Effects of Acid Dissociation and Ionic Solutions on the Aggregation of 2-Pyrone-4,6-dicarboxylic Acid
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Engineering topics
Li, Jianping
Publications and source records attributed to Li, Jianping.
An Explainable Classification Framework for Determining and Understanding the Suitability of Solvent Extraction for Bioproduct Recovery
Lignocellulosic biomass is an abundant feedstock for producing sustainable fuels and chemicals. However, a key challenge in most biomass utilization strategies is the recovery of products from a dilute, typically aqueous, phase. In this respect, liquid–liquid extraction, which relies on a solvent to transfer a product of interest from one liquid phase to another (solvent-rich) phase, is a technology that can reduce the energy requirements for product recovery. To reduce solvent consumption, liquid–liquid extraction needs to be combined with another separation method (e.g., distillation) to recycle the solvent. Despite the research on solvent extraction, there are limited system-wide methods that allow us to determine when extraction is well suited to carry out a specific separation. Accordingly, we present a classification framework to predict whether extraction, coupled with distillation, is feasible and more economical than distillation. Our framework is based on features such as feed composition, liquid–liquid equilibrium constants, relative volatilities, and solvent price, and leads to trained classifiers that show good prediction accuracy. We further study how specific features influence the suitability of extraction. Furthermore, to showcase the applicability of the framework, we use it to analyze the separation of acetic acid from water.
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Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium
We propose a graph neural network architecture that captures molecular interactions in an explicit manner by combining atomic-level (local) graph convolution and molecular-level (global) message passing through a molecular interaction network.
Qin, Shiyi↗
A parametric approach to identify synergistic domains of process intensification for reactive separation
Process intensification aims to combine multiple tasks within multi-functional units to drastically improve economic, energy or sustainability metrics of a chemical process. Limited work exists to systematically identify the synergistic domains where intensification outperforms its nonintensified counterpart. In this work, we computationally derive the synergistic domains of a reactive separation system. Specifically, we first postulate general models for both intensified and nonintensified systems. We use these models to generate data to train a ReLU-type artifical neural network (ANN). Further, the trained ReLU-NN model is formulated as a multi-parametric mixed-integer linear program (mp-MILP), and the critical regions of this mp-MILP define the synergistic feasible domains of intensification. We have derived these synergistic domains of vapor–liquid equilibrium (VLE)-based reactive separation for several industrial applications. These synergistic domains enable quick screening of properties that favor intensification.
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Adaptive Conformer Sampling for Property Prediction Using the Conductor-like Screening Model for Real Solvents
The valorization of lignocellulose-derived bioproducts requires effective separation from excessive water. Liquid–liquid extraction is a promising low-energy separation technology, but effective extraction requires solvent selection based on the thermodynamic properties of the bioproduct and solvent components. We propose a computational framework for predicting such properties by developing an adaptive conformer selection approach for use with COSMO-RS (conductor-like screening model for real solvents) calculations. In this framework, molecular dynamics simulations are used to generate many molecular structures (conformers) at representative temperatures in varying solvent environments. Conformers are then clustered based on structural metrics in a low-dimensional space and selected using a mixed-integer quadratic programming problem to iteratively insert a sampled conformer. At each iteration, we determine bioproduct properties using COSMO-RS. Here, we demonstrate the capability of the proposed framework on representative bioproducts to show convergence of the adaptive sampling toward experimentally measured properties with fewer calculations than required by random conformer sampling, enabling the improved screening of solvent systems for liquid-phase separation.
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Programmed Supramolecular Assemblies Using Orthogonal Pairs of Heterodimeric Coiled Coil Peptides
Despite recent progress, it remains challenging to program biomacromolecules to assemble into discrete nanostructures with pre-determined sizes and topologies. We report here a novel strategy to address this challenge. By using two orthogonal pairs of heterodimeric coiled coils as the building blocks, we constructed six discrete supramolecular assemblies, each composed of a prescribed number of coiled coil components. Within these assemblies, different coiled coils were connected via end-to-side covalent linkages strategically pre-installed between the non-complementary pairs. The overall topological features of two highly complex assemblies, a “barbell” and a “quadrilateral” form, were characterized experimentally and were in good agreement to the designs. This work expands the design paradigms for peptide-based discrete supramolecular assemblies and will provide a route for de novo fabrication of functional protein materials.
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