(643a) A Multi-Disciplinary Framework for Sustainable Process Synthesis: Linking Experiments, Molecular Dynamic Simulation, Process Optimization, and Life Cycle Analysis

Escalating waste accumulation and greenhouse gas emissions are critical global challenges demanding a paradigm shift towards sustainable practices in the chemical industry. The prevailing linear economy, characterized by resource extraction, consumption, and disposal, exacerbates these issues, leading to resource depletion, pollution, and climate change [1]. Transitioning to a circular economy, which emphasizes material reuse, waste valorization, and resource efficiency, offers a vital pathway to sustainability in the chemical industry. Achieving this requires a comprehensive approach that spans the entire product lifecycle, from molecular-level reaction mechanisms to system-level process optimization. While experimental chemistry, molecular dynamics (MD) simulation, and process systems engineering (PSE) contribute valuable tools and insights, their isolated application can hinder progress. However, by integrating these approaches into a multidisciplinary framework, we can overcome their limitations and develop sustainable chemical processes, thereby advancing chemical engineering and sustainability [2, 3].

Despite the strengths of individual disciplines, each faces key limitations. In PSE, process models are typically based on literature-derived data, which can be incomplete or uncertain, particularly for novel reactions, leading to reliance on assumptions that compromise the accuracy of life cycle assessments [4]. Experimental studies often prioritize reaction yield or selectivity, guided by literature-based intuition, but may overlook broader environmental and economic trade-offs. Exhaustive experimental trials are resource-intensive and may fail to explore synergistic effects across a wider reaction network [3]. MD simulations provide mechanistic insight and kinetic parameters but are constrained by the quality of experimental data and limited by computational scale, often lacking system-level evaluation [5]. These limitations collectively underscore the need for integration across disciplines.

We implement a double-direction method [6] to develop a cradle-to-cradle circular reaction network and processes, mapping pathways from feedstock supply to end-of-life recycling. This network includes reactions relevant to synthesis and degradation, enabling comprehensive performance evaluation. Promising reactions are identified based on environmental and economic criteria and selected for experimental validation. Experimental data, including yields, selectivities, and product distributions, inform MD simulations, which estimate reaction mechanisms and kinetic parameters. These simulations uncover major and minor products, and their results are experimentally validated to expand and refine the network. We apply optimization methods to estimate network performance under untested operating conditions (e.g., temperature and reaction time), enabling the identification of promising experimental ranges while reducing trial-and-error experimentation. Updated experimental data are used to refine further MD-derived kinetic parameters, which are incorporated into network optimization and used to develop a rigorous process model using a commercial simulator.

We apply this integrated framework to polyethylene terephthalate (PET) as a case study, a widely used polymer in producing plastic bottles. The initial network includes 100 chemicals and 221 reactions. The evaluation identifies PET hydrolysis as a promising end-of-life pathway, guiding experimental efforts. MD simulations informed by experimental data reveal new compounds, carbonic acid, formic acid, and terephthalaldehyde, not initially included in the network, prompting the addition of five reactions. Bayesian optimization predicts optimal operating conditions of 450–550 °C and 50–75 s, leading to additional experiments that yield high-resolution data for refined MD simulation and kinetic parameter estimation. These data are now used to build a detailed process model. This multidisciplinary approach demonstrates the potential to develop robust, accurate, and sustainable process designs by integrating molecular-level insight, experimental validation, and system-level optimization.

References

[1] Vidal, F., van der Marel, E. R., Kerr, R. W., McElroy, C., Schroeder, N., Mitchell, C., ... & Williams, C. K. (2024). Designing a circular carbon and plastics economy for a sustainable future. Nature, 626(7997), 45-57.

[2] Neira, D. E. B., Gómez-Castro, F. I., del-Río-Chanona, E. A., & Rico-Ramírez, V. (2025). Future insights for optimization in chemical engineering. Optimization in Chemical Engineering: Deterministic, Meta-Heuristic and Data-Driven Techniques, 425.

[3] Ioannou, I., D'Angelo, S. C., Galán-Martín, Á., Pozo, C., Pérez-Ramírez, J., & Guillén-Gosálbez, G. (2021). Process modelling and life cycle assessment coupled with experimental work to shape the future sustainable production of chemicals and fuels. Reaction Chemistry & Engineering, 6(7), 1179-1194.

[4] Baldea, M., Broadbelt, L. J., Ierapetritou, M. G., Li, C., Luo, Z. H., Ma, X., ... & Zhao, D. (2024). 2023 in Retrospective: Trends in Chemical Engineering. Industrial & Engineering Chemistry Research, 63(41), 17419-17429.

[5] Joshi, A. N., & Vaidya, P. D. (2024). Recent studies on aqueous-phase reforming: Catalysts, reactors, hybrid processes and techno-economic analysis. International Journal of Hydrogen Energy, 49, 117-137.

[6] Kim, S., and Bakshi, B. R. (2025). Discovering Net-Zero Chemical Processes and Pathways by Developing Circular Reaction Networks and their Hierarchical Screening. Industrial & Engineering Chemistry Research, under review.