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Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algorithms, more accurate uncertainty quantification, smarter and faster data collection, and incorporation of diverse stakeholders into decision-making processes, improving the robustness of engineering and policy designs while focusing on the multifaceted goals and constraints in wicked problems. 

Self-driving laboratories and automated experiments can accelerate the design workflow and decrease errors associated with experiments that characterize membrane transport properties. Within this study, we use 3D printing to design a custom stirred cell that incorporates inline conductivity probes in the retentate and permeate streams. The probes provide a complete trajectory of the salt concentrations as they evolve over the course of an experiment. Here, automated diafiltration experiments are used to characterize the performance of commercial NF90 and NF270 polyamide membranes over a predetermined range of KCl concentrations from 1 to 100 mM. The measurements obtained by the inline conductivity probes are validated using offline post-experiment analyses. Compared to traditional filtration experiments, the probes decrease the amount of time required for an experimentalist to characterize membrane materials by more than 50×and increase the amount of information generated by 100×. Device design principles to address the physical constraints associated with making conductivity measurements in confined volumes are proposed. Overall, the device developed within this study provides a foundation to establish high-throughput, automated membrane characterization techniques. 

Sustainable energy solutions and electrification are driving increased demand for critical minerals. Unfortunately, current mineral processing techniques are resource intensive, use large quantities of hazardous chemicals, and occur at centralized facilities to realize economies of scale. These aspects of existing technologies are at odds with the sustainability goals driving increased demand for critical minerals. Here, we argue that the small footprint and modular nature of membrane technologies position them well to address declining concentrations in ores and brines, the variable feed concentrations encountered in recycling, and the environmental issues associated with current separation processes; thus, membrane technologies provide new sustainable pathways to strengthening resilient critical mineral supply chains. The success of creating circular economies hinges on overcoming diverse barriers across the molecular to infrastructure scales. As such, solving these challenges requires the convergence of research across disciplines rather than isolated innovations. 

The design of polymeric ion pumps that isolate target solutes from complex milieus is examined. The analysis shows that current materials possess the properties needed to fabricate polymeric ions pumps that outperform conventional membrane systems. 

We review recent advances in software platforms for model-based design (MBD) organized in five overarching themes — from (1) simulation to optimization, (2) commercial to open-source, (3) process-centric to multiscale, (4) mechanistic to data-driven, and (5) deterministic to uncertain — illustrated with several recent examples in membrane system design. We posit MBD provides (chemical) engineers with principled frameworks to tackle global grand challenges such as sustainable energy, clean water, and equitable access to healthcare by integrating knowledge across disciplines. As such, we predict MBD software, which has historically focused on engineered systems, will evolve to interact with models for natural and social systems more holistically. Finally, we emphasize the importance of open-source software development, especially by users who become contributors. 

Hybrid (i.e., grey-box) models are a powerful and flexible paradigm for predictive science and engineering. Grey-box models use data-driven constructs to incorporate unknown or computationally intractable phenomena into glass-box mechanistic models. The pioneering work of statisticians Kennedy and O’Hagan introduced a new paradigm to quantify epistemic (i.e., model-form) uncertainty. While popular in several engineering disciplines, prior work using Kennedy–O’Hagan hybrid models focuses on prediction with accurate uncertainty estimates. This work demonstrates computational strategies to deploy Bayesian hybrid models for optimization under uncertainty. Specifically, the posterior distributions of Bayesian hybrid models provide a principled uncertainty set for stochastic programming, chance-constrained optimization, or robust optimization. Through two illustrative case studies, we demonstrate the efficacy of hybrid models, composed of a structurally inadequate glass-box model and Gaussian process bias correction term, for decision-making using limited training data. From these case studies, we develop recommended best practices and explore the trade-offs between different hybrid model architectures. 

Separating azeotropic mixtures of hydrofluorocarbons (HFCs) for reuse and recycle is environmentally and economically imperative. While ionic liquid (IL)-enabled HFC separations show promise, Edisonian trial-and-error screening for the optimal IL entrainer is intractable and expensive. Here, we propose an open-source, equation-oriented modeling framework to rapidly translate HFC/IL solubility data into regressed thermodynamic models which can be used for process design under uncertainty and rapid IL screening. Moreover, we use data science and process systems engineering tools to contemplate which data are the most valuable for IL screening. We find that binary solubility data collected at multiple temperatures is adequate for separation process design, and newly available ternary solubility measurements should be reserved for validation. Additionally, we use uncertainty quantification analyses to show up to 10% experimental error is acceptable for IL screening decisions. Informed by these results, we recommend a multistep workflow for IL screening.