Information Technologies in Industrial Automation

The Aalto Factory of the Future is a facility for innovation, research and education and comprises a space shared by humans, robots and production stations. The facility serves as a platform for projects in the area of advanced information technologies applied to future production systems. It focuses on achieving revolutionary high flexibility by exploiting the architecture of modular autonomous intelligent production units. The enabling technologies for that include: Artificial Intelligence, Industry 4.0 architecture, collaborative robotics, Industrial Internet of Things, wireless communication (5G, Wifi6), edge/fog/cloud computing paradigms, virtual integration, digital twins, remote commissioning, operation and predictive maintenance, simulation, virtual and augmented reality, human-machine interaction.

Project duration: 2019-2022
Funded by: Finnish Nuclear Waste Management Fund (VYR)

The SEARCH project (“Safety and security assessment of overall I&C architectures”), which is a part of SAFIR2022 research program (The Finnish Research Programme on Nuclear Power Plant Safety 2019-2022, http://safir2022.vtt.fi/), aims to address the safety and security of instrumentation and control (I&C) systems of nuclear power plants (NPPs) with a focus on connections among different I&C systems and the overall I&C system architecture. This will be done by developing new methods and tools. Our research group participates in the work package of SEARCH called “Model-based system engineering and formal verification”. Together with VTT Research Centre of Finland Ltd., we will work on the application of formal verification by means of model checking in the nuclear I&C domain, continuing the research direction of the SAUNA project. Concrete research directions include, but are not limited to hardware issues in model checking, user-friendly model checking.

Key researchers: Igor Buzhinsky, Polina Ovsyannikova

Project duration: 2016-2018
Funded by: EIT RawMaterials KIC

Virtual Upscaling belongs to the first wave of upscaling projects run under the EIT RM’s Baltic CLC during 2016-2018. The practical goal of the project was to collect materials, products and process design related real life R&D use cases from the project partners and develop the means to speed up the design and testing processes using a virtual platform known as the Modelling Factory. In particular, computational tools, data exchange protocols and software requirements were evaluated and developed in order to see, what types of solutions would have practical value for industrial and academic users to help them to ascend the scale ladder of phenomena when taking their ideas from the laboratory to the field.

Project duration: 2016-2018
Funded by: Bussiness Finland

“Raising the level of automation and quality in plant engineering"

Background

  During the recent years we have seen how production has shifted from Europe to the cheaper labour countries, starting with basic manufacturing but expanded towards high value production. It has been argued that by keeping the level of automation high in production we can slow down the movement. Same is also true with the engineering activities. Many Finnish companies are leading brokers in global plant engineering projects for large investments. In order to keep this position we need to raise the level of automation and quality in our plant engineering.

Goals of the Project

Goals of the project are two fold. On the one hand we will improve the plant engineering process efficiency and quality and thus ensure Finnish competitiveness in global process industry investment projects. On the other hand we will create a new engineering marketplace for platform economy. This marketplace will be based on open source platform and interfaces based on international standards.

Improvement of efficiency and quality is done through:
Speeding up the engineering process with more automated model generation and model transformations and by enabling automatic path for design information and simulation models to the systems used in plant operation.
Increasing the quality of engineering with model-based verification, validation and testing
Establishing interoperability through key-technology standards
Validating the results using industrial applications.

Participating organisations:

• Companies: EQUA, Fennovoima, Fortum, Masinotek, Outotec, Prosys, PSK, Pöyry, Semantum
• Research organisations: VTT and Aalto University

Project duration: 2015-2018
Funded by: Finnish Nuclear Waste Management Fund (VYR)

The SAUNA project (“Integrated safety assessment and justification of nuclear power plant automation”) is a part of SAFIR2018 research program (The Finnish Research Programme on Nuclear Power Plant Safety 2015-2018, http://safir2018.vtt.fi/). The general aim of SAUNA is to develop integrated and multidisciplinary ways of assessing and demonstrating the safety of nuclear power plants (NPPs) and their systems. Our research group participated in the work package of this project called “Analysis methods and tools”. In partnership with VTT Research Centre of Finland Ltd., we worked on several problems related to formal verification of instrumentation and control (I&C) systems of NPPs by means of model checking. This involved making model checking more user-friendly, performing closed-loop model checking with automatically generated plant models, addressing hardware issues (such as failures and asynchrony) within model checking.

Key researchers: Igor Buzhinsky

Project duration: 2015-2016
Funded by: TEKES

A district heating grid consists of hot water pipes which uses the waste heat from a power plant and supply the waste heat energy to the consumers for heating. The consumers are always looking for cheaper solutions. In the energy market there is an ongoing trend to to use the local solar thermal and green energy resources to reduce the fossil fuel emission. But, it is always challenging to add local solar/thermal devices at consumer premises without disrupting the overall operation of the district heating grid.  Moreover, one important requirement is to propose solutions to incentivize customers to install their own local energy producing and storage devices. Studying these kind of system is complex due to its inherited distributed nature. In order to enable studying of grid behavior an auction based district heating system is proposed in [1][2].

In an auction based scenario, the consumers which produces energy using their own local energy resources are called ‘prosumers’. In this research these prosumers can take part in the auctions in a district heating grid at hourly epoch. The model of Kaskinen district is developed by VTT earlier is used for this work.  Two different forms of the offer function linear[1], fuzzy[2] were proposed. These offer functions, based on the next hour predictions provide a commitment value for different price options. Since, such price options are not available currently for this kind of work, the fuzzy concept in [2] avoid the limitation of the concrete pricing model from the market and safely use fuzzy concepts.

The results obtained in [1] and [2] provide satisfactory comparison between these two different cases with the baseline fixed price and no auctions. This work has been extended to make use of  longer predictions and offer commitments for more than 1 hour, using a bigger plant model. For the model of grid Apros[3] is used. Fortum and VTT has collaborated with Aalto and developed a larger district heating plant model using the Järvenpää case studies and data. The project is the part CLIC innovation, EFEU/ For, current and future work we use this model to provide concrete results using the finite horizon auctions, optimizing the operation of the grid in a better way. Furthermore, there is a possibility to use more complex offer function and pricing models.

Kashif Gulzar

More information (link to the official page of the project)
[1] K. Gulzar, S. Sierla, V. Vyatkin, N. Papakonstantinou, P. G. Flikkema and C. W. Yang, "An auction-based smart district heating grid," 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg, 2015, pp. 1-8.
[2] C. W. Yang, K. Gulzar, S. Sierla and V. Vyatkin, "Fuzzy Logic Based Prosumer Agent in a Modular Smart Grid Prosumer Architecture," Trustcom/BigDataSE/ISPA, 2015 IEEE, Helsinki, 2015, pp. 261-268.
[3] http://www.apros.fi/en/apros_in_brief_2
[4] http://clicinnovation.fi/activity/efeu/

Project duration: 2014-2016
Funded by: TEKES

The S-STEP programme of FIMECC is positioned in the crossroads of these two significant megatrends: 1) the growing importance of industrial service business, and 2) the remarkable emergence of industrial internet or cyber-physical systems. The combination of these trends holds significant advantages for Finnish industries and export. However, before these opportunities can be realised, the problems related to the combination of these opportunities, related technology issues and scientific problems needs to be solved. This is the core of the S-STEP -program and it has a clear mission: S-STEP creates the industrial internet technology that enables superior services for the Finnish industry.

Key researchers: Tommi Karhela, Gerardo Santillán

EU FP7 (2008-2011)

In the future European farmers will have to effectively manage information on and off their farms to improve economic viability and to reduce environmental impact. All three levels, in which agricultural activities need to be harmonized with economical and environmental constraints, require integrated ICT adoption: (i) improvement of farm efficiency; (ii) integration of public goods provided by farming into management strategies; (iii) relating to the environmental and cultural diversity of Europe’s agriculture by addressing the region-farm interaction. In addition, the communication between agriculture and other sectors needs improvement. Crop products for the value added chains must show their provenance through a transparent and certified management strategy and farmers receiving subsidies are requested to respect the environment through compliance of standards. To this end, an integration of information systems is needed to advise managers of formal instructions, recommended guidelines and implications resulting from different scenarios at the point of decision making during the crop cycle. This will help directly with making better decisions as the manager will be helped to be compliant at the point and time of decision making.

In FUTUREFARM the appropriate tools and technologies will be conceptually designed, prototypes developed and evaluated under practical conditions. Precision Farming as well as robotics are very data intensive and provide a wealth of information that helps to improve crop management and documentation. Based on these technologies a new Farm Information Management Systems (FMIS) will be developed. As most relevant farm data will be readily available in the proposed information system, or may be automatically integrated using standardised services and documentation in the form of instructions to operators, the certification of crop production process and cross compliance of standards can be generated more easily than with present systems.

EIT Digital project (2020-2021)

Collaborative Design-Build-Operate Ecosystem provides a fully digital option for traditional design process of industrial plants. Partners can join a cloud-based environment to work together when designing plants. When designing and building large industrial plants, the result gets easily inefficient due to for example oversized motors and frequency converters. If the industrial plant could be simulated as a whole system before implementation, the components could be selected properly. This leads to savings in costs. Collaborative Design-Build-Operate Ecosystem provides a cloud-based platform for this kind of collaboration and simulation between several partners. Various configurations could be easily simulated together with different simulators, and the optimum solution is found to make the plant efficient. This project includes identification, research, development and testing of new ways to implement future intelligent process plants.