Artificial intelligence applications for fault detection and diagnosis in pharmaceutical bioprocesses: a review
Mohammad Aghaee, Abhishek Mishra, Stéphane Krau, Ibrahim Melih Tamer, Hector Budman
Abstract
Because of increasing demand and strict regulations, pharmaceutical manufacturers encounter significant hurdles in achieving high productivity while ensuring normal process states. Variability in raw materials and operational disturbances can lead to deviations from normal operating conditions that result in decreased productivity. The implementation of smart fault detection and diagnosis (FDD) techniques is crucial for attaining acceptable productivity and ensuring process safety. In this review, we identify the major challenges of smart FDD in pharmaceutical processes, and we discuss future opportunities and new perspectives.
Topics & Concepts
ProductivityRisk analysis (engineering)Process (computing)Fault detection and isolationComputer scienceFault (geology)EngineeringBiochemical engineeringReliability engineeringBusinessArtificial intelligenceEconomicsBiologyActuatorPaleontologyMacroeconomicsOperating systemFault Detection and Control SystemsViral Infectious Diseases and Gene Expression in InsectsMineral Processing and Grinding