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  • Periodic Reporting for period 1 - PRONTO (PRONTO: PROcess NeTwork Optimization for efficient and sustainable operation of Europe’s process industries taking machinery condition and process performance into account.)
H2020

PRONTO Report Summary

Project ID: 675215
Funded under: H2020-EU.1.3.1.

Periodic Reporting for period 1 - PRONTO (PRONTO: PROcess NeTwork Optimization for efficient and sustainable operation of Europe’s process industries taking machinery condition and process performance into account.)

Reporting period: 2016-01-01 to 2017-12-31

Summary of the context and overall objectives of the project

The typical lifetime of an industrial process plant is 30 to 50 years. The PRONTO Consortium partners are strongly convinced that for Europe to stay competitive, the overriding challenge is the efficient and sustainable operation and maintenance of assets already installed and running at the present time. The overall objective of PRONTO is the integration of information about equipment condition into all aspects of process network operation, enabling decisions to be supported by the most up-to-date and relevant information.

Process plants generate complex information from disparate sources in the form of measurements from the process, mechanical and electrical sub-systems, and elsewhere. Production also involves flows of material and energy over an extended area through the distributed and interconnected equipment of the process network.

PRONTO is developing, demonstrating and implementing new concepts that consider the condition of the equipment to achieve the best possible operation of the installed assets of the process industries. This is based on real-time knowledge of the condition of interconnected equipment, merging information sources for decision support, and incorporating the monitoring of equipment performance into the optimization decisions.

To address these challenges, the research topics of PRONTO are:
• Data analytics for assessment of the condition and performance of networks of equipment used for production in the process industries;
• Optimization of use of resources in process networks taking account of real-time information about the condition and performance of the process equipment;
• Development of new concepts for process operation identified as having high potential for impact by industrial partners.

The consortium partners include leading universities and well-known companies with reputations for innovation. The consortium offers fourteen Early Stage Researchers (ESRs) training under the European Industrial Doctorate scheme, involving the non-academic sector extensively in joint supervision of the doctoral training with a strong emphasis on industrially-relevant PhD projects leading to practical demonstrations.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Measurements provide an important source of information about the condition of equipment. Modern automation systems treat the process, mechanical, and electrical hardware of a site as an integrated system. There is a need to merge information from these disparate sources. This development requires new algorithms and methods for reasoning with the system, finding patterns and drawing conclusions.

PRONTO has been developing methods for optimization of process networks taking account of such real-time information about the condition and performance of the process equipment. This is done by developing computationally efficient optimization algorithms for large-scale systems that are able to handle changes in the condition of equipment and the process. Practical case studies give opportunities to incorporate calibrated models of equipment into real-time mathematical optimization. These new concepts for process operation have high potential for impact by industrial partners with production sites.

Results so far have led to 35 papers published, accepted or submitted. One of the highlights is the publication of a PRONTO case study from a multiphase flow rig at Cranfield University that involves data from disparate sources [1]. A paper in the ESCAPE 2017 conference introduced a case study on a hydrogen network [2], while [3] considers degradation modelling for batch reactors. The model will be used in scheduling of production and maintenance. Paper [4] gives a new algorithm that underpins future work on including condition of equipment and the process. Work on online alarm flood identification [5] has been selected as a keynote talk at the 2018 ADCHEM conference. These papers are joint publications between PRONTO partners, and they highlight the collaborations arising as a result of H2020 funding for PRONTO.

PRONTO is a European Industrial Doctorate scheme. Six ESRs are recruited by industrial companies and eight by academic partners. The PRONTO training programme includes personalized PhD research projects with an industrial focus that are generating results such as those discussed above. The ESRs recruited by the academic partners are spending 50% of their fellowship on placements in the industrial sector.

PRONTO also provides scientific courses and training in transferable skills for all ESRs. The PRONTO courses held to date cover topics such as process safety, intellectual property rights, making applications for Microsoft Windows, and a visit to the BASF site at Ludwigshafen in Germany.

[1] Stief, A., Tan, R., Cao, Y., Ottewill, J.R., 2018, 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM 2018)
[2] Galan, A., De Prada, C., Gutierreza, G., Sarabia, D., González, R., Sola, M., Marmol, S., Pascua, C., 2017, European Symposium on Computer-Aided Process Engineering (ESCAPE-27), Barcelona, Spain, Oct 1-5, 2017
[3] Wu, O., Bouaswaig, A.E.F., Schneider, S.M., Leira, F.M., Imsland, L., and Roth, M., 2018, European Symposium on Computer-Aided Process Engineering 2018 (ESCAPE-28).
[4] Dalle Ave, G., Wang, X., Harjunkoski, I., Engell, S., European Symposium on Computer-Aided Process Engineering 2018 (ESCAPE-28)
[5] Lucke, M., Chioua, M., Grimholt, C., Hollender, M., Thornhill, N.F., 2018, 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM 2018)

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

PRONTO will have an impact on the next generation of production management systems.

As a result of PRONTO, it will be possible to:
• Merge data from a range of sources across different items of equipment in a process network;
• Generate and visualize information-rich statistics from the merged data concerning equipment condition and process performance;
• Incorporate calibrated models of equipment into real-time mathematical optimization to take account of the equipment condition and status;
• Use the outputs in operator decision support, maintenance planning, optimization and automated operation taking account of the condition and performance of the system.

PRONTO will have a long-term impact on the ESRs’ careers. The comprehensive training in an academic-industry environment, achieved through intersectoral secondments, will greatly enhance the future employability of the ESRs. ESRs are gaining first-hand experience of interdisciplinary and intersectoral research (underpinning studies, case studies, theory and method development), at a range of R&D organizations and companies.

The active collaboration between the academic and private sectors, which is inherent in this project, promotes a coherent understanding of commercial as well as technical requirements. It engenders an entrepreneurial and commercial mind-set with a focus on innovation. The involvement of industry allows PRONTO ESRs to engage in industrial application of their research and to support the development of new technologies, tools and methodologies which can be developed and commercialised. An example of an output is software that will be delivered in a state ready for commercial development. It is envisaged that this and further commercial applications will help to strengthen European innovation capacity, increase the competitiveness of European companies and contribute to the Europe 2020 strategy for smart, sustainable and inclusive growth.

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