MPC) as a master control system. Using modern computing technology (SPOC Statistical Process Optimisation and Control) based on the relevant process parameters, statistical methods are able to precisely calculate online most of the quality properties. Optimisation methods calculate model-based production settings to meet quality optimally with respect to costs. Process adjustment using a model-based predictive feedback control is discussed. Highly promising results have been achieved, for example, in particleboard and fibreboard production processes. The results are transferable to other processes with similar characteristics (e.g., oil refinery or sugar production).