PRONTO

Research Focus

Data analytics to extract information from large, heterogeneous assemblies of data to determine machinery condition and process performance

Predictive tools and methods to improve system behaviour and operation decision support. Estimation of the current and future condition of the system enables corrective actions .

Optimization of operations, materials, energy and wastes taking machinery condition and process performance into account

New concepts for process operation identified as having high potential for impact by industrial partners

About

Start Date: 1st January 2016

Duration: 48 months

Budget: abt. 3.6 million €

Coordinator: Prof. Nina F. Thornhill, Imperial College, UK

13 partners from 6 countries

About PRONTO

This section of our website wants to provide you an overview of our project in terms of the motivations behind PRONTO project, the manufactoring sector addressed by our research, our scientific goals and the Industrial Training Network(ITN).

In the Introduction section the PRONTO Consortium explains why we are strongly convinced that for the European industry to stay competitive the overriding challenge is the efficient and sustainable operation of assets already installed and running at the present time.

The context section describes the largest and most dynamic industrial sector in the EU: the process industry.

The Aiming excellence section describes the training programme for our ESRs. We are strongly convinced this programme will have a high impact on their careers. Their early stage training with PRONTO will help them to become well-informed, confident, imaginative and creative. The ESRs will be able to see their results influencing the evolution and future development of Europe’s process industries and the consortium will take great care to disseminate results with this in mind.

The scientific goals of PRONTO cover topics from data analytics, to optimization and control. More about our research goals can be read in our section Goals.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 675215.