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Predictive mathematical models are a cornerstone of science and engineering. Yet selecting, calibrating, and validating said science‐based models often remains an art in practice. Model‐based design of experiments (MBDoE) provides a systematic framework to maximize information gain from experiments while minimizing time and resource costs. But MBDoE remains limited to niche application areas, in part because practitioners must integrate expertise in statistics, computational optimization, and modeling. To help reduce this barrier, we introduce Pyomo.DOE, an open‐source package for MBDoE. Pyomo.DOE uses a nonlinear sensitivity analysis code k_aug to quickly approximate the Fisher information matrix and leverages a new stochastic programming abstraction. We demonstrate Pyomo.DOE with the first application of MBDoE to fixed‐bed breakthrough experiments, which highlights the power of Pyomo.DOE to quantify the value of experimental modifications a priori for large‐scale partial differential‐algebraic equation (PDAE) models. We also provide a mathematical primer on MBDoE targeted at general chemical engineers. [ABSTRACT FROM AUTHOR]

Model-based design of experiments (MBDoE) is a powerful framework for selecting and calibrating science-based mathematical models from data. This work extends popular MBDoE workflows by proposing a convex mixed integer (non)linear programming (MINLP) problem to optimize the selection of measurements. The solver MindtPy is modified to support calculating the D-optimality objective and its gradient via an external package, SciPy, using the grey-box module in Pyomo. The new approach is demonstrated in two case studies: estimating highly correlated kinetics from a batch reactor and estimating transport parameters in a large-scale rotary packed bed for CO 2 capture. Both case studies show how examining the Pareto-optimal trade-offs between information content measured by A- and D-optimality versus measurement budget offers practical guidance for selecting measurements for scientific experiments. [Display omitted] • Proposes convex MI(N)LP problems to optimize the Fisher information of measurements. • Uses grey-box module in Pyomo for externally evaluated objective function. • Optimizes with highly correlated measurements on kinetics case study. • Demonstrates scalability in CO 2 capture case study. [ABSTRACT FROM AUTHOR]

The sustainable phaseout of high global warming potential hydrofluorocarbon (HFC) refrigerant mixtures requires novel solvents, such as ionic liquids (ILs), for new HFC reuse and recycle technologies. Accurate, predictive, and interpretable thermodynamic models for HFC/IL mixtures are essential for multiscale design schemes aiding this phaseout. Still, there is limited guidance regarding the best thermodynamic model for an HFC/IL system. We propose a rigorous thermodynamic model selection and analysis workflow for HFC/IL mixtures which harnesses data science tools – visualization, nonlinear regression, Akaike information criteria, Fischer information matrix (FIM)-based identifiability and uncertainty analyses, and model-based design of experiments methods – to evaluate the accuracy, predictive capability, and interpretability of a thermodynamic model. The open-source IDAES™ platform facilitates training and comparison of sixteen candidate HFC/IL thermodynamic models, including two cubic equations of state, Peng–Robinson and Soave–Redlich–Kwong, and eight variations on temperature dependence within a classical van der Waals mixing rule. We apply this analysis to models for three HFC/IL systems: HFC-32/[emim][T F 2 N], HFC-125/[emim][T F 2 N], and HFC-32/[bmim][P F 6 ]. For these mixtures, we observe that models with a temperature dependent mixing rule are consistently ranked higher by Akaike information criteria for model selection. However, these models may still have high parameter uncertainty and correlation, indicating that data at multiple temperatures should be obtained. This result differs from the current practice of generating single isotherm dataset for most new HFC/IL mixtures. Additionally, we find that the most valuable experiments are taken at the bounds of composition, temperature (e.g., 273 and 348 K), and pressure (e.g., 1 MPa) measurements. This analysis guides data generation efforts, showing that optimally selected measurements across multiple temperatures are adequate for regressing thermodynamic models for multiscale process design. [ABSTRACT FROM AUTHOR]

Improved characterization techniques, which address knowledge gaps related to the interfacial processes that govern solute–solute selectivity and the performance of membranes in complex multi-component feed streams, are necessary to advance membrane processes. In this study, guided by the tools of data science, a diafiltration apparatus is developed to inform material and process design by rapidly characterizing membrane performance over a broad range of feed solution compositions. The apparatus doses a fixed-concentration diafiltrate solution into a stirred cell to achieve a predetermined change in the retentate concentration. Here, using an 80 mM potassium chloride (KCl) diafiltrate solution, it was shown that membrane performance, within a 5 mM to 80 mM KCl phase space, could be probed five times more quickly with one diafiltration experiment (8 h) than with an experimental campaign using traditional filtration processes (47 h). Additionally, the synergy between data analytics and instrumentation led to the incorporation of an inline conductivity probe that monitored the real-time retentate concentration. This additional information provided key insights to distinguish between the mechanisms that govern membrane separations (e.g., discriminating between adsorption or rejection based separations) and allowed for the membrane transport coefficients to be determined accurately. Ultimately, incorporating the appropriate governing phenomena identified a single set of self consistent transport parameters for commercial NF90 membranes. [Display omitted] • Synergy between data analytics and instrumentation enables innovative device design. • DATA allows for the evaluation of membrane performance over a wide concentration range. • Diafiltration experiments run five times faster than traditional experiments. • An inline conductivity probe directly measures the retentate concentration. • Modeling the appropriate physics identifies a single set of self-consistent parameters. [ABSTRACT FROM AUTHOR]