What are Soft Sensors.?
Soft sensors, also known as virtual sensors or inferential sensors, are advanced computational models that estimate process variables or measurements that are difficult or costly to measure directly in real-time. These sensors use data from easily accessible physical sensors and apply first principle’s models, machine learning algorithms, or hybrid models to predict the desired parameters. Soft sensors are commonly used in industries where direct measurement is challenging, such as in chemical processes, where they can estimate concentrations, reaction rates, or other critical variables.
Why Soft Sensors ?
Our expertise in Soft Sensor Development encompasses a comprehensive approach that combines first principles modeling, machine learning algorithms, and hybrid models. We excel in creating accurate and reliable soft sensors tailored to complex industrial processes. By leveraging the strengths of both physics-based models and data-driven techniques, our soft sensors provide precise real-time estimations of critical process variables. This integrated approach enhances process monitoring, control, and optimization, ensuring that our clients achieve superior operational performance and efficiency.
Capabilities...
At RPDS, our expertise in Soft Sensor Development encompasses a comprehensive approach that combines first principles modeling, machine learning algorithms, and hybrid models. We excel in creating accurate and reliable soft sensors tailored to complex industrial processes. By leveraging the strengths of both physics-based models and data-driven techniques, our soft sensors provide precise real-time estimations of critical process variables. This integrated approach enhances process monitoring, control, and optimization, ensuring that our clients achieve superior operational performance and efficiency.
First Principles Modeling
Develop soft sensors based on fundamental physical and chemical principles, providing accurate estimations grounded in the intrinsic behavior of the process.
Machine Learning Modeling
Create data-driven soft sensors using advanced machine learning algorithms, enabling the prediction of complex process variables from historical and real-time data.
Hybrid Modeling
Integrate first principles with machine learning techniques to build hybrid soft sensors that combine the strengths of both approaches, offering enhanced accuracy and adaptability for dynamic processes.