The Role of Big Data in Industrial (Bio)chemical Process Operations

Abstract

With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

“…Industry 4.0 and the smart manufacturing movement now provide biomanufacturing the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution [ 118 ]. Due to the promise of increased productivity and flexibility, there is significant interest from both managers and process engineers to transform their plants to smart manufacturing facilities.…”

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

“…Although newer generations of DCS' are more than capable of facilitating the operation of a Digital Twin, a separate node (dedicated server and information management system) may be needed for its execution. There is also a recent trend in biomanufacturing to migrate data to the cloud [13,15], although there are still ongoing internal debates about perceived cybersecurity threats. • Digital Model: the final and the most crucial part of an operational Digital Twin is the Digital Model of the plant, which can take information in real-time and runs concurrently with the real plant.…”

“…This flow of information allows the operators to act on the process based on the predictions made, i.e., an 'operatorenabled' Digital Twin is achievable. However, the availability of information-rich data in real-time is still somewhat limited in biomanufacturing, and in particular in fermentation operations [4,15].…”

Why Is Batch Processing Still Dominating the Biologics Landscape? Towards an Integrated Continuous Bioprocessing Alternative

“…In this case, the objective is to take the large amount of data collected and convert it into actionable information. Initially, the aim is to monitor a process with the intention of later using this information to change/control the process [65]. In batch production, these concepts have already been employed to predict batch end-time from seemingly information-poor variables, such as temperature and pH.…”

“…In batch production, these concepts have already been employed to predict batch end-time from seemingly information-poor variables, such as temperature and pH. Concepts such as predictive maintenance also leverage data to predict and schedule maintenance operations [65].…”