AI in Chemical Engineering – We Are Just at the Beginning

Bortz, Michael; Dadhe, K.; Mitsos, Alexander

doi:10.1002/cite.202171202

Cited by 4 publications

(4 citation statements)

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“…Data-Driven Chemical Reaction Optimization: Machine intelligence models predicting reaction outcomes, optimizing conditions, and discovering new reactivity. 18. Automated Synthesis Planning: Assistance from AI tools in planning and optimizing synthetic routes, reducing experimental trial time and resources.…”

Section: Deep Learning In Structure-activity Relationships (Sar)mentioning

confidence: 99%

“…16,17 The emergence of digital twins and integrated data systems is highlighted for their ability to simulate and optimize chemical processes, enhancing efficiency and sustainability in chemical manufacturing. [18][19][20][21] Furthermore, ongoing research has examined the role of deep learning in exploring complex structure-activity relationships, thereby revolutionizing drug discovery and material science by predicting molecular behaviors with reasonable accuracy. The application of natural language processing in mining the vast volume of chemical literature illuminates its potential for accelerating knowledge acquisition and fostering innovation.…”

Section: Introductionmentioning

confidence: 99%

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Top 20 Influential AI-Based Technologies in Chemistry

Ananikov

2024

Preprint

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The beginning and ripening of digital chemistry is analyzed focusing on the role of artificial intelligence (AI) in an expected leap in chemical sciences to bring this area to the next evolutionary level. The analytic description selects and highlights the top 20 AI-based technologies and 7 broader themes that are reshaping the field. It underscores the integration of digital tools such as machine learning, big data, digital twins, the Internet of Things (IoT), robotic platforms, smart control of chemical processes, virtual reality and blockchain, among many others, in enhancing research methods, educational approaches, and industrial practices in chemistry. The significance of this study lies in its focused overview of how these digital innovations foster a more efficient, sustainable, and innovative future in chemical sciences. This article not only illustrates the transformative impact of these technologies but also draws new pathways in chemistry, offering a broad appeal to researchers, educators, and industry professionals to embrace these advancements for addressing contemporary challenges in the field.

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Section: Deep Learning In Structure-activity Relationships (Sar)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Top 20 Influential AI-Based Technologies in Chemistry

Ananikov

2024

Preprint

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“…Since the capabilities for AI/ML along with the advances in access to high-resolution high-fidelity distributed data through cloud computing and highperformance computing (HPC) have improved greatly in the past decades, it is important to reassess the role of such technologies in the future research directions of chemical engineering. Bortz et al [37] have correctly assessed that AI/ML in chemical engineering is at the beginning stages, and thus it is an opportune time for providing multiple perspectives on this highly relevant issue for the chemical engineering discipline and industry. Coley et al [8,9] presented a two-part review of scientific workflows for autonomous discoveries in chemical sciences in broad categories of physical matter, processes, and models.…”

Section: Fieldscalementioning

confidence: 99%

Chemical engineering at crossroads

Nandakumar

Tyagi

et al. 2022

Can J Chem Eng

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Through periodic introspection and assessment, the chemical engineering field has developed a mature undergraduate curriculum built on a strong science background in mathematics, physics, and chemistry. This brings a unique set of skills in transport, reaction engineering, and thermodynamics, coupled with suitable process systems engineering and process design courses, to supply well-trained engineers to a vast array of process manufacturing facilities. These facilities produce basic chemicals, pharmaceuticals, oil and gas, petrochemicals, food and agricultural products, minerals, and materials. While this maturity has served existing industries well, we argue that the chemical engineering field is at crossroads between managing the curriculum of undergraduate and graduate education to supply the needs of established industries while creating innovators for emerging industries. While this is a great opportunity for yet another introspection, we caution that the inadvertent cannibalization of the field must be avoided. We do argue in favour of adding a biology sequence and a computational science sequence to the core at the undergraduate level in a related perspective article.

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“…Simulation supports decision making, while comparing the experimental data to simulation results confirming the accuracy of the measurement procedures. However, the model used in simulation should be reliable for its prediction, by capturing significant phenomena and showing flexibility to fit relevant model parameters as it was reported in [15]. We used PPBDesigner, a population balance-based column module integrated in the DWSIM Pro process simulator to simulate discrete flow systems such as solvent extraction columns.…”

Section: Introductionmentioning

confidence: 99%

AI‐Based Supervision for a Stirred Extraction Column Assisted with Population Balance‐Based Simulation

Neuendorf

Hammal²,

Fricke³

et al. 2023

Chemie Ingenieur Technik

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Solvent extraction as environmental benign separation technique can be modeled in physical detail by population balance of the droplet size distribution. However, much information on the droplet generation and coalescence is necessary for representative results. In this contribution, we present a comparison of AI-evaluated experimental and simulated data on the behavior of a stirred solvent extraction column with an inner diameter of 32 mm. Lab experiments were performed using the standard test system with n-butyl acetate, acetone, and deionized water. A digital camera is placed in front of the middle section as well as the head of the column. Droplet size evaluation is performed using a retrained neural net (Mask R-CNN). The stirred DN32 extraction column is modeled and simulated using a 1D CFD population balance software. The simulation allows for behavior analysis, trends comparison, and validation of the hydrodynamics and mass transfer performances.

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AI in Chemical Engineering – We Are Just at the Beginning

Abstract: No abstract.

Cited by 4 publications

References 0 publications

Top 20 Influential AI-Based Technologies in Chemistry

Top 20 Influential AI-Based Technologies in Chemistry

Chemical engineering at crossroads

AI‐Based Supervision for a Stirred Extraction Column Assisted with Population Balance‐Based Simulation

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