Improved Understanding of Industrial Process Relationships Through Conditional Path Modelling With Process PLS

Understanding how different units of an industrial production plant are operationally related is key to improving quality and sustainability. Data science has proven indispensable in obtaining such understanding from vast amounts historical process data. Path modelling a valuable statistical tool obtain information Investigating relationships within affected by multiple conditions their interactions can however provide even deeper the plant’s daily operation. We therefore propose conditional path as approach improved understanding, demonstrated for milk protein powder plant. For this we studied between steps dependent on factors like line, seasons product range. show interaction be quantified interpreted context This analysis revealed augmented insight into that readily placed structure behavior. Such insights vital identify improve upon shortcomings current plant-wide monitoring control routines.