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Reality-Connected Machine Simulation of Heavy Machinery

The RealSim project proposal aims to connect machine simulation to reality in the framework of heavy machinery, which is an interdisciplinary application. The results of this project are expected to optimize machine performance, accurately and reliably predict machine failure, and reduce overall development and operation costs, thus, serving the Europe’s priority of green transformation and the United Nation’s sustainable development goal 9. The RealSim project will close the gaps of: (1) Onboard estimations of force and pressure components of heavy machinery; (2) Estimating deformability, cylinder friction, leakage, and valve dynamics of heavy machinery using independent nonlinear Kalman filters; (3) Employing dependent nonlinear Kalman filters in state estimators of heavy machinery; (4) Tuning algorithms for the covariance matrix of plant noise in the state estimation of heavy machinery. 

I will consolidate and leverage my research career and position as a leading researcher in the field through integration in Assoc. Prof. Mohammad Poursina’s group at the Department of Engineering Sciences of the University of Agder (UiA), Norway. Here, I will acquire hands-on experience of working with actual mechatronic systems and develop efficient control algorithms. I will improve my knowledge of fluid power designing, control theory, instrumentation, and develop efficient coding skills. I will also develop entrepreneurial skills. The skills, knowledge, and international network that I will develop during this fellowship will put me in an excellent position to achieve my long-term goal of becoming a professor with my own independent research group at an esteemed university in EU. This fellowship will help me to prepare a strong ERC Starting Grant application in the future. It will also cater my additional goal of setting-up a research-related start-up in the future.

Algebraic Theory of Information Quantities

Information theory is a discipline at the intersection of statistics, engineering and computer science. As the study of information quantities, such as compression or communication capacities, information content or measures of statistical dependency, it is one of the theoretical underpinnings of data science.

Computational problems in information theory are highly non-linear. The goal of this project is to transfer state-of-the-art methods of computational non-linear algebra to information theory, to study the inherent algebraic complexity of information-theoretical problems and to provide tools for solving them in practice. The algebraic point of view has proven to be fruitful in seemingly unrelated areas, as witnessed by a surge of recent work in algebraic statistics, in particular on likelihood geometry. However, maximizing the likelihood function is the same as minimizing relative entropy — a specific information quantity. Hence, this project also aims at generalizing the techniques developed in likelihood geometry.

One focus is on the practical computation of information quantities using numerical and differential algebraic geometry. Such quantities are defined via non-linear optimization problems and we aim to pinpoint the algebraic complexity of these problems in instances of general interest, such as common information measures. The final objective is finding fundamental laws and limits of data science imposed by non-linear inequalities constraining the entropic region. These inequalities provide, by duality, universal bounds for many of the optimization problems studied in information theory.

WHeelchair Activity Monitoring project (WHAM)

Wheelchair users (WCU) are three to five times more inactive than the general population, leading to a high risk of developing preventable secondary conditions. Although physical activity (PA) guidelines have been specifically developed for WCU, we lack systematic approaches to implement them in practice, as well as valid algorithms to monitor PA change in WCU through sensor-based solutions. Accordingly, the overall scientific aim of the WHeelchair Activity Monitoring project (WHAM) is to advance PA research and practice in WCU by developing valid digital markers for monitoring of and providing feedback on PA, paving the way for implementation and intervention. I will accomplish this through combining the complementary research fields of 1) behavioural intervention studies focusing on increasing PA in WCU (core competence University of British Columbia, UBC; outgoing phase) and 2) sensor-based PA monitoring (core competence Norwegian University of Science and Technology, NTNU; returning phase – consolidated by non-academic placement at Maastricht Instruments, which develop research prototypes including PA monitors). WHAM’s scientific aim is achieved through three objectives: 1) Measure the effect of a pragmatic exercise intervention trial on self-reported physical activity levels and cardio-respiratory fitness, 2) Develop valid algorithms for tracking PA markers in WCU, and 3) Co-create a framework that outlines how PA can be conjointly monitored and provided feedback on in WCU. This fellowship will enable me to become established as an internationally recognized researcher in the field of “physical activity and disability”, while bringing together ongoing national disjointed research activities in this field. Altogether, my overall ambition is to contribute to reducing inequalities in daily-life participation among WCU compared to the rest of the population.

EOSC Beyond: advancing innovation and collaboration for research

EOSC Beyond overall objective is to advance Open Science and innovation in research in the context of the European Open Science Cloud (EOSC) by providing new EOSC Core capabilities allowing scientific applications to find, compose and access multiple Open Science resources and offer them as integrated capabilities to researchers. To do so, EOSC Beyond supports a new concept of EOSC: a federated and integrated network of Nodes operated at different levels, national, regional, international and thematic, to serve the specific scientific missions of their stakeholders. Further specific objectives of the project are to accelerate ‘time to product’ of new scientific applications with software adapters, enable Open Science with machine composability and dynamic deployment of shared resources, support innovation in EOSC with a testing and integration environment, and align the EOSC Core architecture and specifications to integrate with European dataspaces. The project extends the state of the art of the EOSC Core and adopts a co-design methodology, including requirements elicitation, software development and validation in collaboration with different use cases from EOSC national and regional initiatives (e-Infra CZ, Czechia, NFDI, Germany, and NI4OS, South East Europe region), thematic research infrastructures from Social Sciences and Humanities (CESSDA), Life Sciences (CNB-CSIC and Instruct-ERIC), Environmental Science (ENES and LifeWatch), and Health and Food (METROFood-RI). EOSC Beyond builds on the capacities of prospective EOSC Nodes and partners with multi-annual experience in developing solutions for large-scale federated digital infrastructures and aligns with the technical architecture and requirements of data spaces from different business sectors. Ultimately, EOSC Beyond supports Open Science in modern, data-intensive, and multidisciplinary research, facilitating resource discovery, access, and reuse across scientific communities, organisations, and countries.


Harmonization of Advanced Materials Ecosystems serving strategic Innovation Markets to pave the way to a Digital Materials & Product Passport

The overarching objective of DigiPass is to enhance the digital maturity of the European communities that develop materials and intermediate products. The project will develop recommendations and clear routes toward digitalized circular business models. The overarching key result of DigiPass is to create a sustainable platform which includes support for Digital Materials & Product Passport and for collaborative innovation-by-design processes in a circular economy served by advanced materials. A business model for operating such a platform completes the overarching objective.
DigiPass is harmonizing and synergizing collected materials data sources and digital infrastructures. This will enable interoperability of data exchange, and standardization of advanced materials knowledge representation at all maturity levels. Accelerating the design, development, and production of advanced, safe, and sustainable chemicals and materials, as they are necessary for innovative products, calls for a collaborative approach involving different stakeholders to support durability, repair and overhaul, reuse, and recyclability of products. DigiPass project impacts all materials development communities over the whole circular value chain. 


Enhanced X(cross)-disciplinary Community-driven Imaging Technologies for Earth and Environmental material research

The EXCITE² Network is revolutionizing Earth and environmental material science research by consolidating a critical mass of 18 research facilities in 12 European and associated partner countries into a unified infrastructure and providing transnational access to advanced imaging technologies. This enables scientists to decipher complex processes within Earth materials at scales from nanometres to hundreds of centimetres, gaining unparalleled insight into the chemical and physical processes that govern, among others, environmental toxicity and its impact on human health, critical metal extraction for renewable energies, and the utilisation of Earth materials as long-term storage for climate-altering gases. The consortium fosters knowledge-sharing and talent attraction, bolstering the problem-solving capacity of the European Research Area. Moreover, EXCITE² is driving interdisciplinary collaboration and cross-fertilisation across academic institutions, scientific disciplines, and industry, propelling Europe towards a sustainable future. The initiative offers dedicated training programmes to a new generation of researchers within the European open science landscape, paired with access to world-class imaging facilities and expertise for problem-solving research and innovation. EXCITE² is introducing innovative service developments, such as artificial intelligence and leading-edge imaging experiments, to increase the user's problem-solving capacity. In addition, the consortium is implementing customised project valorisation strategies to maximize the impact of research outcomes in academia, society, and the economy. Citizen science projects allow the public to participate directly within the EXCITE² Network, contributing to solving EU challenges. As such, EXCITE² is paving the way towards a sustainable future, driving scientific excellence, and creating positive societal impact.

Engineering carbon quantum dots for zinc ion hybrid capacitors

Zinc ion hybrid capacitors (ZIHCs) integrate two energy storage mechanisms of battery-type electrode and capacitive electrode, bearing the advantages in energy density, power density, and safety. Until now, the cycle retention rate of ZIHC's capacity reported is still not more than 80%, restricting its development and commercial application severely. Focusing on the issue, the project aims at overcoming the limitation of traditional electrode materials and re-defining the designing concept to construct Carbon Quantum Dots (C-QDs) for ZIHCs. Advanced methods will be used to harvest carbon quantum dots from coal tar pitch (a sort of industrial solid castoff). The bulk sp2-carbon network, innumerable edge sites, profuse surface functional groups, and regular size ensures that as-synthesis C-QDs have high electrical conductivity, sufficient ion transport channels and rich adsorption sites. Moreover, the new concept of dynamic evolution is introduced for insight into the structure and interface failure of ZIHC's electrode. The interface-reconstruction mechanism and the attenuation model of capacity will be established for the first time. Further, the methods to strengthen structures of C-QD electrode will be developed based on a trade-off strategy. The ultimate goal is to improve the energy storage efficiency of ZIHCs by 20% and to increase their cycle performance more than 95% unprecedentedly. This research will develop long-running high performance ZIHCs, for contributing European energy storage industry. The ER will achieve abundant research experience and scientific skills from the project and the capability to launch her own research group in future.

IMProving User experience, Long-term sustainability and Services of EU-OPENSCREEN

EU-OPENSCREEN (EU-OS), the distributed European Research Infrastructure for Chemical Biology and early drug discovery, provides world-class services in high-throughput compound screening and medicinal chemistry to users from academia and industry. As one of the largest open-access initiative globally, EU-OPENSCREEN plays an increasingly important role in facilitating early stage drug discovery in Europe. It recently integrated fragment-based drug discovery and chemoproteomics as two new services, but the increasing complexity of modern drug discovery projects requires an even more comprehensive portfolio and integration of emerging technologies. To ensure that EU-OS continues to offer a comprehensive suite of relevant services and support a growing user community to conduct forefront research, IMPULSE will develop, validate and integrate new services in AI/ML, new chemical modalities, and innovative disease-relevant cell models combined with CRISPR-Cas9/RNAi-based genetic screening. IMPULSE will work towards the implementation of common operational and data standards to increase data reproducibility and FAIRness. Bespoke outreach and training activities will raise awareness among key user groups of its new services and advance operational excellence and reproducibility in pre-clinical drug discovery. New models for pre-competitive drug discovery with industry or charities, and growing ERIC member community will reinforce the sustainability and accessibility of EU-OPENSCREEN.

Aqua Research Infrastructure Services for the health and protection of our unique, oceans, seas and freshwater ecosystems

AQUARIUS will provide a highly comprehensive suite of integrated research infrastructures appropriate to addressing significant challenges for the long-term sustainability of our unique oceans, seas and freshwater ecosystems. For the first time, diverse research infrastructures will be combined to facilitate the work of researchers and key stakeholders focused on challenges and opportunities for both marine and freshwater systems. An impressive range of 57 research infrastructure services will be made available to include research vessels, mobile marine observation platforms, aircraft,  drones, satellite, sensors, fixed freshwater and marine observatories and test sites, experimental facilities, and sophisticated data infrastructures. AQUARIUS will support the development phase of the EU Mission to Restore our Ocean and waters by 2030, the Sustainable Blue Economy Partnership, the European Green Deal, and international climate initiatives. It will also be an essential component in achieving the European Digital Twin of the Ocean and the UN Decade for Ocean Sciences. The needs of researchers will be met through a robust and transparent system of transnational access funding Calls, facilitated by centralised user-friendly access portal. The Call programme will be informed through stakeholder engagement and brokerage events. Projects to be selected for Access must convincingly integrate multiple infrastructures and contribute to the core policy objectives of Mission Ocean, that is, to protect and restore marine and freshwater ecosystems and biodiversity; to prevent and eliminate pollution of our oceans, seas and waters; and to ensure a sustainable, carbon-neutral and circular blue economy. A thematic and geographic focus will be the hallmark of the proposed transnational Calls, aligning with the Lighthouse Regions, that is, the Baltic and the North Sea Basins, Black Sea, Atlantic/Arctic, and Mediterranean Sea along with their associated rivers.

EOSC-ENTRUST: A European Network of TRUSTed research environments

The mission of EOSC-ENTRUST is to create a European network of trusted research environments for sensitive data and to drive European interoperability by joint development of a common blueprint for federated data access and analysis. 
Countries and institutions have made significant investments in secure and trusted data environments to meet the need for research and policy-driven analysis of sensitive datasets such as people's health and socio-economic status, the habitats of vulnerable species and geo-spatial locations of protected sites. There are now a large number of such secure environments in operation - linked to national centres, individual organisations or specific research communities. This fragmented landscape poses challenges for both users and providers: researchers are faced with a large number of different systems and access procedures; providers need to manage federated access across multiple, potentially incompatible, technology and governance frameworks.
EOSC-ENTRUST brings together providers of operational Trusted Research Environments from 15 European countries with a shared goal to implement, validate and promote their capabilities through a common European framework using shared standards and common legal, operational and technical language. This blueprint for interoperability is anchored in the EOSC Interoperability Framework spanning the four dimensions of Legal, Organisational, Technical and Semantic interoperability. EOSC-ENTRUST has identified four driver projects covering Genomics, Clinical trials, Social science, and public-private partnerships to benchmark capabilities, inform blueprint design and demonstrate secure  data analysis using federated workflows.
Targeted outreach activities will expand this open network with further providers and develop policy papers and guidelines for the full range of stakeholders to create a long-term operational TRE framework within the European Open Science Cloud.


Antimikrobiell resistens i et Én helse-perspektiv

Midlene fra BEDREHELSE-Bedre helse og livskvalitet fordelt på år

Midlene fra BEDREHELSE-Bedre helse og livskvalitet fordelt på fylker

Midlene fra BEDREHELSE-Bedre helse og livskvalitet fordelt på fagområde

Professor

The One Health concept acknowledges the interconnectedness of human, animal and environmental health. In order to study the interaction between humans, animals, and the environment and how this affects the spread of infectious agents and antimicrobial resistance (AMR), cross-sectoral and transdisciplinary cooperation is key. In CORNELIA, we have put together researchers from all three One Health pillars, including groups representing biological and technological sectors.
AMR is one of the greatest health threats of our time, challenging both human and veterinary medicine. The use of antimicrobial agents is the main driver of the development and spread of resistance. Identical AMR determinants and their corresponding genetic vehicles occur in bacteria isolated from humans, animals, and the environment. In CORNELIA we will study how the development and dissemination of AMR take place between bacteria that belong to humans, animals and the environment. Our research questions are based on the fact that wastewater, sludge and manure constitute a melting pot for interactions between such different bacteria. These interfaces are well suited for the selection and spread of new resistance determinants, which will be transferred to soil and water and potentially recycled into the food chains.  CORNELIA will assess the consequences this may have for public and animal health.
The consortium will develop a scientific basis and strategies for a potential national monitoring program for AMR in the environment, as a parallel to the existing surveillance programs in humans, animals, and food. We are also working with two start-up companies to test innovative solutions to reduce the amount of AMR that is released into the environment from treatment plants, using VEAS and OUS as our experimental sites.
The project is a collaboration between NMBU, UiT, FHI, NIBIO, NIVA, OUS, VEAS, Blueshift and Sustaintech.

The One Health perspective is based on an understanding that human, animal and ecosystem health are inextricably linked. As antimicrobial resistance (AMR) exists in and affects all sectors, a One Health approach is required to address the dynamic and cross-sectorial behavior of the current AMR-threat. Environmental compartments as water and soil are dynamic and complex compartments representing important interfaces for all One Health sectors but are yet the most neglected part of One Health-AMR initiatives. The CORNELIA project (Antimicrobial Resistance in One Health Interfaces) challenges the environmental knowledge gap, and addresses development and dissemination of AMR in soil and aquatic environments as they are important interfaces between humans and animals. We do this with an interdisciplinary and cross-sectorial approach, involving participation from all the three pillars of One Health and bringing the biology and technology sectors together in exploiting new technologies for data analysis and surveillance, development of diagnostic tools and establishment of mitigation measures. 
CORNELIA is organized into five scientific workpackages, and each workpackage is designed to fill in knowledge gaps described in previous research and risk assessments: 1) Occurrence of AMR in environmental interfaces, 2) New diagnostic tools and new technologies for diagnosis and metagenome and whole genome sequence data, 3) Persistence, transmission and expression of AMR in environmental interfaces, 4) Strategies for surveillance of AMR in the environment, 5) Innovative technologies for AMR mitigation and wastewater treatment.
The comprehensive CORNELIA team consists of scientific partners from NMBU, FHI, UiT, NIBIO, and NIVA, and two start-up companies within wastewater technology; Blueshift AS and Sustaintech AS. Access to relevant sampling and testing of technology are ensured through collaboration with OUS and VEAS.

Antimicrobial Resistance in One Health Interfaces

Dette prosjektet utvikler nye metoder for å optimalisere driften av komplekse storskala prosesser, for eksempel kjemiske anlegg, bioraffinaderier, energimanagement i bygninger og industrielle klynger med varmeintegrasjon.

Moderne metoder for å realisere optimal og bærekraftig drift av slike prosesser er basert på å bruke en digital tvilling (dvs. en matematisk representasjon av prosessen) i en numerisk optimaliseringsalgoritme som gjentatte ganger kjøres for å finne de beste prosessinnstillingene, for eksempel ventilåpninger, kompressorkraft eller oppvarming som gir den mest effektive og økonomiske driften.

En utfordring ved å anvende disse metodene er at det resulterende optimaliseringsproblemet må løses i sanntid slik at de optimale prosessinnstillingene kan implementeres i anlegget. Når usikre driftsforhold som energipriser eller råvarekvalitet, tas inn optimaliseringen, kan det resulterende optimaliseringsproblemet være for komplisert til å løse i sanntid, og beregningstiden blir for lang.

For å redusere beregningstiden utvikler vi nye algoritmer som er basert på å dele det store og komplekse optimaliseringsproblemet i mindre delproblemer som er enklere å løse, og vi modifiserer gjentatte ganger delproblemene, slik at løsningene av delproblemene gir løsningen av det originale problemet.

Måten de mindre delproblemene er satt opp på, gjør det mulig å løse bare noen få av disse delproblemene, og deretter få løsningen til det komplette settet ved å bruke effektive path-followings metoder som er basert på sensitiviteten av den optimale løsningen. Vi skal videreutvikle sensitivititetsbaserte metodene for en ny klasse av optimaliseringsproblemer som det ikke finnes sensitivitetsbaserte path-followingmetoder ennå.

The research in this project is focused on finding innovative strategies and developing new computational tools for fast optimization of large and complex dynamic systems with uncertain models and parameters. The project is embedded in a larger activity on optimization-based methods for achieving optimal operation in chemical plants in the Jäschke research group, and in the process systems engineering group at NTNU.

Optimization-based control of large systems requires solving large nonlinear programs (NLP) in real-time. When robust approaches are used to handle uncertainty, the problem can size increase dramatically (e.g Lucia et al, 2013). This large computational effort is an important reason for why many industry sectors are currently unwilling to use these methods. 

To address this issue, our first objective is to develop new and efficient algorithms for solving large-scale NLP using a decomposition approach (Marti et al. 2015). In decomposition a large NLP is broken down into several smaller NLPs that are solved separately. The innovative idea in this project is to avoid solving the NLP for the subproblems, and instead apply a recently developed path-following algorithm (Jäschke et al 2014, Kungurtsev & Jäschke 2017), to obtain efficient approximate solutions of the subproblems. This can lead to dramatic increases in efficiency for decomposition methods.

The second objective is to develop an efficient sensitivity-based path-following algorithm to track optimal
solutions of mathematical programs with complementarity constraints (MPCC). MPCC can be used to describe many interesting discrete and nonsmooth phenomena, such as phase appearance and disappearance or flow reversals in pipeline networks (Baumrucker et al. 2008). While efficient sensitivity-based fast NMPC approaches are available for standard NLP, there are none for MPCC. This project will bridge the gap by combining sensitivity concepts with special MPCC sequential quadratic programming approaches.

SensPATH
Sensitivity-based path-following for robust economic model predictive control

I dette prosjektet styrker vi Norsk-Kinesisk forsknings og industrisamarbeid for å øke digitalisering i offshore olje og gass industrien, med fokus på subsea produksjonssystemer. 
Det vitenskapelige målet er å utvikle smart subsea produksjonssystemer som opererer autonomt over flere år, uten menneskelig inngrep. Ved bruk maskinlæring og utvikling av nye digitale verktøy skal vi lage algoritmer for å diagnostiserer tilstanden til produskjonssystemet, og som automatisk justerer produksjonsparametere av systemet for å oppnå optimal produksjon og bruk av utstyret.

I det første året jobbet teamet hovedsakelig med den organiserte PhD utdanningen og med å lære relevante metoder. Dette resulterte i kunnskap om hvordan å anvende avanserte maskinlæringsmetoder for å realisere autonom drift og prognose av offshore produksjonssystemer.

Pga COVID situasjonen ble samarbeidet med våre kinesiske partnere basert på digitale samarbeidsverktøy og online seminarer.

This project is aiming to strengthen Chinese-Norwegian research and industrial collaborations towards digitalizing the offshore oil and gas production especially in subsea. The scientific objective is to develop cognitive subsea process systems that operate autonomously over several years. A special focus is placed on harnessing the power of machine learning and digitalization to improve the effectiveness and efficiency of system prognosis and diagnosis methods, and to use the developed digital twins in combination with the methods for fully autonomous process operations. In addition, we will study the cultural and societal impacts of digitalization on stake holders. 

In the Norwegian part of this project, we will focus on building and maintaing digital twin models for degrading systems, such that they give an adequate representation of the physical production system. We will further use these digital twin models for developing novel condition-based maintenance approaches that schedule maintenance when the system has reached a sufficiently degraded state. Finally, we will develop algorithms for integrating production decisions with maintennce decisions, such that the overall system is operated optimally. 

The methods will be tested in collaboration with the industry and with our Partners in China, who will contribute to this project with expertise in degradation modelling and Big Data algorithms. This project will also enhance the exchange of personell. 3 PhD students from NTNU will spend 4 months each in China, and 3 PhD students from China will spend 6 months each at NTNU. In addition, we will organize annual workshops in China and Norway to discuss and share research ideas. 

The research will be performed in close collaboration with the industrial partners in Norway and China, and we plan industrial placements in which the students can test their results in an industry setting.

AutoPRO: Digitalization for Autonomous Prognosis and Production Optimization in Offshore Production Systems

Dette prosjektet studerer strategier for optimalisering av dynamiske systemer der driftsparameterne varierer med tiden. Et eksempel på et slikt system kan være en kjemisk prosess som drives på en agil måte for å tilpasse seg energikostnadene. Et annet eksempel kan være bygninger som er koblet til et «smart-grid», der energiprisene varierer, og optimaliseringsproblemet består av å finne den mest økonomiske tiden å skru av eller på fleksible energibrukere som vaskemaskiner eller panelovner.
I dette prosjektet vil vi minske driftskostnader ved å justere pådrag som f.eks. oppvarming eller kjøling. For å finne den optimale løsningen må man, i tillegg til å evaluere det nåværende tidspunktet, også ta fremtiden i betraktning. Dette gjør vi ved hjelp av en modell (digital tvilling), som blir brukt i en optimaliseringsalgoritme som blir kjørt fortløpende for å finne de optimale verdier til prosessvariablene til enhver tid. Disse variablene blir sendt til et reguleringslag under, som justerer pådragene slik at prosessvariablene holdes på sine optimale verdiene. 
For å oppnå optimal drift, er det viktig at optimaliseringsproblemet blir løst rask nok, ellers blir driftsstrategien basert på utdatert informasjon. Dette prosjektet har resultert i nye raske og effektive algoritmer for å løse det storskala dynamiske optimaliseringsproblemet i sann-tid. De nye algoritmene er basert på en sensitivitetsbasert «path-following» metode som ble utviklet som del av dette prosjektet, og som gir raske approksimasjoner av den optimale løsningen. Algoritmene våre ble videreutviklet til å kunne håndtere også modellusikkerhet. Dette ble gjort innen et multi-stage modellprediktiv regulerings rammeverk, som tar hensyn til alle mulige framtidige realisasjoner av usikkerheten.

The research in this project is focused on finding strategies and developing tools for optimizing dynamic behavior and transients in complex systems. It is part of a larger activity on optimal operation methods in the process systems group at the Department of Chemical Engineering at NTNU.
In practice, most implementations of optimal operation are realized in a hierarchical control structure, where an upper layer does the online economic optimization on a slow time-scale, and passes the optimal setpoint trajectories to a fast lower layer which adapts the plant inputs to follow the trajectories.

The problem of designing a hierarchical control structure for economic optimization with intended transients is considered an industrially very important problem (Scattolini, 2009).
In control structure design, decisions are made on what variables to control in which layer (e.g. Skogestad, 2000).
While for steady state optimization there exist approaches for designing hierarchical control structures (Skogestad 2000), there are no systematic methods and guidelines for the more difficult case of  dynamic optimization, and there are very few theoretical results in this area. 
  
The first objective of this project is to combine the offline optimization paradigm of self-optimizing control with the paradigm of online optimization, to find a systematic method for designing a hierarchical control structure for optimizing dynamic performance of a chemical plant.

The second objective is to study a fast sensitivity-based path-following algorithm that is used in the optimization layer, and to establish a robust stability proof for a system which is optimized by this algorithm. In particular, we will add a corrector element to our previously developed predictor algorithm, study its convergence, and develop a stability proof based on input to state stability arguments.

This project is meant to fund 1 postdoc; if no suitable candidate is found, a PhD student will be employed instead.

0 projects

FRIPRO-Fri prosjektstøtte

SensPATH Sensitivity-based path-following for robust economic model predictive control

Awarded: NOK 8.1 mill.

Project Period: 2020-2024

Location: Trøndelag - Trööndelage

IKTPLUSS-IKT og digital innovasjon

AutoPRO: Digitalization for Autonomous Prognosis and Production Optimization in Offshore Production Systems

Awarded: NOK 10.0 mill.

Project Period: 2020-2025

Location: Trøndelag - Trööndelage

IKTPLUSS-IKT og digital innovasjon

Fast hierarchical economic optimization of dynamic systems

Awarded: NOK 3.5 mill.

Project Period: 2015-2019

Location: Trøndelag - Trööndelage