196 citations · 723 across the 27 of their papers we have counts for
14 papers · 1 filter
Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response
Sonja H. M. Germscheid, Benedikt Nilges, Niklas von der Assen +2
This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity gen…
Optimal Design and Flexible Operation of a Fully Electrified Biodiesel Production Process
Mohammad El Wajeh, Adel Mhamdi, Alexander Mitsos
The flexible operation of electrified chemical processes, powered by renewable electricity, offers economic and ecological incentives, paving the way for a more sustainable chemica…
Cost-Optimal Power-to-Methanol: Flexible Operation or Intermediate Storage?
Simone Mucci, Alexander Mitsos, Dominik Bongartz
The synthesis of methanol from captured carbon dioxide and green hydrogen could be a promising replacement for the current fossil-based production. The major energy input and cost…
Optimization with Trained Machine Learning Models Embedded
Artur M. Schweidtmann, Dominik Bongartz, Alexander Mitsos
Trained ML models are commonly embedded in optimization problems. In many cases, this leads to large-scale NLPs that are difficult to solve to global optimality. While ML models fr…
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
Eike Cramer, Leonard Paeleke, Alexander Mitsos +1
We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing fl…
Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep Learning
Jan C. Schulze, Danimir T. Doncevic, Alexander Mitsos
We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical systems. While…
COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization
Marco Langiu, David Yang Shu, Florian Joseph Baader +7
Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling fr…
Optimal operating policies for organic Rankine cycles for waste heat recovery under transient conditions
Yannic Vaupel, Wolfgang R. Huster, Adel Mhamdi +1
Waste heat recovery for trucks via organic Rankine cycle is a promising technology to reduce fuel consumption and emissions. As the vehicles are operated in street traffic, the hea…
Adaptive Learning of Hybrid Models for Nonlinear Model Predictive Control of Distillation Columns
Jannik T. Lüthje, Jan C. Schulze, Adrian Caspari +3
Nonlinear model predictive control (NMPC) requires accurate and computationally efficient plant models. Our previous work has shown that the classical compartmentalization model re…
Obey validity limits of data-driven models
Artur M Schweidtmann, Jana M Weber, Christian Wende +2
Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in m…
Global Optimization of Gaussian processes
Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe +4
Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subs…
Global Deterministic Optimization with Artificial Neural Networks Embedded
Artur M Schweidtmann, Alexander Mitsos
Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for determ…
Tighter McCormick Relaxations through Subgradient Propagation
Jaromił Najman, Alexander Mitsos
Tight convex and concave relaxations are of high importance in the field of deterministic global optimization. We present a heuristic to tighten relaxations obtained by the McCormi…
Optimal Deterministic Algorithm Generation
Alexander Mitsos, Jaromił Najman, Ioannis G. Kevrekidis
A formulation for the automated generation of algorithms via mathematical programming (optimization) is proposed. The formulation is based on the concept of optimizing within a par…