PROMIND provides a wide range of mathematical models used to materialise knowledge on plant dynamics into scientific models instead of “personal and non-transferable” empiric rules.
Correctly understanding how key processes work.
Objectively suggesting correct working parameters.
Comparing the real versus the intended status of the system, and suggesting adjustments to bring the functioning mode back to optimum conditions.
Experimenting with the system in unconventional functioning spaces, so as to suggest improvements that dramatically change the processing capabilities.
Technologies and key concepts:
Architecture orientated to services
Chemical company aimed to enhance productivity by reducing the resources needed to manufacture within quality standards. In order to do so, the company needed a profound understanding of the production process and the most significant variables at play that have an impact on the output. Solution: Promind Machine Learning goes live to detect the most significant variables that have an impact on quality standards and the process effectiveness. Along with mathematical model that reflects the process behavior; several utilities are to be developed to assist the machine operator on the regulation of the production line to reduce the consumption of resources as well as meeting quality standards.