In this study, an AM-based continuous processing reactor system was designed, manufactured, and assembled on a laboratory scale for the generation of pharmaceutical substances with an improved process control. The developed AM-based (additively manufactured) continuous pharmaceutical reactor system for the synthesis of metronidazole derivatives aimed to optimize both the physical and the chemical processes with time savings. Using AM, we were able to build reactor subcomponents with complex designs and precise dimensions, which facilitated the precise control of the reaction parameters and reduced the amount of chemicals required compared to macroscale reactors. The assembly of the whole reactor system consisted of main reactor bodies, mixers, valves, heat exchangers, electrical motors, and a microcontroller system. The assembled reactor system revealed a continuous flow of reagents and ensured uniform mixing and reaction conditions, thereby increasing the process efficiency and product quality. Five metronidazole derivatives were synthesized via two continuous processes, involving metronidazole reduction and its subsequent reactions with terephthalic aldehyde and anthracen-9(10H)-one to form Schiff bases. The optimal conditions were determined as follows: compound A (72% yield, 120 min, 55 °C), compounds B and C (63% and 68% yield, respectively, 8 h, 65 °C), and compounds D and E (74% and 85% yield, respectively, 8 h, 45 °C).

The deployment of autonomous predictive vibration-based diagnostics in Edge-IIoT requires balancing the cost of Micro-Electro-Mechanical System (MEMS) accelerometers against their metrological limitations. This study proposes a parametric Sensor Digital Twin (SDT) framework for virtual benchmarking of measurement chains at the hardware-software co-design stage. The SDT model emulates mechanical and electrical filtering, aliasing, noise, and quantization; its fidelity was validated experimentally using a physical ADXL345 sensor, with an RMS noise error of 8.56%. Virtual profiles of the commercial ADXL345 and ADXL357 sensors were generated using the reference NLN-EMP centrifugal pump dataset acquired with a Wilcoxon 786B-10 piezoelectric accelerometer. Their diagnostic performance was evaluated using eight diagnostic features and five heterogeneous machine-learning algorithms under interpolation, forward extrapolation, and backward extrapolation scenarios across fault severity levels. In the interpolation scenario, the ADXL345 and ADXL357 profiles achieved Macro F1-scores of 0.8999 and 0.8947, respectively, compared with 0.9567 for the reference measurement chain. In the forward extrapolation scenario, the ADXL357 profile achieved a Macro F1-score of 0.7171, compared with 0.6768 for the reference measurement chain. The results confirm that the SDT can support sensor hardware selection through virtual benchmarking, while the considered MEMS accelerometers provide comparable diagnostic performance in the evaluated scenarios when a representative training dataset is available.