Process / Monitoring
Predicting Product Quality with Soft Sensors: Application, Opportunities and Challenges Overview of Most Popular Applications
Chemical plants are usually highly instrumented and have a large number of sensors that collect measured data for process control and monitoring. About two decades ago researchers began using the large amount of data to build predictive models, and these model were called soft sensors. Soft sensors are most often data-driven models based on data measured within the processing plants, providing real-time information necessary for effective product quality control.
The volume of information available has grown significantly over the last decade and has open a totally new area of using the data to help both science and operations. These days everybody is talking about “big data” concepts.
We have looked into some of the data mining tools available for free use and have come to a conclusion – there is really a lot available!!!
The necessity to operate industrial units at minimum operating costs and still meet all requirements of product quality and tight environmental laws requires continuous process monitoring, optimization and efficient quality control. Nowadays soft sensors are used as the supplement to online instrument measurements for process monitoring and control improvement. Given that the industrial applications must be simple and easy to use but still reliable, the objective of this article is to illustrate the application of the soft sensors (data-driven sensors) on examples of commercial plants.
Mathematical models and process data How to use process data to improve process monitoring and control
Application of mathematical models for the purpose of process monitoring is rising due to improved support of data historization and data handling. In most modern industries, process control systems are connected to systems that collect process data on a regular basis (e.g. 1 minute) and by using special data compression techniques, data can be stored for years and used for purposes of improved monitoring and control. Models developed for process monitoring enable faults recognitions, prediction of properties and improved quality and process control.