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The MONSOON project - MOdel-based coNtrol framework for Site-wide OptimizatiON of data-intensive processes - aims to establish data-driven methodology to support identification and exploitation of optimization potentials by applying model-based predictive controls so as to perform plant and site-wide optimization of production process. The ambition of MONSOON project is shared by 2 significant process industries from the sectors of aluminium and plastic.
"Process industries represent a significant share of European industry in terms of employment and turnover, but also in terms of energy, resources consumption and environmental impact. MONSOON vision is to provide such industries with a dependable, replicable and cost-effective methodology that helps them achieving significant improvements in the efficient use (and re-use) of raw resources and energy, easing effective use of cross-sectorial competences."
MONSOON dissemination material:
About the MONSOON project:
Start Date: 1st October 2016
Duration: 36 months
Budget: 5 million €
Coordinator: Claudio Pastrone, ISMB
The MONSOON project consortium
Tweets by @MONSOON_EU
The Technical Objectives (TO) of MONSOON project are:
MONSOON will define an effective data-driven, model based, multi-level distributed control methodology suitable to link with each other robust model based monitoring and control systems and coordinate systems existing at different layers. The increased amount of correlated information and knowledge will provide plant and site managers with increased awareness and control over the effective use of equipment as well as over the use of raw material and energy resources.
In order to achieve seamless, plant- and site-wide distributed control, MONSOON will develop a comprehensive integration methodology, suitable to connect all components in a common, dynamic ICT infrastructure supporting the monitoring of data-intensive flows and distributed control. The proposed methodology will be verified in the field by deploying dedicated resource-aware software connectors, suitable to integrate existing process industry systems.
Building upon its real-time and dependable integration infrastructure, MONSOON will employ semantic techniques to define, inter-link and share distributed models describing all the key aspects of multi-scale processes of interest. Defined models will be accessible by means of pragmatic open APIs, suitable to help factory systems developers to look-up existing relationships and entities.
MONSOON will integrate a library of scalable analytics functions suitable for application in the domains of interest. Such functions which will be executable either on stored off-line data or in real-time fashion based on data fed in real-time by the field, and will deliver new, refined metrics and information which can in turn be seamlessly fed in model based control loops. The novel data-driven techniques that will be applied either on historical off-line or in real time data are statistical functions, trend analysis functions, machine and deep learning algorithms.
MONSOON will include a core set of analytics and visualization tools to fuse data coming from disjoint plant levels. Such tools will detect complex patterns of manufacturing processes and provide useful information both for supporting short-term analysis (optimization, scheduling, monitoring of KPIs) and refinement of long-term manufacturing strategies (re-design, new processes, ramp-up).
In order to cope with the growing data complexity MONSOON will define and implement hybrid simulation infrastructure suitable to facilitate design, development, integration, deployment and testing of predictive control algorithms. The proposed modular framework will enable to generate data streams produced by virtualized entities as data generators based on models of the represented entities and historical data. The framework will employ plant-wide and site-wide structures of virtual entities by the selection, constraining and configuration of available formally described models.
MONSOON will integrate life-cycle (LC) management tools in a symmetric fashion e.g. enabling on the one hand LC tools to access data from enterprise resource planning (ERP) and manufacturing execution system (MES) system and on the other hand to feed LC targets and elaborated metrics back into the control infrastructure e.g. allowing definition and deployment of dedicated LC controls in the infrastructure itself.
The cross-sectorial MONSOON solution will be developed and evaluated in two industrial sites from the Aluminium and Plastics sectors to assess its acceptance and usability by its intended end-users and for its potential effectiveness and impact on resource optimization. In the Aluminium production industry, the selected scenario will be focused on predictive monitoring of a large potlines, where the increased amount of collected data from low-level control systems will be exploited for data-driven predictive control, leading to earlier detection of anomalies and identification of potential optimization gains. In the Plastics domain, the selected uses will focus on enriching existing injection molding equipment with additional data-intensive in-mold sensors, and on the integration of extracted results with information from higher levels of the SCADA pyramid, leading to a faster and more precise identification of potential production problems.
MONSOON proposed methodology for plant-wide monitoring and control
This section outlines the overall high-level conceptual MONSOON methodology for developing model based predictive function suitable to monitor and control data-intensive production in process industries. MONSOON follows a cognitive OODA cycle (Observe, Orient, Decide, Act).
As a pre-requisite, a scalable infrastructure for monitoring process must be in place, spawning one or more production plants or sites (observe phase). Data from the monitored data-intensive processes is continuously collected, stored, annotated with relevant meta-information suitable to keep relevant relationship among different data sets and made available within a development environment designed to support collaborative development of model based predictive functions, namely the MONSOON Data Lab. By using such component, “pain” situations can be detected by process experts analysing data (e.g. by benchmarking against standards or similar production processes) or just reported and enriched with detailed information by the production floor manager suspecting inefficiencies (orient phase). Once a “diagnosis” is made, a suitable control solution can be developed or selected among the set of controls belonging to the company’s control functions knowledge base. At this stage, multi-scale controls can be developed through different iterative cycles of development and evaluation, performed with different approaches depending on the required evaluation needs at hand (e.g. evaluating feasibility, performance, potential impact, etc.) using a hybrid mix of off-line and on-line processing techniques and predictive analytics. At this stage, typically also the business feasibility of the developed solution is evaluated, to verify whether expected gains are sufficient to justify investments e.g. in deploying new sensors or actuators to support the developed controls in the production environment.
Once a feasible and sustainable solution matching the needs is identified and pre-evaluated, a rapid prototyping and deployment stage occurs, resulting in the controls to be made operational within a dedicated production runtime integrated in the plant control infrastructure (act phase). At this stage, the control is able to interact with automation systems managing processes at different levels (i.e. PLCs, SCADA systems, MES, ERP, etc.) to monitor data and perform controls. At this stage, the available monitoring infrastructure can be used to perform evaluation of impact and/or life-cycle Key Performance Indicators (KPIs). Throughout the whole process, a set of dynamic, multi scale models suitable to describe both domain-specific and cross-sectorial phenomena in process industries are employed to ensure interoperability among all systems supporting the process. Such models are chosen among available standard models and/or proposed as standardization output by MONSOON, and allow semantic inter-linking of meta-information from devices, process, management and enterprise information from the production floor, as well as of data extracted by processing from the available collected data sets.
The collaborative companies with MONSOON project are Aluminium Pechiney and GLNPlast. Their plants will be used as pilot sites for the implementation of MONSOON project's techniques and methodologies.
Aluminium Pechiney Company is a world leader in aluminium production and the French subsidiary of Rio Tinto Aluminium. Aluminium Pechiney has worked for more than 40 years on the development of electrolysis process equipment, process control, manufacturing execution and advanced data analysis and has proposed its Dunkerque plant as an indicative use case as there is an intensive need for plant-wide monitoring within its aluminium production, carbon and potline process.
Based on the electrochemical reduction of aluminium oxide (alumina), a process invented at the end of the 19th century and radically improved since then, the Aluminium Dunkerque plant is directly employing 550 people and more than 400 indirectly. Most of the contractors, as well as 90% of employees are coming from the neighbouring community, which shows the commitment of the smelter to develop local economy and local skills.
The Dunkerque smelter is in fact the highest-producing primary aluminium smelter in the EU-28 area. Located in northern France, on the North-Sea coast close to the Belgian border, it is benefitting from the very dense transport and energy infrastructure of the Hauts-de-France Region. Its main clients are located in France, Germany and the Netherlands, mainly in the canstock and transportation industries.
Built in 1990, and started up in 1991, the Dunkerque plant is also the first aluminium factory in France with 65% of total national production and Europe’s largest sheet-aluminium producer and an important player in the ingot market as well as one of the most modern smelters, with a state of the art technology and equipment, insuring as well a minimised environmental impact.
The smelter is equipped with 264 AP39 electrolytic pots in one potline operating at 390 kA, for a total yearly production of about 280.000 tons of primary aluminium. As an energy intensive process, its 450 MW nameplate power input result in a power consumption of 3.7 TWh of electricity per year, equivalent to a 1-million people city consumption.
For the past GLN Plast dedicates its activity to the injection thermoplastic components for use in various and di-verse segments, such as the automotive, pharmaceutical, medical, electronic, cosmetic and food packaging.
GLN Plast specializes in the mass production of castings in an environment certified through ISO/TS 16949 and ISO 9001. It has nearly 40 plastic injection machines and is capable of testing molds in machines weighing be-tween 40 to 420 tons and can guarantee the production of pre-series for the approval of new products as well as the production of large, medium and small series. Hence, GLN Plast can produce final parts, ready to be distributed and that can also include small assemblies, packaging and laser engraving. It also offers a wide range of high added value services to deliver market-ready products and develops customized injection solutions to match the productivity and efficiency requirements of optimized productions. As such, the company has an integrated offer, capable of supplying mold and injection, susceptible to providing benefits in terms of knowledge enhancement and cross-selling opportunities.
The company is a member of a larger industrial group, named GLN Group, which incorporates three companies, all tied to the molding and plastic industry. Several decades of experience and constant technological innovation, establish it as a dynamic group of excellence that integrates the phases of Product Development and Engineering, the Development and Manufacture of high precision molds, and the injection of plastic parts, supported by complementary high added value services. This association provides GLN Plast with far reaching knowledge of the sector and production technologies, as well as the necessary financial muscle to provide it with the investment capacity to enable it to easily adjust to market oscillations and adapt to new technological developments. This corporate reality has also provided the opportunity for the creation of a shared R&D Centre – GLN Innov.
For GLN Plast, human resources development is a high priority. In this light, GLN Group has a GLN Academy – its own knowledge center, in order to sustainably assure a highly qualified workforce. It organizes and provides courses focused on capacitating increases in productivity and is based on a wider knowledge sharing and innovation based corporate culture. The GLN Academy is the first and only of its kind in Portugal, within the context of mold and plastic industries.
The MONSOON Consortium consists of 11 complementary partners from 7 different European Countries, namely Italy (Torino, Castellamonte), Germany (Munich, Ludenscheid), Greece (Thessaloniki), Slovakia (Kosice), France (Voreppe, Suresnes, Montbonnot Saint-Martin), Portugal (Maceira) and Spain (Madrid). All partners are combining knowledge to achieve project aims.
Here is a list of partners of the MONSOON project:
EXPECTED IMPACTS FOR THE MONSOON PROJECT
"Improve the environmental efficiency, by decreasing the emissions"
Head of Pervasive Technologies Research Area at ISMB and MONSOON PROJECT MANAGER
"Decrease of the resource consumption and the use of energy"
Research Assistant at Fraunhofer and MONSOON TECHNICAL MANAGER
"Increase the productivity and the efficiency in the considered industrial scenarios"
Chief Financial Officer at GLN and INDUSTRIAL PARTNER FOR PASTIC DOMAIN
List of submitted deliverables of MONSOON project:
D1.1 - Project Quality & Risk Management Plan
Leader: Fraunhofer FIT | 20 October 2016
The D1.1 Project Quality & Risk Management Plan is a practical management guide for members of the MONSOON consortium. It sets out the procedures for Quality Assurance activities in the project with special emphasis on Risk Management.
D2.1 - MONSOON platform usage scenarios
Leader: Fraunhofer FIT | 30 November 2016
The purpose of this deliverable is to document and describe a set of plausible usage scenarios in the year 2020 and beyond for the MONSOON platform in the two domains Aluminium and Plastics but should represent scenarios that are also common or at least applicable to other process industry sectors as well.
D2.2 - Initial Process Industry Domain Analysis and Use Cases
Leader: Aluminium Pechiney AP | 30 December 2016
The purpose of this deliverable is to document and describe the aluminium and plastic domain-specific and cross-sectorial use cases, defining how the MONSOON platform will be used for predictive optimization and scheduling tasks in production plants and sites.
D2.5 - Initial Requirements and Architecture Specifications
Leader: Fraunhofer FIT | 29 March 2017
This deliverable D2.5 Initial Requirements and Architecture Specifications describes the initial requirements, the first version of the architecture description of the MONSOON platform, including stakeholders which might be relevant for this project and the platform, as well as a scenario description for the use cases in the aluminium and plastics domain. There will be an update of all these topics in D2.6 Final Requirements and Architecture Specifications which is due in month 21.
D3.1 - Initial Real Time Communications Framework
Leader: Capgemini CAP | 30 December 2016
The document describes:
D3.4 - Initial Virtual Process Industries Resources Adaptation
Leader: Istituto Superiore Mario Boella ISMB | 31 January 2017
The D3.4 “Initial Virtual Process industries Resources Adaptation” collects the initial specification of the virtual process industries resources connector along with the description of the environment infrastructure, in both pilot sites, and the initial architecture and modules.
D4.3 - Initial Big Data Storage and Analytics Platform
Leader: Fraunhofer FIT | 31 March 2017
This document describes the distributed platform for Big Data Storage and Analytics that provides resources and functionalities for storage batch, and real-time processing of the big data. The platform combines and orchestrates existing technologies from Big Data and analytic landscape and sets a distributed and scalable run-time infrastructure for the data analytics methods developed in the project. The high-level architecture with its provided interfaces for cross-sectorial collaboration is presented. The solutions and technology options available for each logical component of the architecture are briefly explained.
D6.1 - Test and Integration Plan
This document describes the comprehensive test, integration and deployment strategy for the software components developed in MONSOON, and how these software modules, sub-systems and systems will be integrated and deployed into a common prototype platform.
D8.1 - Communication and Dissemination Strategy
Leader: Center for Research and Technology Hellas CERTH | 30 December 2016
This deliverable presents the Communication and Dissemination Strategy for the European Union (EU), under its Horizon 2020 Research and Innovation programme (H2020), Sustainable Process Industry through Resource and Energy Efficiency (SPIRE) “MOdel-based coNtrol framework for Site-wide OptimizatiON of data-intensive processes (MONSOON)”.
D8.2 - Project Website
This document constitutes the Deliverable D8.2 – MONSOON Project Website of the MONSOON project (Grant Agreement No.:723650), and presents the online dissemination channels of MONSOON as they were created through the first three months of the project.
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Claudio PastroneHead of Pervasive Technologies Research Area
Istituto Superiore Mario Boella
Via Pier Carlo Boggio 61, 10138
Tel: +39 011 2276612