The Role of User Guidance in the Industrial Adoption of MDE Approach

Model-Driven Engineering (MDE) has emerged as an actively researched and established approach for next generation control application development. Technology transfer to the industry is a topical research problem. Since most professional factory process control engineers do not have computer science backgrounds, there is an urgent need for studies of the role of user guidance in the professional learning, and thus, of industrial adoption of MDE approaches. In this study professionals were invited to a hands-on assessment of the AUKOTON MDE approach for factory process control engineering. Qualitative empirical material was collected and analyzed to identify the role of user guidance in the context of other factors impacting industrial adoption. Challenges in adoption that could be solved by user guidance were identified with the theory of organizational knowledge creation (SECI) model

Requirement verification in simulation-based automation testing

The emergence of the Industrial Internet results in an increasing number of complicated temporal interdependencies between automation systems and the processes to be controlled. There is a need for verification methods that scale better than formal verification methods and which are more exact than testing. Simulation-based runtime verification is proposed as such a method, and an application of Metric temporal logic is presented as a contribution. The practical scalability of the proposed approach is validated against a production process designed by an industrial partner, resulting in the discovery of requirement violations.Comment: 4 pages, 2 figures. Added IEEE copyright notic

Sitting on a gold mine: the story of the process industry's automatic formation of a digital twin

The use of a software tool chain to generate Digital Twins (DTs) automatically can speed up digitization and lower development costs. Engineering documents and system data are just two examples of source information that can be used to generate a DT. After proposing a general plan for semi-automatic generation of a DT for a process system, this work describe our efforts to extract necessary information for the generation of a DT of a process system from existing information in a factory floor like piping and instrumentation diagrams (P&IDs). To extract initial raw model data, techniques such as image, pattern, and text recognition can be used, and then an intermediate graph model can be generated and modified based on requirements. In order to increase the system's adaptability and reliability, this research will delve deeper into the steps involved in creating and manipulating an intermediate graph model

Applying graph matching techniques to enhance reuse of plant design information

This article investigates how graph matching can be applied to process plant design data in order to support the reuse of previous designs. A literature review of existing graph matching algorithms is performed, and a group of algorithms is chosen for further testing. A use case from early phase plant design is presented. A methodology for addressing the use case is proposed, including graph simplification algorithms and node similarity measures, so that existing graph matching algorithms can be applied in the process plant domain. The proposed methodology is evaluated empirically on an industrial case consisting of design data from several pulp and paper plants

An Artificial Intelligence Framework for Bidding Optimization with Uncertainty inMultiple Frequency Reserve Markets

The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalises on frequency reserves of renewable energy. Frequency reserves are resources that adjust power production or consumption in real time to react to a power grid frequency deviation. Revenue generation motivates the availability of these resources for managing such deviations. However, limited research has been conducted on data-driven decisions and optimal bidding strategies for trading such capacities in multiple frequency reserves markets. We address this limitation by making the following research contributions. Firstly, a generalised model is designed based on an extensive study of critical characteristics of global frequency reserves markets. Secondly, three bidding strategies are proposed, based on this market model, to capitalise on price peaks in multi-stage markets. Two strategies are proposed for non-reschedulable loads, in which case the bidding strategy aims to select the market with the highest anticipated price, and the third bidding strategy focuses on rescheduling loads to hours on which highest reserve market prices are anticipated. The third research contribution is an Artificial Intelligence (AI) based bidding optimization framework that implements these three strategies, with novel uncertainty metrics that supplement data-driven price prediction. Finally, the framework is evaluated empirically using a case study of multiple frequency reserves markets in Finland. The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves

Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings

Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also beneficial for making buildings sustainably greener on account of using V2B technology in conjunction with local photovoltaic (PV) generation. It is assumed that there is no local battery storage other than EVs and that the EV batteries are fully available for driving, so that the EVs batteries must be at the intended state of charge at the departure time announced by the EV driver. Our goal is to exploit the potential of the EV batteries capacity as much as possible in order to permit a large area of solar panels, so that even on sunny days all PV power can be used to supply the building needs or the EV charging at the parking lot. A system architecture and collaboration protocols that account for uncertainties in EV behaviour are proposed. The proposed approach is proven in simulation covering one year period for three locations in different climatic regions of the US, resulting in the electricity bill reductions of 15.8%, 9.1% and 4.9% for California, New Jersey and Alaska, respectively. These results are compared to state-of-the-art research in combining V2B with PV or wind power generation. It is concluded that the achieved electricity bill reductions are superior to the state-of-the-art, because previous work is based on problem formulations that exploit only a part of the potential EV battery capacity. Document type: Articl

Ohjausjärjestelmien ohjelmistonkehityksen ongelmanratkaisustrategioiden soveltaminen uusiin standardeihin: case esimerkkeinä IEC 61499 ja ISOBUS

New standards in software development can significantly constrain teamwork, and practical success in applying a new standard in a pilot project is therefore more than a technical problem. This dissertation focuses on teamwork specifically from the aspect of problem solving. In two case examples in which the new standards IEC 61499 and ISOBUS were adopted, it is observed that preferred or familiar problem solving strategies can be unsuccessful when work is constrained by a new standard. Adaptations to problem solving strategies are required for successful control software development, yet these changes should permit a team to continue working in a way that has been successful in the past. A compromise is needed between familiar practices and new practices that would best support the use of the new standard. This dissertation studies the emergence of such practices in the context of two standards: IEC 61499 and ISOBUS. Case studies focus on projects, in which there was a requirement to adopt a new standard: either IEC 61499 or ISOBUS. It was presumed that the teams would make attempts to use problem solving strategies that would match the nature of the standard adoption problem. Such attempts were observed only in some situations; participants were inclined to use problem solving approaches that had been successful in the past, even though the nature of problem to be solved had changed due to the new standard. Hence, there are two disparate and possibly conflicting forces at work in a standard adoption project: firstly, there is an attempt to orient problem solving according to the new problem, and secondly there is a tendency to cling to familiar practices that have been successful in the past. Two theoretical frameworks, knowledge networking and technological frames, are used to analyze these two disparate aspects of standard adoption in a software development project. The frameworks are first applied separately and then some conclusions are drawn regarding how these analyses can complement each other to address the two aspects of standards adoption mentioned above. The results suggest how problem solving strategies can be assessed before a project, taking into consideration both the nature of the standard adoption problem and the capability and willingness of the team to follow a particular problem solving approach.Uusien standardien käyttö ohjelmistonkehitysprojektissa voi asettaa merkittäviä reunaehtoja projektin ryhmätyön ongelmanratkaisulle. Väitöskirjatyössä tutkittiin uusien IEC 61499 ja ISOBUS standardien käyttöönottoa ja havaittiin, että nämä voivat rajoittaa tuttujen ongelmanratkaisustrategioiden käyttöä siinä määrin, että standardeja pilotoiva projekti ei saavuta kaikkia tavoitteitaan. Strategioita muutettaessa tarvitaan kompromissi uuden standardin määrittelemään ongelmaan sopivien ja entuudestaan tuttujen ongelmanratkaisustrategioiden välillä. Väitöskirjatyössä tutkitaan tällaisten strategioiden syntymistä kahden standardin kontekstissa. Case tutkimukset on tehty projekteihin, joissa oli vaatimuksena ottaa käyttöön uusi standardi: joko IEC 61499 tai ISOBUS. Projektiryhmien oletettiin pyrkivän käyttämään ongelmanratkaisustrategioita, jotka soveltuisivat uuden standardin käyttöönotto-ongelmaan. Tällaisia yrityksiä havaittiin vain joissain tilanteissa, koska tekijöillä oli taipumusta käyttää entuudestaan tuttuja ja hyväksi havaittuja ongelmanratkaisustrategioita. Näin kävi siitä huolimatta, että ongelman luonne oli muuttunut uuden standardin käyttöönoton takia. Tutkimuksessa pyritään huomioimaan nämä kaksi ongelmanratkaisuun vaikuttavaa tekijää, jotka eivät ole yhteismitallisia: toisaalta esiintyy pyrkimystä orientioda projektin ongelmanratkaisua uuden ongelman mukaan, mutta toisaalta on taipumusta pitää kiinni tutuista toimintamalleista. Yllämainittuja kahta ongelmanratkaisun aspektia analysoidaan kahdella teoreettisella viitekehyksellä: tiedon verkottumisella ja teknologisilla kehyksillä. Näitä on sovellettu erikseen ja tämän perusteella on päädytty johtopäätöksiin kuinka kumpaakin viitekehystä soveltamalla voidaan vastata molempiin yllämainittuihin uuden standardin käyttöönottoprojektin ongelmanratkaisun aspekteihin. Johtopäätöksissä esitetään, miten tulokset voidaan ottaa huomioon uuden standardin käyttööottoprojektia suunniteltaessa.reviewe

An overview of machine learning applications for smart buildings

Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Publisher Copyright: © 2021 The AuthorsThe efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings’ adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation.Peer reviewe