6,412 research outputs found
Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019
A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands
of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector
that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the
potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent
modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the
main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the
time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing.
Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy
prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify
system and market effects effectively
Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Achieving real-time capability is an essential prerequisite for the
industrial implementation of nonlinear model predictive control (NMPC).
Data-driven model reduction offers a way to obtain low-order control models
from complex digital twins. In particular, data-driven approaches require
little expert knowledge of the particular process and its model, and provide
reduced models of a well-defined generic structure. Herein, we apply our
recently proposed data-driven reduction strategy based on Koopman theory
[Schulze et al. (2022), Comput. Chem. Eng.] to generate a low-order control
model of an air separation unit (ASU). The reduced Koopman model combines
autoencoders and linear latent dynamics and is constructed using machine
learning. Further, we present an NMPC implementation that uses derivative
computation tailored to the fixed block structure of reduced Koopman models.
Our reduction approach with tailored NMPC implementation enables real-time NMPC
of an ASU at an average CPU time decrease by 98 %
Complexity challenges in ATM
After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state
of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise
2020 NASA Technology Taxonomy
This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world
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