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The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

The high substrate variability and complexity of fermentation media derived from lignocellulosic feedstock affects the concentration profiles and the length of the fermentation. Failing to account for such variability raises operational and scheduling issues and affects the overall performance of these processes. In this work, a hybrid soft sensor was developed to monitor and forecast the evolution of cellulose‐to‐ethanol fermentation. The soft sensor consisted of two modules (a data‐driven model and a kinetic model) connected sequentially. The data‐driven module used a partial‐least‐squares model to estimate the current state of glucose from spectroscopic data. The kinetic model was recursively fitted to known concentrations of glucose to update the long‐horizon predictions of glucose, xylose, and ethanol. This combination of real‐time data update from an actual fermentation process to a high‐fidelity kinetic model constitutes the basis of the digital twin concept and allows for a better real‐time understanding of complex inhibition phenomena caused by different inhibitors commonly found in lignocellulosic feedstocks. The soft sensor was experimentally validated with three different cellulose‐to‐ethanol fermentations and the results suggested that this method is suitable for monitoring and forecasting fermentation when the measurements provide reasonably good estimates of the real state of the system. These results would allow the flexibility of the operation of cellulosic processes to be increased, and would permit the scheduling to be adapted to the inherent variability of such substrates © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

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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.