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Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology. This review paper examines the state-of-the-art in the transformation of raw data into actionable insight, specifically for water resource recovery facility (WRRF) operation. Sources of difficulties found along the way are pinpointed, while also exploring possible paths towards improving the value of collected data for all stakeholders, i.e., all personnel that have a stake in the good and efficient operation of a WRRF.

The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature ( T i ) exceeds the the collapse temperature ( T c ) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of T i and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.

Assuring compliance of intermediate and final quality attributes in a continuous pharmaceutical manufacturing campaign is of utmost importance. Application of corrective actions might be required in real-time. This work exemplifies the steps needed to identify a linear pulse transfer function for the dynamic behavior of the granule liquid-to-solid ratio (%w/w) at the end of the granulation unit of a commercial ConsiGma(TM)-25 production line. Near-infrared spectroscopy was used to monitor the granule composition in-line. The outcome for both the tracking and regulator problem using either conventional or model predictive control was implemented and evaluated. Dynamic setpoints were correctly followed and an RMSE of 0.25%w/w with respect to the setpoint was obtained when inducing artificial disturbances. Important practical challenges were also tackled. Examples are fouling, computational limitations, and the limited flexibility of the automation software. Applying the proposed advanced process control solution offers an answer to upstream material flow rate deviations. (c) 2018 American Institute of Chemical Engineers AIChE J, 64: 2500-2514, 2018

One major advantage of continuous pharmaceutical manufacturing over traditional batch manufacturing is the possibility of enhanced in-process control, reducing out-of-specification and waste material by appropriate discharge strategies. The decision on material discharge can be based on the measurement of active pharmaceutical ingredient (API) concentration at specific locations in the production line via process analytic technology (PAT), e.g. near-infrared (NIR) spectrometers. The implementation of the PAT instruments is associated with monetary investment and the long term operation requires techniques avoiding sensor drifts. Therefore, our paper proposes a soft sensor approach for predicting the API concentration from the feeder data. In addition, this information can be used to detect sensor drift, or serve as a replacement/supplement of specific PAT equipment. The paper presents the experimental determination of the residence time distribution of selected unit operations in three different continuous processing lines (hot melt extrusion, direct compaction, wet granulation). The mathematical models describing the soft sensor are developed and parameterized. Finally, the suggested soft sensor approach is validated on the three mentioned, different continuous processing lines, demonstrating its versatility.

The pharmaceutical industry is facing some major challenges in the 21st century. Most notably are the decrease in research and development productivity and the ever increasing competition because of patent expiration. This triggered the industry and its regulators to rethink the entire business. One particular measure is the aim for faster drug development using flexible manufacturing technologies based on fundamental scientific process understanding. Hence, the aim of this doctoral thesis is to investigate the opportunities of automatic process control in the continuous manufacturing of pharmaceutical tablets. It is illustrated how a combination of real-time chemical and physical product analyzers can be combined with mathematical models to monitor and predict the state of intermediate products in real-time. Moreover, these novel tools were applied in automated control strategies in order to ensure the required drug product quality at all times.