Luca Bosetti

Last Name

Bosetti

First Name

Luca

ORCID

0000-0001-6657-6510

Organisational unit

01159 - Lehre Maschinenbau und Verfahrenstechnik

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Publications 1 - 10 of 35

Accounting for the Presence of Molecular Clusters in Modeling and Interpreting Nucleation and Growth
Ahn, Byeongho; Bosetti, Luca; Mazzotti, Marco (2022)
Crystal Growth & Design
The effect of molecular cluster formation on the estimation of kinetic parameters for primary nucleation and growth in different systems has been studied using computationally generated data and three sets of experimental data in the literature. It is shown that the formation of molecular clusters decreases the concentration of monomers and hence the thermodynamic driving force for crystallization, which consequently affects the crystallization kinetics. For a system exhibiting a strong tendency to form molecular clusters, accounting for cluster formation in a kinetic model is critical to interpret kinetic data accurately, for instance, to estimate the specific surface energy γ from a set of primary nucleation rates. On the contrary, for a system with negligible cluster formation, a consideration of cluster formation does not affect parameter estimation outcomes. Moreover, it is demonstrated that using a growth kinetic model that accounts for cluster formation allows the estimation of γ from typical growth kinetic data (i.e., de-supersaturation profiles of seeded batch crystallization), which is a novel method of estimating γ developed in this work. The applicability of the novel method to different systems is proven by showing that the estimated values of γ are closely comparable to the actual values used for generating the kinetic data or the corresponding estimates reported in the literature.
Automating Life Cycle Assessment from Chemical Process Simulations
Spiekermann, Lukas; Sewani, Hitesh; Lochmann, Sebastian; et al. (2024)
Advancing sustainability requires knowledge on the environmental impacts of chemicals. For this purpose, life cycle assessment is the preferred method, but usually carried out by manually extracting data from process simulation software and transferring data to life cycle assessment software. This process is very labor-intensive and error-prone. Here, we bridge the gap between process simulation and life cycle assessment by automated data extraction from process simulators to life cycle assessment software. Our tool currently links the process simulators Aspen Plus, Aspen HYSYS, and AVEVA Process Simulation to the open-source tools Brightway/Activity Browser for life cycle assessment. The tool is exemplified using openly available case studies and simulation files for bio-based and CO2-based processes. Simulation studies can be combined to, e.g., integrated CO2 capture and utilization chains within life cycle assessment software. Our tool directly integrates process simulations results into life cycle inventory databases with easy workflows and could thereby enable the generation of more life cycle assessments of chemical processes.
Integrating CO2 Electrolyzers in Electrochemical Plants: Heat Integration, Techno-Economic Analysis, and Life Cycle Assessment of the Production of 1-Butene from CO2
Spiekermann, Lukas; Lee, Mi Gyoung; Wicks, Joshua; et al. (2024)
2024 AIChE Annual Meeting Proceedings
Efforts to mitigate climate change have increased interest in utilizing CO2 as a sustainable feedstock within the chemical industry [1]. Recent progress in electrochemical CO2 reduction, facilitated using renewable electricity sources, presents a promising avenue for synthesizing valuable C2+ chemicals [2]. While the initial focus has been on the development of electrolyzers, recent research has broadened to include process design and downstream separation techniques [3]. However, the question remains how to integrate feedback from process design back into electrolyzer development. This work introduces a feedback loop from process design to electrolyzer development. The loop combines process modeling, heat integration, techno-economic analysis, and life cycle assessment. The resulting workflow is demonstrated for the CO2 electroreduction to ethylene and subsequent dimerization to 1-butene [4]. From the analysis, we derive development targets for CO2 electrolyzers. We assess the significance of cell voltage and Faradaic efficiency, highlighting that single-pass conversion minimally impacts overall process feasibility. Moreover, we emphasize the benefits of integrating electrolyzers with downstream and upstream units. In summary, we combine electrolyzer development and process systems engineering perspectives by bridging the scales from electrolyzers to integrated processes with economic and sustainability objectives. This study illustrates the integration of a process-oriented approach into technology advancement for an electrified chemical industry.
Integrated design of solvent–antisolvent mixtures and crystallization processes powered by machine learning
Bosetti, Luca; Winter, Benedikt; Lindfeld, Johanna; et al. (2025)
Computers & Chemical Engineering
Crystallization is a key separation technology in the chemical and pharmaceutical industries, offering high-purity products with relatively low energy consumption. However, the design of efficient antisolvent crystallization processes is inherently complex due to the interactions between solvent and antisolvent, as well as the selection of process conditions. Existing computer-aided molecule and process design (CAMPD) frameworks rely on group contribution or quantum-mechanical methods for thermophysical property predictions, which either limit the molecular design space or result in high computational costs. To overcome these challenges, we couple machine learning-based property predictions with a SMILES-based molecular design algorithm into a CAMPD framework (ML-CAMPD), enabling rapid and accurate solvent selection for crystallization. We demonstrate this ML-CAMPD framework through the case study of ibuprofen antisolvent crystallization, showing improvements in process efficiency. A screening study identified acetone-water as the most promising solvent–antisolvent pair. By applying the CAMPD framework to design new mixtures, we find solvent–antisolvent systems that outperformed acetone-water by 10% in energy efficiency. The proposed approach broadens the applicability of CAMPD frameworks and offers a powerful tool for designing efficient and sustainable crystallization processes.
From machine learning for phase equilibria to solvent design for liquid-liquid extraction
Lindfeld, Johanna; Bosetti, Luca; Winter, Benedikt; et al. (2024)
Higher alcohol = higher value? Identifying Promising and Unpromising Synthesis Routes for 1-Propanol
Spiekermann, Lukas; McKenna, Mae; Bosetti, Luca; et al. (2025)
ESCAPE|35: Book of Short Papers
In response to climate change, the chemical industry is investigating the synthesis of new platform chemicals from renewable carbon sources. A potential higher-value platform chemical is 1-propa nol. 1-propanol can be produced from CO2 and biomass via various routes, but their respective benefits and disadvantages are unclear. Here, we aim to identify promising synthesis routes for 1-propanol production and establish de velopment targets for competitiveness with benchmark technologies. To evaluate their cost-ef fectiveness and climate impacts, we expand a technology choice model of chemical synthesis routes by thermo-catalytic, electrocatalytic, and fermentation-based synthesis steps to produce 1-propanol from CO2, biomass feedstocks, and fossil resources. While the model accounts for var ious intermediates, the direct conversion of CO2 or CO to 1-propanol is a growing research field and, thus, the focus of our analysis. A comprehensive techno-economic analysis and a life cycle assessment quantify new synthesis routes' economic and environmental potentials. Our findings define performance targets for the competitiveness of the direct conversion of CO2 or CO to 1-propanol via thermo-catalytic hydrogenation or electrocatalysis. If these performance targets remain unachieved, the direct synthesis of 1-propanol is outperformed by multi-step pro cesses based on syngas and ethylene from CO2 or biomass. Overall, our study demonstrates the critical role of synthesis route optimization in guiding the de velopment of new chemical processes. By establishing quantitative benchmarks, we provide a roadmap for advancing 1-propanol synthesis technologies, contributing to the broader effort of reducing the chemical industry's carbon footprint.
Machine learning-based prediction of liquid-liquid equilibria of aqueous mixtures
Lindfeld, Johanna; Bosetti, Luca; Winter, Benedikt; et al. (2024)
How similar are human- and computer-generated flowsheets?
Laub, Jan-Frederic; Bosetti, Luca; Bardow, André (2025)
Machine-learning-powered molecular design: Optimal solvents for hybrid extraction-distillation
Lindfeld, Johanna; Bosetti, Luca; Winter, Benedikt Alexander; et al. (2023)
Foundations of Process/Product Analytics and Machine Learning (FOPAM 2023). Poster Abstracts
Secondary Nucleation by Interparticle Energies. I. Thermodynamics
Bosetti, Luca; Ahn, Byeongho; Mazzotti, Marco (2022)
Crystal Growth & Design
Secondary nucleation, in the absence of attrition, is known to be dependent on external fields, such as contact forces, shear, or interparticle forces. In this contribution, the thermodynamic effect of the presence of the seed crystal surface on secondary nucleation is derived in the context of the classical nucleation theory. The Gibbs free energy for the formation of a cluster close to a seed crystal is calculated with the addition of interparticle energies, namely, van der Waals attractive forces and Born repulsive forces. This results in the stabilization of a subcritical cluster close to the seed surface that can become a secondary nucleus more easily than under homogeneous nucleation conditions. Far from the seed surface, the developed model is reduced to the homogeneous nucleation described by the classical nucleation theory. The crystallization of paracetamol from an ethanol solution is taken as a case study, and the stabilization effect, given by the presence of interparticle energies, can be observed at different values of supersaturation. Three key indicators have been defined and calculated to describe the intensity of the stabilization effect, two of which, namely, the distance from the seed surface where the stabilization is active and the enhancement factor for supersaturation, are used in Part II of this series to describe the kinetics of secondary nucleation by interparticle energies.

Publications 1 - 10 of 35

Luca Bosetti

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