# Events

# SSD- Palazoglu Seminar

## Prof. Ahmet Palazoglu, Ph.D. - A new Design Paradigm to Address Demand Response Objectives in Process Systems

Department of Chemical Engineering, University of California, Davis, USA

### Abstract

With the continuing penetration of renewable sources into the power grid, the energy picture presented to the process industries has changed dramatically within the last 10 years. The most visible consequence is the ability to offer real-time electricity pricing by the grid operators as they manage a number of distributed power sources including renewables. If power is available directly from the renewables such as solar and wind, their intermittency challenges the operation of process systems as the available energy varies during the day. This leads to the use of hybrid systems where renewable sources are complemented with storage systems (batteries) and the process has the flexibility to draw energy from the grid or sell back to it when appropriate [1]. The variations on the supply side both in terms of price and availability result in a search for optimal allocation of loads (demand) during the day. Accordingly, demand response (DR) is defined as the ability of the operators to modify process conditions in real-time to take advantage of and respond to such variations and to formulate load shifting strategies. In this talk, I will summarize our ongoing work towards the goal of developing demand responsive process designs. Such designs are not only expected to accommodate variations in price and availability by modifying (scheduling) process steady-states [2] but also consider re-configuring the process flowsheet in real-time for a more holistic DR strategy [3]. The formulation of the design problem leads to a mixed integer nonlinear programming (MINLP) problem in which the objective function quantifies the capital and operating costs (CAPEX and OPEX) subject to recourse constraints that express scenario-dependent costs. Our recent studies include both deterministic and stochastic versions which present significant algorithmic challenges and these will be briefly discussed. The methodology will be illustrated by examples of process networks.

** **

[1] Wang, X., H. Teichgraber, A. Palazoglu N.H. El-Farra, “An Economic Receding Horizon Optimization Approach for Energy Management in the Chlor-Alkali Process with Hybrid Renewable Energy Generation,” *J. Process Control*, 24, 1318-1327 (2014).

[2] Tong, C., A. Palazoglu, N.H. El-Farra, X. Yan, “Energy Demand Management for Process Systems through Production Scheduling and Control,” *AIChE J*., 61(11), 3756–3769 (2015).

[3] Wang, X., N.H. El-Farra, A. Palazoglu, “Proactive Reconfiguration of Heat-Exchanger Super Networks,” *Ind. & Eng. Chemistry Research*, 54, 9178−9190 (2015).

# EU Regional School - Pingen Seminar

## Prof. Dr. Georg Pingen - Introduction to Topology Optimization for Fluids

Department of Engineering Union University, USA

## Abstract

# EU Regional School - Schöps Seminar-Part 2

## Prof. Dr. Sebastian Schöps - Isogeometric Analysis with Application in Electrical Engineering

Graduate School CE, Technische Universität Darmstadt

## Abstract

This lecture presents isogeometric analysis (IGA) in the context of electromagnetic simulations, [1]. IGA extends the set of polynomial basis functions, commonly employed by the classical Finite Element Method (FEM). While identical to FEM with Nedelec's basis functions in the lowest order case, it is generally based on B-spline and Non-Uniform Rational B-spline basis functions. The main benefit of this extension is the exact representation of the geometry in the language of computer aided design (CAD) tools. This simplifies the meshing as the computational mesh is implicitly created by the engineer using a CAD tool.

# EU Regional School - Schöps Seminar-Part 1

## Prof. Dr. Sebastian Schöps - Isogeometric Analysis with Application in Electrical Engineering

Graduate School CE, Technische Universität Darmstadt

## Abstract

This lecture presents isogeometric analysis (IGA) in the context of electromagnetic simulations, [1]. IGA extends the set of polynomial basis functions, commonly employed by the classical Finite Element Method (FEM). While identical to FEM with Nedelec's basis functions in the lowest order case, it is generally based on B-spline and Non-Uniform Rational B-spline basis functions. The main benefit of this extension is the exact representation of the geometry in the language of computer aided design (CAD) tools. This simplifies the meshing as the computational mesh is implicitly created by the engineer using a CAD tool.

# I³MS - Göttsche Seminar

## Dr. Malte Göttsche - Reconstructing Past Fissile Material Production: An Inverse Problem

AICES Graduate School, RWTH Aachen University

### Abstract

Uncertainties of current fissile material stockpiles usable in nuclear weapons are high. There is a significant research gap on methods to establish such inventories accurately. Closing this gap will be one requirement to enable nuclear disarmament. Inspectors will need to verify that no significant amounts of undeclared or unknown stocks exist. The most promising approach is to reconstruct the fissile material production history based on information available today. This inverse approach is called nuclear archaeology.

An analysis of provided records on past nuclear fuel cycle operations can be conducted using fuel cycle simulation tools. Such an approach should be complemented by integrating into the analysis information gained from measurements taken during inspections. Regarding plutonium production for example, neutron activation assessments of permanent reactor components in or near the core could be used to calculate the reactor’s neutron fluence, which is related to the amount of produced plutonium. Measurements of the volume/mass and isotopic concentration of radioactive waste can provide further information on reactor operations.

To make best use of the various sources of information, new models are needed that integrate all information into an overall fissile material assessment. Uncertainties must be determined adequately; the overall aim is to use the available information such that the uncertainty of the overall estimate is minimized.