Johanna Kleinekorte

BoFire: Bayesian Optimization Framework Intended for Real Experiments

Johannes P. Dürholt, Thomas S. Asche, Johanna Kleinekorte, Gabriel Mancino-Ball et autres

Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature or software, require substantial adaptation for effective real-world …

APPROPRIATE Life Cycle Assessment: A PRO cess-Specific, PR edictive I mpact A ssessmen T Method for Emerging Chemical Processes

Johanna Kleinekorte, Jonas Kleppich, Lorenz Fleitmann, Verena Beckert et autres

High Resolution Image Download MS PowerPoint Slide A sustainable chemical industry needs to quantify its emissions and resource consumption by life cycle assessment (LCA). However, LCA requires detailed mass and energy balances, which are usually not available at early process development stages. …

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Syngas from What? Comparative Life-Cycle Assessment for Syngas Production from Biomass, CO2, and Steel Mill Off-Gases

Marvin Bachmann, Simon Völker, Johanna Kleinekorte, André Bardow

High Resolution Image Download MS PowerPoint Slide Syngas is an essential platform chemical for producing chemicals and fuels, whose market volume is expected to grow strongly. Today, syngas is mainly produced from fossil resources and thus emits large amounts of greenhouse gases …

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Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

Lorenz Fleitmann, Philipp Ackermann, Johannes Schilling, Johanna Kleinekorte et autres

High Resolution Image Download MS PowerPoint Slide Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel’s molecular structure to the needs of SI engines with very high compression ratios, computer-aided …

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What Shall We Do with Steel Mill Off-Gas: Polygeneration Systems Minimizing Greenhouse Gas Emissions

Johanna Kleinekorte, Matthias Leitl, Christian Zibunas, André Bardow

Both the global steel and chemical industries contribute largely to industrial greenhouse gas (GHG) emissions. For both industries, GHG emissions are strongly related to the consumption of fossil resources. While the chemical industry often releases GHGs as direct process emissions, steel mills …

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COSMO-susCAMPD: Sustainable solvents from combining computer-aided molecular and process design with predictive life cycle assessment

Lorenz Fleitmann, Johanna Kleinekorte, Kai Leonhard, André Bardow

Sustainable solvents are crucial for chemical processes and can be tailored to applications by Computer-Aided Molecular and Process Design (CAMPD). Recent CAMPD methods consider not only economics but also environmental hazards and impacts. However, holistic environmental assessment needs to address the complete …

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Hüttengas‐to‐X: Life Cycle Assessment zum Potenzial der Sektorenkopplung von Stahl und Chemie

Johanna Kleinekorte, Christian Zibunas, André Bardow

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Life Cycle Assessment for the Design of Chemical Processes, Products, and Supply Chains

Johanna Kleinekorte, Lorenz Fleitmann, Marvin Bachmann, Arne Kätelhön et autres

Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to …

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Combining Process Short cuts and ArtificialNeural Networks for Predictive Life CycleAssessment of Chemicals

Johanna Kleinekorte, Marcel Welz, Lorenz Fleitmann, Leif C. Kröger et autres

A Neural Network-Based Framework to Predict Process-Specific Environmental Impacts

Johanna Kleinekorte, Leif C. Kröger, Kai Leonhard, André Bardow

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Energieeffizienz vs. Umweltauswirkungen: Integration von Molekül‐ und Prozessdesign mit prädiktiver Ökobilanzierung

Johanna Kleinekorte, Lorenz Fleitmann, Jan Scheffczyk, André Bardow

Im H2020 Projekt CoPro werden Technologien entwickelt, die die Fahrweise gekoppelter Prozesse unter Beru ¨cksichtigung von externen Faktoren und internen Beschra ¨nkungen optimieren.Da eine zentrale Standortoptimierung meist nicht mo ¨glich ist, wird eine verteilte Optimierung vorgeschlagen, bei der fu ¨r die Anlagen …

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Robust dynamic optimization of batch processes under parametric uncertainty: Utilizing approaches from semi-infinite programs

Jennifer Puschke, Hatim Djelassi, Johanna Kleinekorte, Ralf Hannemann et autres

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