EasyOpt - Decision making tool for process optimization

DESCRIPTION

EasyOpt is a web based decision making tool to facilitate the use of data-driven models and improve key performance indicators (energy, resource usage, quality, production) in industrial processes by using machine learning algorithms.
In the use case creation procedure, a prior phase of feasibility analysis is necessary. This phase consists of creating process models based on historical data. The data-driven models will be part of the optimizer in the configuration that will be carried out after this phase is finished.
Optimization of industrial processes should be a task for industrial process experts. But, the complexity of the processes has required the help of mathematical techniques able to manage huge quantity of data that extract the intrinsic relations and keys that are hidden in those data.
Likewise, the complexity of these mathematical techniques has required the participation of data analytics engineers to help the industrial process engineers. They have worked together to disentangle the industrial process keys hidden in the data, but this complementary work is not always efficient. In the end, the ideal situation would require only industrial process experts, and so, the question to be faced would be: is there any approach to provide the industrial process experts with the necessary tools to optimize their process? The EasyOpt by Tecnalia is the answer to that question.
The main objective of this SW is to provide, to industrial staff, a friendly tool able to prescript optimal setpoints of process control parameters that will help them to make better decisions for the optimization of their industrial processes.