This study presents a robust, parallelized optimization framework for kernel parameter identification that is adaptable to any population balance equation (PBE) formulation and process type. The framework addresses the challenge of incomplete 2D particle size distribution (PSD) measurements in multi-material systems by combining a reduced 2D PSD with complementary 1D datasets. The framework was validated by using noisy synthetic PSD data and evaluating both the error in PSD and kernel values across eight kernel parameters. Hyperparameter and sensitivity analyses provided configuration recommendations and insights into the influence of individual parameters, thus guiding kernel model selection. Incorporating prior knowledge of one kernel parameter (e.g., through multi-scale simulations) mitigated non-unique solutions and enhanced noise tolerance, ultimately improving the framework’s robustness and reliability. A case study based on experimental data from a dispersion process demonstrated the framework’s flexibility and practical relevance. • A robust framework for kernel parameter optimization in 2D PBE models is developed. • A multi-data input strategy improved kernel convergence and accuracy. • Kernel estimation is improved by hyperparameter study and sensitivity analysis. • Pre-determining kernels via multi-scale simulations improves accuracy. • Framework is applicable to experimental data of particle dispersion.

• Lightweight 1D SENet classifies seven flow regimes from raw capacitance signals. • Achieves >85% accuracy in-distribution and >71% under out-of-distribution tests. • Removes need for imaging, multi-sensor arrays, or handcrafted feature extraction. • Compact 12 MB model enables real-time deployment on embedded industrial devices. • Validated with experiments, confusion matrices, PCA, and baseline model comparison. Accurate classification of flow patterns in multiphase systems is pivotal for optimizing fluid transport and enhancing overall system performance. Conventional methods—such as visual inspection, standard video analysis, and high-speed imaging—remain widely used in industrial and laboratory settings. However, these approaches are often constrained by subjective interpretation, limited applicability to non-transparent pipelines, and inconsistent performance under varying operating conditions. To overcome these limitations, this study introduces a novel framework that integrates capacitance sensing with Artificial Intelligence (AI)-driven classification. The proposed methodology employs a one-dimensional Squeeze-and-Excitation Network (1D SENet) to extract and interpret time-series features from raw capacitance signals. Experimental validation demonstrates robust classification accuracies, achieving over 85% on in-distribution datasets and 71% on out-of-distribution scenarios—substantially outperforming traditional techniques. These findings underscore the enhanced generalization and reliability of the proposed system. This work establishes a scalable foundation for real-time flow regime monitoring and predictive analytics, offering transformative potential for intelligent fluid management in complex industrial environments.

When designing a dividing wall column, some decisions regarding the layout of the column cannot be altered once the unit is built, whereas decisions regarding the column’s operation can, to some extent, be adjusted later. During the design phase, both layout and operation can be optimized to achieve an optimal column performance. However, such solutions are tailored to pre-specified process demands. If these demands change later, the physical layout can become suboptimal. Hence, we are interested in design decisions that keep the losses in performance minimal, leading to a column design that is flexible across different demands.In this paper, we present a new methodology to measure flexibility. The approach is based on multi-criteria optimization, where Pareto fronts with fixed design variables and optimized operating variables are compared to an ideal Pareto front that optimizes both the layout and the operation simultaneously. The difference between two such fronts, representing the losses in performance of the fixed layout for a wide range of demands, is measured by a novel flexibility indicator. We apply our methodology to designing a dividing wall column separating an example mixture. A fast computation of the corresponding Pareto fronts is achieved by solving the arising optimization problems using a reduction method based on stage-to-stage calculations.

This work presents a circular exploration of cryoprotective agents (CPAs) for stem cells using computer-aided molecular design approaches that can comprehensively consider compounds. An exploration cycle was developed that consists of the following five steps: setting conditions, computational evaluation, experimental evaluation, verification experiments, and discussions with experts in biotechnology. It aims to discover promising CPA candidate compounds by incorporating domain knowledge through discussions with the experts. The developed cycle can be applied to fields where the required physical properties have not been clearly known. As a result, 1-methylimidazole and pyridazine were selected as promising CPA candidate compounds, which were both heterocyclic amines. Hence, heterocyclic amines could be a stepping-stone toward the future development of CPAs for stem cells. By repeatedly using the exploration cycle, CPA candidate compounds with better cryoprotective effects could be discovered.