Control & Surveillance of Automated Production Steps (a part of the inSPIRe Food)

Summary of project: 
Automation of many manual operations in the food industry is difficult, because the criteria for process control are often based on tacit knowledge of the operator. Our hypothesis is that a route to optimal automation of such operations is to register how the trained process operator makes decisions from observations of the process and combining this knowledge with predictive modelling of input/output of the process units.