Data-driven modeling and process intensification for sustainable chemical manufacturing
| Author(s) | Chen, Tai-YIng | |
| Date Accessioned | 2023-01-25T16:53:41Z | |
| Date Available | 2023-01-25T16:53:41Z | |
| Publication Date | 2022 | |
| SWORD Update | 2022-09-21T16:08:27Z | |
| Abstract | Sustainability is vital for society, and sustainable chemical manufacturing has received growing interest for improving atom and energy efficiency and reducing operating costs and waste. Microflow chemistry can enable continuous manufacturing by precise residence time control, enhanced mixing and transport, improved yield and productivity, and inherent safety. Electrification with alternative energy sources, such as microwaves (MWs), using renewable energy could enable carbon-neutral production. Currently, the principles for designing such intensified reactors are lacking due to the limited understanding of the complex interaction of different phases among MW irradiation, hydro dynamics, and transport phenomena in the reactor. A reliable predictive modeling framework is needed for reactor design and optimization. ☐ First, in this thesis, we investigate the design and scale-up of a continuous flow microreactor under MW irradiation using first-principles modeling, data science tools, and machine learning techniques. First, we develop a multiphysics model to investigate the effects of various processing parameters on the outlet temperature. We observe a strong correlation between parameters and create a gradient boost regression tree model to predict the outlet temperature accurately. Multi-objective Bayesian optimization is employed to optimize the microfluidic channel dimensions and the processing conditions considering both heating performance and energy efficiency. Then, we scale up the reactor using an alternative cavity. We combine homogeneous Brønsted acid catalyst and MW heating for 5-hydroxymethyl furfural (HMF) production from fructose dehydration. An active learning approach is applied to optimize the HMF production rate and attain high throughput and energy efficiency. The developed workflow and combined numerical and experimental approach provide insights into scale-up and optimization. ☐ Second, we model liquid-liquid microflow systems. We employ a state-of-the-art algorithm in computational fluid dynamics (CFD) simulations to study flow patterns, extraction, and mass transport in biphasic microreactors. The convective and diffusive contributions to the mass transfer of different flow patterns are analyzed. Moreover, we build a machine learning model to predict the flow patterns accurately and identify critical features for design. ☐ Finally, we study liquid-liquid biphasic systems under MW heating. We investigate the MW-induced temperature gradients between the aqueous and organic phases. A simple analytical model is then developed to describe the temperature difference and provide design principles. We delineate the effect of temperature difference on the species partitioning and interfacial mass transfer between phases. The systematic approach offers new insights into the design and optimization of the MW-heated | |
| Advisor | Vlachos, Dionisios G. | |
| Degree | Ph.D. | |
| Department | University of Delaware, Department of Chemical and Biomolecular Engineering | |
| DOI | https://doi.org/10.58088/5cyf-ht11 | |
| Unique Identifier | 1365387080 | |
| URL | https://udspace.udel.edu/handle/19716/32148 | |
| Language | en | |
| Publisher | University of Delaware | |
| URI | https://login.udel.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/data-driven-modeling-process-intensification/docview/2717715435/se-2?accountid=10457 | |
| Keywords | Chemical manufacturing | |
| Keywords | Renewable energy | |
| Keywords | Carbon-neutral | |
| Title | Data-driven modeling and process intensification for sustainable chemical manufacturing | |
| Type | Thesis |