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Goedecke, Dr.-Ing. Ralf; Hofen, Dipl.-Ing. Willi; Sass, Richard; Wendeler, Dr.-Ing. Helmut; Schembecker, Dr.-Ing. Gerhard; Wozny, Dr.-Ing. Günter; Hahn, Dr.-Ing. Henrik; Albert, Dipl.-Ing. Wolfgang

doi:10.1002/9783527623631.ch2

Cited by 3 publications

(3 citation statements)

References 181 publications

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“…1, which have to be considered for the selection of a separation operation, as well as the respective characteristic physicochemical quantities. Goedecke et al [3] list these material properties as relevant decision variables, too. The substance properties shown in the table represent all necessary physical relationships and are therefore extracted from databases stored in CHEMCAD for training.…”

Section: Synthesis and Trainingmentioning

confidence: 99%

“…The development of a digital twin [1] and the integration of artificial intelligence (AI) offer a multitude of new opportunities to reduce costs, time and engineering effort [2]. Particularly in the area of process development, which is predominantly based on heuristic decisions [3], data-driven models can find their application for engineering assistance. However, the increasing digitalization of the process industry continuously provides new data and opportunities, which can be used as a basis for AI or, in particular, machine learning (ML), which is able to learn rules and regularities from data by using statistical models.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation

Oeing

Henke

Kockmann

2021

Chemie Ingenieur Technik

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As part of Industry 4.0, workflows in the process industry are becoming increasingly digitalized. In this context, artificial intelligence (AI) methods are also finding their way into the process development. In this communication, machine learning (ML) algorithms are used to suggest suitable separation units based on simulated process streams. Simulations that have been performed earlier are used as training data and the information is learned by machine learning models implemented in Python. The trained models show good, reliable results and are connected to a process simulator using a .NET framework. For further optimization, a concept for the implementation of user feedback will be assigned. The results will provide the fundamental basis for future AI‐based recommendation systems.

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Section: Synthesis and Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation

Oeing

Henke

Kockmann

2021

Chemie Ingenieur Technik

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“…In the present work, the sequential modular approach of the process simulation software Aspen Plus was applied [13,14]. Firstly, the state-of-the-art process was simulated utilizing parameters given in literature as summarized in Table 1.…”

Section: Simulation Of the State-of-the-art Process And The Proposed mentioning

confidence: 99%

Novel process for the exergetically efficient recycling of chlorine by gas phase electrolysis of hydrogen chloride

Bechtel

Vidaković‐Koch

Sundmacher

2018

Chemical Engineering Journal

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Due to the steadily increasing production capacities of polycarbonates and polyurethanes, the volume of hydrogen chloride (HCl), which emerges as byproduct from these processes, is experiencing a significant growth. Owing to the oversaturation of the market for HCl and hydrochloric acid, the question on how to utilize this byproduct in a sustainable and energyefficient way is becoming more and more important. The oxidation of HCl to chlorine, which can be redirected as an educt to these above mentioned processes, offers a feasible solution to this problem and can be carried out either heterogeneously catalyzed at high temperatures or through electrolysis. Up to now, the most energy-efficient industrially employed electrochemical variant is the Bayer UHDENORA process, based on aqueous hydrochloric acid as a feed stock. The major objective of this work is to propose a novel electrochemical process utilizing HCl as a gaseous reactor feed in order to significantly reduce the electrical energy demand of the reactor combined with new and more energy-efficient subsequent separation sequences. For this purpose, flowsheet simulations of the Bayer UHDENORA process and two novel process variants based on the gas phase reactor and two different separation sequences were carried out and followed by a detailed exergy analysis. The analysis shows significant exergetic savings of up to 38 % in this novel process variants compared to the Bayer UHDENORA process, including not only the electrochemical reactor but also the subsequent separation steps. The novel process therefore allows for a more sustainable and energy-efficient production of the base chemical chlorine and, contrary to the heterogeneously catalyzed high temperature process, for the utilization of electrical energy from advancing renewable sources like wind and sun.

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Scale Up and Scale Down of Equipment

Moussa

2022

ACS Symposium Series

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Verfahrensentwicklung

Cited by 3 publications

References 181 publications

Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation

Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation

Novel process for the exergetically efficient recycling of chlorine by gas phase electrolysis of hydrogen chloride

Scale Up and Scale Down of Equipment

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