[comp] Kemian tekniikan korkeakoulu / CHEM

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    International Process Metallurgy Symposium in Honour of Professor Pekka Taskinen - Metallurgy as a tool for challenges in circular economy
    (Aalto University, 2017) Avarmaa, K. (editor); Jokilaakso, A. (editor); Kemian tekniikan ja metallurgian laitos; Department of Chemical and Metallurgical Engineering; Research Group of Metallurgical Thermodynamics and Modelling; Kemian tekniikan korkeakoulu; School of Chemical Technology
    This International Process Metallurgy Symposium in Honour of Professor Pekka Taskinen was already the second honorary symposia in Aalto University School of Chemical Engineering, Department of Chemical and Metallurgical Engineering, so it was already a tradition. It truly was International Symposium having presenters from more than ten nationalities. The title of this Symposium includes a sub-title of “Metallurgy as a tool for challenges in circular economy”, which emphasizes the important role metallurgy and metallurgists have in solving the resource deficit in metals and minerals. We have already seen the shift from “traditional” raw material research towards recycled materials and their impact on metallurgical processes. Another emerging or growing trend is the need and request for zero waste processes. All this underlines the importance of fundamental metallurgical research, knowledge and education.
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    Giant Magnetostrictive Materials
    (Elsevier BV, 2016) Aaltio, I.; Sozinov, A.; Ge, Y.; Ullakko, K.; Lindroos, V.K.; Hannula, S.-P.; Materiaalitekniikan laitos; Department of Materials Science and Engineering; Kemian tekniikan korkeakoulu; School of Chemical Technology
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    Using design thinking in engaging different stakeholders in development of higher education, decision making processes and policy making
    (Food and Agriculture Organization of the United Nations, 2015) Bakic, Irena; Husgafvel, Roope; Martikka, Mikko; Puunjalostustekniikan laitos; Department of Forest Products Technology; Clean Technologies; Puhtaat teknologiat; Kemian tekniikan korkeakoulu; School of Chemical Technology
    This paper investigates the suitability of design thinking approach in engaging different stakeholders in development of higher education, decision-making processes and policy making to support the ongoing efforts of both policy and decision makers and higher education institutions in improving governance and capacity building in the natural resources and forest sectors. Case studies from Mozambique, Uruguay , South Sudan and UNICEF Nicaragua illustrate how design-thinking methods can and could be applied in policy and decision making and further development of higher education. In general, design thinking requires a human-centered approach and participation. In human-driven policy making, policy makers are deliberately seeking a deeper understanding of the context and the lives of those the policy will affect and apprize how the policy should be designed. Well informed decision- making and design of relevant higher education can significantly benefit from this kind of approach as well. Comprehensive stakeholder involvement in planning of tuition and environment is important in building a suitable education program and unit to serve the purpose sustainably. Defining key areas for educational needs as well as the capacity needed to provide it are equally important in raising awareness both in academia as well as on the local level where theoretical knowledge can be brought to practice.
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    A dynamic prognosis algorithm in distributed fault tolerant model predictive control
    (Institute of Electrical & Electronics Engineers (IEEE), 2014) Zakharov, Alexey; Yu, Miao; Jamsa-Jounela, Sirkka-Liisa; Biotekniikan ja kemian tekniikan laitos; Department of Biotechnology and Chemical Technology; Research Group of Process Control and Automation; Kemian tekniikan korkeakoulu; School of Chemical Technology
    This paper presents a dynamic prognosis algorithm in distributed fault tolerant model predictive control (DFTMPC). The dynamic prognosis, which means predicting the trajectories of process variables under distributed model predictive control, is performed when a fault is diagnosed and several candidate reconfigured controls are proposed. Then, the dynamic prognosis is utilized to check whether the candidate reconfigured controls are able to drive the system to the new operating conditions and to evaluate the performance during the transition period. Thus, the most suitable candidate reconfigured controller is selected and its feasibility is ensured without using a Lyapunov function that is difficult to obtain for large-scale systems. On the other hand, the on-line computation burden of the prognosis algorithm is moderate under the assumption that the sets of active constraints in non-faulty subsystems remain the same as they are at the nominal operating conditions. Thus, the dynamic prognosis for DMPC is aimed to improve the applicability of the existing fault tolerant methods to large-scale systems.
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    Discrete-time adaptive learning control for parametric uncertainties with unknown periods
    (Institute of Electrical & Electronics Engineers (IEEE), 2013) Miao Yu; Deqing Huang; Biotekniikan ja kemian tekniikan laitos; Department of Biotechnology and Chemical Technology; Research Group of Process Control and Automation; Kemian tekniikan korkeakoulu; School of Chemical Technology
    In this paper, we approach the problem of unknown periods for a class of discrete-time parametric nonlinear systems with nonlinearities which do not necessarily satisfy the sector-bounded condition. The unknown periods hide in the parametric uncertainties, which is difficult to estimate. By incorporating a logic-based switching mechanism, we estimate the period and bound of unknown parameter simultaneously under Lyapunov-based analysis. Rigorous proof is given to demonstrate that a finite number of switchings can guarantee the asymptotic regulation of the nonlinear system considered. The simulation result also shows the efficacy of the proposed switching periodic adaptive control method.