7 research outputs found
Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics
Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics
Optimizing the transient control of gas networks is a highly challenging
task. The corresponding model incorporates the combinatorial complexity of
determining the settings for the many active elements as well as the non-linear
and non-convex nature of the physical and technical principles of gas
transport. In this paper, we present the latest improvements of our ongoing
work to solve this problem for real-world, large-scale problem instances: By
adjusting our mixed-integer non-linear programming model regarding the gas
compression capabilities in the network, we reflect the technical limits of the
underlying units more accurately while maintaining a similar overall model
size. In addition, we introduce a new algorithmic approach that is based on
splitting the complexity of the problem by first finding assignments for
discrete variables and then determining the continuous variables as locally
optimal solution of the corresponding non-linear program. For the first task,
we design multiple different heuristics based on concepts for general
time-expanded optimization problems that find solutions by solving a sequence
of sub-problems defined on reduced time horizons. To demonstrate the
competitiveness of our approach, we test our algorithm on particularly
challenging historic demand scenarios. The results show that high-quality
solutions are obtained reliably within short solving times, making the
algorithm well-suited to be applied at the core of time-critical industrial
applications
A hybrid approach for high precision prediction of gas flows
10.1007/s12667-021-00466-4Energy System
Evaluation of Uncertainties in Linear-Optimizing Energy System Models - Compendium
Für die Energiesystemforschung sind Software-Modelle ein Kernelement zur Analyse von Szenarien. Das Forschungsprojekt UNSEEN hatte das Ziel eine bisher unerreichte Anzahl an modellbasierten Energieszenarien zu berechnen, um Unsicherheiten – vor allem unter Nutzung linear optimierender Energiesystem-Modelle - besser bewerten zu können. Hierfür wurden umfangreiche Parametervariationen auf Energieszenarien angewendet und das wesentliche methodische Hindernis in diesem Zusammenhang adressiert: die rechnerische Beherrschbarkeit der zu lösenden mathematischen Optimierungsprobleme. Im Vorläuferprojekt BEAM-ME wurde mit der Entwicklung und Anwendung des Open-Source-Lösers PIPS-IPM++ die Grundlage für den Einsatz von High-Performance-Computing (HPC) zur Lösung dieser Modelle gelegt. In UNSEEN war dieser Löser die zentrale Komponente eines Workflows, welcher zur Generierung, Lösung und multi-kriteriellen Bewertung von Energieszenarien auf dem Hochleistungscomputer JUWELS am Forschungszentrum Jülich implementiert wurde. Zur effizienten Generierung und Kommunikation von Modellinstanzen für Methoden der mathematischen Optimierung auf HPC wurde eine weitere Workflow-Komponente von der GAMS Software GmbH entwickelt: der Szenariogenerator. Bei der Weiterentwicklung von Lösungsalgorithmen für linear optimierende Energie-Systemmodelle standen gemischt-ganzzahlige Optimierungsprobleme im Fokus, welche für die Modellierung konkreter Infrastrukturen und Maßnahmen zur Umsetzung der Energiewende gelöst werden müssen. Die in diesem Zusammenhang stehenden Arbeiten zur Entwicklung von Algorithmen wurden von der Technischen Universität Berlin verantwortet. Bei Design und Implementierung dieser Methoden wurde sie vom Zuse Instituts Berlin unterstützt
Synergistic approach of multi-energy models for a European optimal energy system management tool
A randomized, double-blind, placebo-controlled study of latrepirdine in patients with mild to moderate huntington disease: HORIZON investigators of the huntington study group and european huntington's disease network
Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology
The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5–15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction