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Flexible operation of modular electrochemical CO 2 reduction processes

IFAC-PapersOnLine 2022年第7期55卷 298-303页

作者： K. Roh L.C. Brée P. Schäfer D. Strohmeier A. Mitsos Department of Chemical Engineering and Applied Chemistry Chungnam National University 34141 Daejeon Republic of Korea Process Systems Engineering (AVT.SVT) RWTH Aachen University 52074 Aachen Germany

Electrochemical CO 2 reduction (eCO 2 R) is an emerging technology that is capable of producing various organic chemicals from CO 2 , but its high electricity cost is a big economic obstacle. One solution to reduce the cumulative electricity cost is demand side management, i.e., to adjust the power load based on time-variant electricity prices. However, varying the power load of CO 2 -electrolyzers often leads to changes in Faraday efficiency towards target components and thereby influences the product composition. Such deviations from the target product composition may be undesired for downstream processes. We tackle this challenge by proposing a flexible operating scheme for a modular eCO 2 R process. We formulate the economically optimal operation of an eCO 2 R process with multiple electrolyzer stacks as a parallel-machine scheduling problem. Adjusting the power load of each sub-process properly, we can save electricity costs while the desired product composition is met at any time. We apply an algorithm based on wavelet transform to solve the resulting large-scale nonlinear scheduling problem in tractable time. We solve each optimization problem with a deterministic global optimization software MAiNGO. We examine flexible operation of a modular eCO 2 R process for syngas production. The case studies show that the modular structure enables savings in the cumulative electricity cost of the eCO 2 R process via flexible operation while deviations in the syngas composition could be reduced. Also, the maximum ramping speed of the entire process is found to be a key parameter that strongly influences the cost saving.

关键词： Electrochemical CO2 reduction Demand side management Modularization Parallel-machine scheduling Nonlinear scheduling Wavelet transform

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MAiNGO – A Global Optimizer for process engineering: Algorithm and Applications in process Design and Machine Learning

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Chemie Ingenieur Technik 2022年第9期94卷

作者： D. Bongartz S. Fahr J. Najman C. D. Kappatou S. Sass A. M. Schweidtmann A. Mitsos RWTH Aachen University Process Systems Engineering (AVT. SVT) Forckenbeckstr. 51 52074 Aachen Germany

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Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points

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COMPUTERS & CHEMICAL engineering 2022年 161卷

作者： Langiu, Marco Dahmen, Manuel Mitsos, Alexander [a]Forschungszentrum Julich GmbH Institute of Energy and Climate Research Energy Systems Engineering (IEK-10) Julich Germany [b]RWTH Aachen University Germany [c]JARA-ENERGY Julich 52425 Germany [d]RWTH Aachen University Process Systems Engineering (AVT.SVT) Aachen 52074 Germany

We simultaneously optimize both the design and operation of an air-cooled geothermal organic Rankine cycle, maximizing total annual return (TAR), while considering multiple operating scenarios based on different ambient temperatures. In order to accurately capture realistic off-design behavior of the heat exchangers and turbine, as well as for the overall system, we incorporate component models that consider performance variations with both size and operating conditions. We employ a hybrid mechanistic data-driven modeling approach, involving artificial neural networks (ANNs) as surrogate models for accurate fluid properties, as well as for intermediate expressions for which ANNs improve tractability of the optimization problem. We demonstrate the importance of considering multiple operating conditions within design problems and propose a methodology for formulating and solving such problems globally, using our open-source solver MAiNGO. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Design and operation ORC Optimization Two-stage stochastic programming Artificial neural networks

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Reactor Design Via Scan Line Patterning: An Implicit Approach to Create Scalable Microstructured Parts in Selective Laser Melting

SSRN

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SSRN 2023年

作者： Limper, Alexander Brodersen, Anselm Zaio, Francesca Ambrosetti, Matteo Linkhorst, John Chemical Process Engineering AVT.CVT RWTH Aachen University Forckenbeckstraße 51 Aachen52074 Germany Process Engineering of Electrochemical Systems Department of Mechanical Engineering Technical University of Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany Politecnico di Milano Dipartimento di Energia Via Lambruschini 4 Milano20156 Italy

The adaptation of additive manufacturing for chemical flow reactors has recently gained momentum as the methods become more advanced and equipment is increasingly *** the design of periodic open cellular structures (POCS) from metal selective laser melting (SLM), structured catalyst substrates can be realized in a much more ordered fashion than well-established metal foams with a random ***, tailored transport properties are achieved and flow profiles are homogenized, eliminating flow channeling and stagnant flow zones. However, with current SLM methods, the minimal achievable feature size and therewith the specific surface area is still limited. Moreover, the size of finely resolved STL files that define the structure grows exponentially with decreasing feature sizes, further limiting the scalability of methods with direct structure definition. In this work, we present a method for implicit strucutre design by metal SLM with specific surface areas and porosities that compete with established catalyst *** definition of the microstructure is realized by control of the laser scan lines, where single scan lines create fine struts. The general applicability of our method to various metal materials is demonstrated by production from stainless steel and copper. In a numerical study for a model steam reforming application, we show that the POCS provide mass transfer coefficients and volumetric reaction rates that compete with benchmark substrates. Scan line patterning therefore provides a promising method to create highly ordered, well-scalable substrates for heterogeneous catalysis from a wide range of materials, e.g., for the application in highly efficient small-scale reactors. © 2023, The Authors. All rights reserved.

关键词： Substrates

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Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

arXiv

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arXiv 2022年

作者： Rall, Deniz Schweidtmann, Artur M. Kruse, Maximilian Evdochenko, Elizaveta Mitsos, Alexander Wessling, Matthias RWTH Aachen University AVT.CVT - Aachener Verfahrenstechnik Chemical Process Engineering Forckenbeckstrasse 51 Aachen52074 Germany DWI - Leibniz Institute for Interactive Materials Forckenbeckstrasse 50 Aachen52074 Germany RWTH Aachen University AVT.SVT - Aachener Verfahrenstechnik Process Systems Engineering Forckenbeckstrasse 51 Aachen52074 Germany

Innovative membrane technologies optimally integrated into large separation process plants are essential for economical water treatment and disposal. However, the mass transport through membranes is commonly described by nonlinear differential-algebraic mechanistic models at the nano-scale, while the process and its economics range up to large-scale. Thus, the optimal design of membranes in process plants requires decision making across multiple scales, which is not tractable using standard tools. In this work, we embed artificial neural networks (ANNs) as surrogate models in the deterministic global optimization to bridge the gap of scales. This methodology allows for deterministic global optimization of membrane processes with accurate transport models – avoiding the utilization of inaccurate approximations through heuristics or short-cut models. The ANNs are trained based on data generated by a one-dimensional extended Nernst-Planck ion transport model and extended to a more accurate two-dimensional distribution of the membrane module, that captures the filtration-related decreasing retention of salt. We simultaneously design the membrane and plant layout yielding optimal membrane module synthesis properties along with the optimal plant design for multiple objectives, feed concentrations, filtration stages, and salt mixtures. The developed process models and the optimization solver are available open-source, enabling computational resource-efficient multi-scale optimization in membrane science. Copyright © 2022, The Authors. All rights reserved.

关键词： Microfiltration

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GraphXForm: Graph transformer for computer-aided molecular design with application to extraction

arXiv

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arXiv 2024年

作者： Pirnay, Jonathan Rittig, Jan G. Wolf, Alexander B. Grohe, Martin Burger, Jakob Mitsos, Alexander Grimm, Dominik G. Technical University of Munich TUM Campus Straubing for Biotechnology and Sustainability Bioinformatics Straubing Germany University of Applied Sciences Weihenstephan-Triesdorf Bioinformatics Straubing Germany Process Systems Engineering AVT.SVT RWTH Aachen University Aachen Germany Technical University of Munich TUM Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Straubing Germany Chair of Computer Science 7 RWTH Aachen University Aachen Germany Aachen Germany Forschungszentrum Jülich GmbH Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering Jülich Germany

Generative deep learning has become pivotal in molecular design for drug discovery and materials science. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on specific objectives. However, string-based models face challenges in ensuring chemical validity and enforcing structural constraints like the presence of specific substructures. We propose to instead combine graph-based molecular representations, which can naturally ensure chemical validity, with transformer architectures, which are highly expressive and capable of modeling long-range dependencies between atoms. Our approach iteratively modifies a molecular graph by adding atoms and bonds, which ensures chemical validity and facilitates the incorporation of structural constraints. We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned using a new training algorithm that combines elements of the deep cross-entropy method with self-improvement learning from language modeling, allowing stable fine-tuning of deep transformers with many layers. We evaluate GraphXForm on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques, while it can flexibly enforce structural constraints or initiate the design from existing molecular structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Customizable and Interactive Visualizations for Investigating Spatio-Temporal Single-Cell Information

Customizable and Interactive Visualizations for Investigatin...

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IEEE International Symposium on Biomedical Imaging

作者： Johannes Seiffarth Luisa Blöbaum Alexander Grünberger Katharina Nöh Institute of Bio- and Geosciences IBG-1: Biotechnology Forschungszentrum Jülich Jülich Germany Computational Systems Biotechnology (AVT.CSB) RWTH Aachen University Aachen Germany Multiscale Bioengineering Technical Faculty Bielefeld University Bielefeld Germany Institute of Process Engineering in Life Sciences Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Modern live-cell imaging and machine-learning based image processing methods shed light on the secrets of the behavior and characteristics of individual cells including their growth and development into colonies. However, the amount and complexity of spatio-temporal live-cell data makes gaining insights notoriously difficult. Therefore, we present novel visualizations that combine spatial and temporal information in interconnected and highly customizable view components. We demonstrate the flexibility of these components by designing experiment-specific visualizations at the example of three live-cell imaging data sets and gain insights into cell division, population formation and dynamic growth rates. The visualizations are implemented in Python notebooks and publicly available at https://***/JuBiotech/Supplement-to-Seiffarth-et-al.-ISBI-2024.

关键词： Image processing Sociology Data visualization Machine learning Complexity theory Statistics Biomedical imaging

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Adaptive learning of hybrid models for nonlinear model predictive control of distillation columns

arXiv

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arXiv 2020年

作者： Lüthje, Jannik T. Schulze, Jan C. Caspari, Adrian Mhamdi, Adel Mitsos, Alexander Schäfer, Pascal RWTH Aachen University AVT - Aachener Verfahrenstechnik Process Systems Engineering Aachen52072 Germany AVT Process Systems Engineering RWTH Aachen University Aachen52056 Germany

Nonlinear model predictive control (NMPC) requires accurate and computationally efficient plant models. Our previous work has shown that the classical compartmentalization model reduction approach for distillation columns can be enhanced by replacing parts of the system of equations by artificial neural networks (ANNs) trained on offline solved solutions to improve computational performance. In real-life applications, the absence of a high-fidelity model for data generation can, however, prevent the deployment of this approach. Therefore, we propose a method that utilizes solely plant measurement data, starting from a small initial data set and then continuously adapting to newly measured data. The efficacy of the proposed approach is examined in silico for a distillation column from literature. To this end, we first adjust our reduced hybrid mechanistic/data-driven modeling approach that originally builds on compartmentalization to a stage-aggregation procedure, tailoring it for the application within the adaptive learning framework. Second, we apply an adaptive learning algorithm that trains the ANNs replacing the stationary stage-to-stage calculations on newly available data. We apply the adaptive learning of the hybrid model within a regulatory NMPC framework and conduct closed-loop simulations. We demonstrate that by using the proposed method, the control performance can be steadily improved over time compared to a non-adaptive approach while being real-time applicable. Moreover, we show that the performance when using either a model trained on excessive amounts of offline generated data or the original high-fidelity model can be approached in the limit. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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Economic Nonlinear Model Predictive Control for Flexible Operation of Air Separation Units⁎

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IFAC-PapersOnLine 2018年第20期51卷 295-300页

作者： Caspari, Adrian Faust, Johannes M.M. Schäfer, Pascal Mhamdi, Adel Mitsos, Alexander AVT Process Systems Engineering RWTH Aachen University Aachen52056 Germany

The integration of renewables into the portfolio of energy sources implies that dynamic operation of energy intensive processes, such as air separation, may give an economic advantage. Dynamic process operation can be achieved by applying economic nonlinear model predictive control (eNMPC). In this work, we present an in-silico case study of dynamic operation of an air separation process under fluctuating electricity prices. Using a day-ahead electricity price profile, an offline dynamic optimization (DO) problem is solved, which is used as an initial guess to start a fast update method deployed by the eNMPC. The eNMPC uses the same objective and constraints as the offline DO. However, the prediction horizon is shorter and current states and disturbances are taken into account. A first-principle air separation process model implemented in Modelica is used in all optimization problems. All optimization problems are solved using the DO framework DyOS. The process is flexibly operated by the application of the eNMPC, which leads to near-optimal economic process behavior during operation. This work demonstrates the contribution of model based process control to the integration of renewable energy sources in the supply chain of the process industry. © 2018

关键词： Model predictive control Demand side management Electric utilities Natural resources Nonlinear systems Optimal control systems Optimization Predictive control systems Renewable energy resources Separation Source separation Supply chains Air separation Economic modeling Nonlinear model predictive control Optimal control problem Renewable energy source

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Dynamics of the Boundary Layer in Pulsed CO2Electrolysis

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Angewandte Chemie 2024年第34期136卷

作者： Matthias Heßelmann Daniel Felder Wenzel Plischka Sajad Nabi Prof. Dr.-Ing. John Linkhorst Prof. Dr.-Ing. Matthias Wessling Dr.-Ing. Robert Keller Chemical Process Engineering AVT.CVT RWTH Aachen University Forckenbeckstraße 51 52074 Aachen Germany These authors contributed equally. DWI—Leibniz Institute for Interactive Materials e.V. Forckenbeckstraße 50 52074 Aachen Germany Current address: TU Darmstadt Process Engineering of Electrochemical Systems Otto-Berndt-Straße 2 64287 Darmstadt Germany

Electrochemical reduction of CO 2 poses a vast potential to contribute to a defossilized industry. Despite tremendous developments within the field, mass transport limitations, carbonate salt formation, and electrode degradation mechanisms still hamper the process performance. One promising approach to tweak CO 2 electrolysis beyond today's limitations is pulsed electrolysis with potential cycling between an operating and a regeneration mode. Here, we rigorously model the boundary layer at a silver electrode in pulsed operation to get profound insights into the dynamic reorganization of the electrode microenvironment. In our simulation, pulsed electrolysis leads to a significant improvement of up to six times higher CO current density and 20 times higher cathodic energy efficiency when pulsing between −1.85 and −1.05 V vs SHE compared to constant potential operation. We found that elevated reactant availability in pulsed electrolysis originates from alternating replenishment of CO 2 by diffusion and not from pH-induced carbonate and bicarbonate conversion. Moreover, pulsed electrolysis substantially promotes carbonate removal from the electrode by up to 83 % compared to constant potential operation, thus reducing the risk of salt formation. Therefore, this model lays the groundwork for an accurate simulation of the dynamic boundary layer modulation, which can provide insights into manifold electrochemical conversions.

关键词： Electrochemistry Electric double layer Modelling Pulsed electrolysis Renewable resources

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