Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - MONSOON (MOdel based coNtrol framework for Site-wide OptmizatiON of data-intensive processes)

Teaser

Process industries are characterized by intense use of raw resources and energy, thus providing a context where even small optimizations can lead to savings both in terms of economic and environmental costs. This is especially true for specific industrial processes such as...

Summary

Process industries are characterized by intense use of raw resources and energy, thus providing a context where even small optimizations can lead to savings both in terms of economic and environmental costs. This is especially true for specific industrial processes such as aluminium smelting or injection moulding, characterized by production in high volumes divided among distributed production units, across several lines, plants or even sites.
Predictive modelling techniques can be especially effective in optimizing processes in such context, but their application is not straightforward for several reasons including e.g., the high cost of integrating new sensors or actuators into legacy production, difficulties in monitoring physical parameters in harsh conditions, interoperability issues, difficulties in application fusing and correlating information collected at different SCADA levels, challenges in defining KPIs, etc. As a consequence, the deployment of model-based predictive functions in such production environment at a sustainable cost or with sufficient reliability is not always feasible, resulting in optimization potentials remaining untapped.
In past markets characterized by lower international competition, stable demand, relatively low labour cost and high abundance of raw materials, industry was able to remain viable just through progressive improvements in production technology, organization and logistics. The change in global competition and resources availability calls instead for a drastic re-invention and re-design of production processes and sites. Enabling benefits by integrating innovations in the installed process base is a fundamental step to help process industries transitioning from the current model oriented to the production of goods by consuming resources, to newer “circular” models. In this perspective, resource, cost and environmental sustainability is considered, monitored and optimized at all times, resulting in benefits for industries and society as a whole.
MONSOON project aims at establishing data-driven methodology and tools to support identification and exploitation of optimization potentials through model based predictive controls. The data lab enables multidisciplinary teams to jointly model, develop, simulate, verify, deploy and evaluate distributed predictions and controls. This will help plants in meeting their optimization.

Work performed

This first period in the project was shaped by the work on project initiation, requirements engineering and specification, prototypes development and pilot definition.
Specific effort was devoted to the definition of the main vision and related context scenarios as well as an initial set of requirements, for both the aluminium and plastic domains. To this aim, user workshops and interviews with end users have been realized to provide a comprehensive description of main features being offered by MONSOON platform to support the envisioned data-driven methodology, the identification and exploitation of optimization potentials in sustainable and process industries.
A work with an Extended Stakeholder Group (ESG) - experts with strong skills in one or more technology fields related to the MONSOON platform - was initiated.
In the first months of the project (ramp up phase), the efforts were aimed to put in place a minimal ICT/IoT infrastructure to enable continuous data collection from the production floor in the aluminium and plastic domains and storage into a scalable, cloud-oriented platform.
In parallel, starting from the conceptual architecture (please, refer to the picture below), reference architecture was designed based on scenarios and user interviews. The resulting architecture has led the development of MONSOON components and the deployment of prototypes in the both aluminium and plastic sites.
The defined architecture is built upon harmonized site-wide dynamic models and upon the concept of cross-sectorial data lab, a collaborative environment where high amounts of data from multiple sites are collected and processed in a scalable way. The data lab enables multidisciplinary collaboration of experts allowing teams to jointly model, develop and evaluate distributed controls in rapid and cost-effective way, create predictive functions with the help of machine learning algorithms, make simulations, possibly using online data from the connected production environment.
MONSOON framework is organized in two macro-components: the “Real Time Plant Operation Platform” and the “Cross Sectorial Data Lab”.
A first release of the Real-time Plant Operations Platform has been delivered. It is currently supporting few functionalities already deployed in aluminium and plastic test sites.
A first version of the Cross Sectorial Data Lab platform has been also developed along with its main components: a Big Data Storage and Analytics Platform, the Development Tools, the Semantic Framework and the Life Cycle Management Plugin.

Final results

MONSOON will combine known best practices methodologies for model based site-wide control and integrate them within the collaborative Data Lab methodology. Advances lie in the use of the data-driven methodology, exploiting since the beginning of the project real data from field installation.
MONSOON is addressing progress beyond state of the art in the following topics: methodologies for the multi scale modelling , techniques for early malfunctioning detection, integration of heterogeneous systems for model based plant-site monitoring and optimization, data analysis techniques, multi-Scale Deep Learning, methodologies for Life Cycle Management, multi-level Analytics and Multi-Modal Visualization.
MONSOON will then provide the following major outcomes.
** Multi-scale control methodology - Data-driven methodology (based on machine and deep learning algorithms) to perform large-scale data analysis for predictive control.
** Real-time Plant Operations Platform - SW framework allowing to collect data and interact with process industry systems, to implement predictive control and life cycle management.
** Cross-sectorial Data Lab - Distributed big data storage and data analytics platform.
** Semantic framework - Proposal for the standard to formalize data analysis process for predictive control and maintenance and simplify communication between experts and data scientists.
** Analytics and visualization tools - Fuse data coming from disjoint plant levels to detect complex patterns of manufacturing processes and provide useful information.
** Integrated LC Management Tools - Integration of LC management tools to access data from ERP and MES system and to feed LC targets and elaborated metrics back into the control infrastructure.
Based on the technical results, MONSOON expected impact directly relates to the wide / comprehensive / sustainable growth of the European productivity ecosystem. The change in global competition is actually driving a drastic re-design of production processes and sites. In this scenario, having the possibility to reach the same results with “few” ICT-driven investments represents an enormous potential, enabling immediate benefits for the company involved, as well as indirect positive impacts on the European production tissue and on the wider society.

Website & more info

More info: https://www.spire2030.eu/monsoon.