This delay prevents real-time process control. Inline sensors calibrated to laboratory methods enhance responsiveness; however, calibrating every material or recipe can be time-consuming and impractical.
In automated systems, control loops adjust process parameters to maintain a target condition. Importantly, they do not need the absolute moisture value to do this. What they need is a measurement that is stable and repeatable. When a sensor provides a consistent signal, the controller can react reliably to whether the material is wetter or drier than expected. This makes controlling to relative values highly attractive, delivering effective automation without the complexity of calibration.
Why Moisture Measurement Enables Automation
Automated systems make continuous adjustments to maintain consistent material behaviour and dry‑matter content. Moisture can fluctuate with the season, storage and preprocessing environment. Manual determination of moisture can take hours, and adjustments to the process cannot be made in real-time, resulting in imprecise measurements. Inline sensors enable real-time correction. Calibrating the sensor to real (absolute) moisture values still takes time, and this can become burdensome in processes that use many different recipes.
Absolute vs Relative Values
Absolute moisture values represent the actual percentage of water in the material. Achieving this requires extensive sampling, laboratory testing, and careful calibration, and it is sensitive to changes in components and their proportions. It is valuable when a finished product must meet a precise specification and is primarily useful for quality control and record-keeping.
Relative values, in contrast, use the sensor output as a consistent indicator of change. It is not tied to a laboratory reference value and so does not require detailed calibration. For automated control, repeatability is what matters most. This is the sensor’s ability to reliably indicate whether the material is wetter or drier than its target.
Why Material Conditioning Does Not Require Absolute Moisture for Control?
When conditioning materials, the objective is to maintain consistent conditions, ensure predictable behaviour during processing, and achieve high-quality final products. In water addition processes, if incoming material is wetter than usual, the system reduces water addition; if it is drier, it increases it. Similarly, in drying processes, if the incoming material is wetter, it increases the drying time, and if it is drier, it reduces the drying time. A repeatable relative measurement provides the precise feedback a Control loop needs. The Controller uses this signal to make corrections, ensuring steady-state performance without the need for constant retuning and recalibration.
How Hydronix Sensors Facilitate Controlling to Relative Values
Hydronix digital microwave sensors provide the stable, repeatable measurement required for automated control. They are simple to set up and use, require minimal maintenance, and remain dependable even when material colour, particle size, surface moisture, or dust levels vary. Their long‑term signal stability reduces recalibration requirements, and they can be installed in mixers, conveyors, ducts, bins, and silos, making them easy to integrate throughout the production line.
Machine Learning: The Next Level of Control
Machine learning enhances relative value control by analysing historical process behaviour and predicting how values (and therefore moisture) will change under different conditions. ML‑enhanced systems can anticipate disturbances, adjust control parameters proactively, minimise manual intervention, and compensate automatically for raw‑material variability. This leads to more consistent production, better energy efficiency, and fewer process interruptions.
Conclusion
Absolute moisture measurement has its place, but automated control in production does not depend on it. Automation requires repeatable, real‑time insight. Relative measurement provides precisely that. Combined with the long-term stability of Hydronix sensors and the predictive power of machine learning, the future of production is more intelligent control, fewer manual corrections, and more time freed up for operators to focus on value-added work.
