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Kondo M, Wathsala HDP, Ishikawa K, Yamashita D, Miyazaki T, Ohno Y, Sasai H, Washio T, Takizawa S. Bayesian Optimization-Assisted Screening to Identify Improved Reaction Conditions for Spiro-Dithiolane Synthesis. Molecules 2023; 28:5180. [PMID: 37446842 DOI: 10.3390/molecules28135180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Bayesian optimization (BO)-assisted screening was applied to identify improved reaction conditions toward a hundred-gram scale-up synthesis of 2,3,7,8-tetrathiaspiro[4.4]nonane (1), a key synthetic intermediate of 2,2-bis(mercaptomethyl)propane-1,3-dithiol [tetramercaptan pentaerythritol]. Starting from the initial training set (ITS) consisting of six trials sampled by random screening for BO, suitable parameters were predicted (78% conversion yield of spiro-dithiolane 1) within seven experiments. Moreover, BO-assisted screening with the ITS selected by Latin hypercube sampling (LHS) further improved the yield of 1 to 89% within the eight trials. The established conditions were confirmed to be satisfactory for a hundred grams scale-up synthesis of 1.
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Affiliation(s)
- Masaru Kondo
- SANKEN, Osaka University, Ibaraki-shi 567-0047, Japan
- Department of Materials Science and Engineering, Graduate School of Science and Engineering, Ibaraki University, Nakanarusawa-cho, Hitachi-shi 316-8511, Japan
| | | | | | - Daisuke Yamashita
- Asahi Chemical Co., Ltd., Mitsuya-Minami, Yodogawa Ward, Osaka-shi 532-0035, Japan
| | - Takeshi Miyazaki
- Asahi Chemical Co., Ltd., Mitsuya-Minami, Yodogawa Ward, Osaka-shi 532-0035, Japan
| | - Yoji Ohno
- Asahi Chemical Co., Ltd., Mitsuya-Minami, Yodogawa Ward, Osaka-shi 532-0035, Japan
| | - Hiroaki Sasai
- SANKEN, Osaka University, Ibaraki-shi 567-0047, Japan
- Graduate School of Pharmaceutical Sciences, Osaka University, Suita-shi 565-0871, Japan
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Ehlhardt J, Ahmad A, Wolf I, Engell S. Real‐Time Optimization Using Machine Learning Models Applied to the 4,4′‐Diphenylmethane Diisocyanate Production Process. CHEM-ING-TECH 2023. [DOI: 10.1002/cite.202200244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Affiliation(s)
- Jens Ehlhardt
- TU Dortmund, Process Dynamics and Operations Group Department of Biochemical and Chemical Emil-Figge-Straße 70 44227 Dortmund Germany
| | - Afaq Ahmad
- TU Dortmund, Process Dynamics and Operations Group Department of Biochemical and Chemical Emil-Figge-Straße 70 44227 Dortmund Germany
| | - Inga Wolf
- Covestro AG Process Technology Kaiser-Wilhelm Allee 60 51373 Leverkusen Germany
| | - Sebastian Engell
- TU Dortmund, Process Dynamics and Operations Group Department of Biochemical and Chemical Emil-Figge-Straße 70 44227 Dortmund Germany
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A Real-Time Optimization Strategy for Small-Scale Facilities and Implementation in a Gas Processing Unit. Processes (Basel) 2021. [DOI: 10.3390/pr9071179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The rise of new digital technologies and their applications in several areas pushes the process industry to update its methodologies with more intensive use of mathematical models—commonly denoted as digital twins—and artificial intelligence (AI) approaches to continuously enhance operational efficiency. In this context, Real-time Optimization (RTO) is a strategy that is able to maximize an economic function while respecting the existing constraints, which enables keeping the operation at its optimum point even though the plant is subjected to nonlinear behavior and frequent disturbances. However, the investment related to the project of commercial RTOs may make its application infeasible for small-scale facilities. In this work, an in-house, small-scale RTO is presented and its successful application in a real industrial case—a Natural Gas Processing Unit—is shown. Besides that, a new method for enhancing the efficiency of using sequential-modular simulator inside an optimization framework and a new method to account for the economic return of optimization-based tools are proposed and described. The application of RTO in the industrial case showed an enhancement in the stability of the main variables and an increase in profit of 0.64% when compared to the operation of the regulatory control layer alone.
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Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa. Processes (Basel) 2020. [DOI: 10.3390/pr8070752] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.
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Eifert T, Eisen K, Maiwald M, Herwig C. Current and future requirements to industrial analytical infrastructure-part 2: smart sensors. Anal Bioanal Chem 2020; 412:2037-2045. [PMID: 32055909 PMCID: PMC7072042 DOI: 10.1007/s00216-020-02421-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 12/24/2019] [Accepted: 01/14/2020] [Indexed: 11/28/2022]
Abstract
Complex processes meet and need Industry 4.0 capabilities. Shorter product cycles, flexible production needs, and direct assessment of product quality attributes and raw material attributes call for an increased need of new process analytical technologies (PAT) concepts. While individual PAT tools may be available since decades, we need holistic concepts to fulfill above industrial needs. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (Part 1) and smart sensors (Part 2). Part 2 of this feature article series describes the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality. The smart sensor consists of (i) chemical and process information in the physical twin by smart field devices, by measuring multiple components, and is fully connected in the IIoT 4.0 environment. In addition, (ii) it includes process intelligence in the digital twin, as to being able to generate knowledge from multi-sensor and multi-dimensional data. The cyber-physical system (CPS) combines both elements mentioned above and allows the smart sensor to be self-calibrating and self-optimizing. It maintains its operation autonomously. Furthermore, it allows—as central PAT enabler—a flexible but also target-oriented predictive control strategy and efficient process development and can compensate variations of the process and raw material attributes. Future cyber-physical production systems—like smart sensors—consist of the fusion of two main pillars, the physical and the digital twins. We discuss the individual elements of both pillars, such as connectivity, and chemical analytics on the one hand as well as hybrid models and knowledge workflows on the other. Finally, we discuss its integration needs in a CPS in order to allow its versatile deployment in efficient process development and advanced optimum predictive process control.
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Affiliation(s)
- Tobias Eifert
- Arbeitskreis Prozessanalytik, Gesellschaft Deutscher Chemiker, 60486, Frankfurt am Main, Germany.,Covestro Deutschland AG, /Uerdingen, 47829, Krefeld, Germany
| | - Kristina Eisen
- Arbeitskreis Prozessanalytik, Gesellschaft Deutscher Chemiker, 60486, Frankfurt am Main, Germany.,Daiichi Sankyo Europe GmbH, 81379, Munich, Germany
| | - Michael Maiwald
- Arbeitskreis Prozessanalytik, Gesellschaft Deutscher Chemiker, 60486, Frankfurt am Main, Germany.,Bundesanstalt für Materialforschung und -prüfung (BAM), 12489, Berlin, Germany
| | - Christoph Herwig
- Arbeitskreis Prozessanalytik, Gesellschaft Deutscher Chemiker, 60486, Frankfurt am Main, Germany. .,ICEBE, Research Area Biochemical Engineering, TU Wien, 1060, Vienna, Austria.
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Rößler M, Huth PU, Liauw MA. Process analytical technology (PAT) as a versatile tool for real-time monitoring and kinetic evaluation of photocatalytic reactions. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00256a] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Combining in situ Raman spectroscopy with multivariate data analysis enables the real-time monitoring and kinetic evaluation of photocatalytic reactions. The applicability is demonstrated on the photooxidation of 4-methoxythiophenol.
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Affiliation(s)
- Martin Rößler
- Institut für Technische und Makromolekulare Chemie (ITMC)
- RWTH Aachen University
- 52074 Aachen
- Germany
| | - Philipp U. Huth
- Institut für Technische und Makromolekulare Chemie (ITMC)
- RWTH Aachen University
- 52074 Aachen
- Germany
| | - Marcel A. Liauw
- Institut für Technische und Makromolekulare Chemie (ITMC)
- RWTH Aachen University
- 52074 Aachen
- Germany
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Asprion N, Bortz M. Process Modeling, Simulation and Optimization: From Single Solutions to a Multitude of Solutions to Support Decision Making. CHEM-ING-TECH 2018. [DOI: 10.1002/cite.201800051] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
| | - Michael Bortz
- Fraunhofer Institute for Industrial Mathematics; Fraunhofer Platz 1 67663 Kaiserslautern Germany
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Bezinge L, Maceiczyk RM, Lignos I, Kovalenko MV, deMello AJ. Pick a Color MARIA: Adaptive Sampling Enables the Rapid Identification of Complex Perovskite Nanocrystal Compositions with Defined Emission Characteristics. ACS APPLIED MATERIALS & INTERFACES 2018; 10:18869-18878. [PMID: 29766716 DOI: 10.1021/acsami.8b03381] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Recent advances in the development of hybrid organic-inorganic lead halide perovskite (LHP) nanocrystals (NCs) have demonstrated their versatility and potential application in photovoltaics and as light sources through compositional tuning of optical properties. That said, due to their compositional complexity, the targeted synthesis of mixed-cation and/or mixed-halide LHP NCs still represents an immense challenge for traditional batch-scale chemistry. To address this limitation, we herein report the integration of a high-throughput segmented-flow microfluidic reactor and a self-optimizing algorithm for the synthesis of NCs with defined emission properties. The algorithm, named Multiparametric Automated Regression Kriging Interpolation and Adaptive Sampling (MARIA), iteratively computes optimal sampling points at each stage of an experimental sequence to reach a target emission peak wavelength based on spectroscopic measurements. We demonstrate the efficacy of the method through the synthesis of multinary LHP NCs, (Cs/FA)Pb(I/Br)3 (FA = formamidinium) and (Rb/Cs/FA)Pb(I/Br)3 NCs, using MARIA to rapidly identify reagent concentrations that yield user-defined photoluminescence peak wavelengths in the green-red spectral region. The procedure returns a robust model around a target output in far fewer measurements than systematic screening of parametric space and additionally enables the prediction of other spectral properties, such as, full-width at half-maximum and intensity, for conditions yielding NCs with similar emission peak wavelength.
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Affiliation(s)
| | | | | | - Maksym V Kovalenko
- Laboratory for Thin Films and Photovoltaics , Empa-Swiss Federal Laboratories for Materials Science and Technology , Überlandstrasse 129 , 8600 Dübendorf , Switzerland
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