Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics

Zhang, Xiang; Zhou, Teng; Ng, Ka Ming

doi:10.1002/aic.17064

Cited by 15 publications

(9 citation statements)

References 56 publications

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“…On the other hand, machine learning (ML) algorithms such as ANN and SVM have been employed to build complex nonlinear GC or QSPR models for different properties such as gas solubility, − surface tension, viscosity, toxicity, , melting point, , and the acid dissociation constants of organic compounds . Besides these, some of ML-based models have been integrated into the computer-aided design method for addressing some optimal design problems such as CO 2 capture , and cosmetic formulation . In addition, these ML-based design problems generally present less complex than those conventional thermodynamic model-based design problems. , …”

Section: Optimal Design Of Il-absmentioning

confidence: 99%

“…61 Besides these, some of ML-based models have been integrated into the computer-aided design method for addressing some optimal design problems such as CO 2 capture 62,63 and cosmetic formulation. 64 In addition, these ML-based design problems generally present less complex than those conventional thermodynamic model-based design problems. 4,7 Most recently, ML-based modeling studies of IL-ABS were carried out in our group and a three-layer artificial neural network (ANN)-group contribution (GC) model was successfully developed to predict the phase equilibria conditions of IL-ABS.…”

Section: ■ Introductionmentioning

confidence: 99%

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Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids

Chen

Meng

Cai

et al. 2021

Ind. Eng. Chem. Res.

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Ionic liquid-based aqueous biphasic systems (IL-ABS) have attracted much attention in both academia and industries due to their superior performance in many applications. In order to better utilize these novel biphasic liquid−liquid systems for recovering hydrophilic ILs from their dilute aqueous solutions, a machine learning (ML)-based ABS design method is proposed for such a purpose in this work. In this method, an ML-based model, i.e., artificial neural network (ANN)-group contribution (GC) model, is employed to predict the phase equilibrium behaviors of IL-ABS. Based on the integration with a computeraided design technique, the optimal IL-ABS is determined by formulating and solving an optimization-based mixed-integer nonlinear programming problem, where the structure of IL-ABS is denoted as the input vector in the ANN-GC model. As a proof of the concept, results of the recovery of 1-butyl-3methylimidazolium chloride ([C 4 mIm][Cl]) and n-butylpyridinium trifluoromethanesulfonate ([C 4 Py][TfO]) from aqueous solutions are presented. The ABS [C 4 mIm][Cl]-H 2 O-(NH 4 ) 2 SO 3 (identified in this work) gives an IL recovery efficiency of 95.0 wt % and a salting-out agent input of 2.36 kg/kg IL recovery, and for the ABS [C 4 mIm][Cl]-H 2 O-K 2 CO 3 (reported in the literature), they are 81.7 and 5.25, respectively. For the second case, our proposed ABS [C 4 Py][TfO]-H 2 O-KH 2 PO 4 gives an IL recovery efficiency of 95.6 wt % and a salting-out agent input of 1.81 kg/kg IL recovery, and for the reported ABS [C 4 Py][TfO]-H 2 O-(NH 4 ) 2 SO 4 , they are 80.6 and 3.16, respectively.

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Section: Optimal Design Of Il-absmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids

Chen

Meng

Cai

et al. 2021

Ind. Eng. Chem. Res.

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“…In addition, in the food industry, where ML techniques find widespread application, the cosmetics and textile industries are also interested in similar ML and/or hybrid approaches to support sensorial analyses. For example, in the field of cosmetics, the authors in [168] employed an ANN-based surrogate model, as part of an integrated optimizationbased cosmetic formulation methodology, including the implementation of mechanistic models and heuristics, to predict the sensorial rating of cosmetic products given their recipes and microstructures. The authors in [151] also used ANN and fuzzy logic for tactile sensory property prediction from the process and structure parameters of knitted fabrics.…”

Section: Support For Sensorial Analysismentioning

confidence: 99%

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

2021

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Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

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“…Although data-driven models have been widely used in chemical and energy engineering, − few studies have been done on applying data-driven models for IL/molecular design. Recently, Liu et al developed a machine learning model to predict molecular σ-profile for designing organic solvents.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Ionic Liquid Design for CO₂ Capture: Molecular Structure Optimization and DFT Verification

Zhang

Wang

Song

et al. 2021

Ind. Eng. Chem. Res.

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To identify optimal ionic liquids (ILs) for CO2 capture, an efficient computer-aided IL design (CAILD) approach is desired. The traditional CAILD methods usually combine an equation of state with the UNIFAC-IL model to calculate gas solubility, which is computationally expensive and sometimes cannot give quantitatively satisfying results. In this contribution, a new CAILD approach is presented for the optimal design of ILs for CO2 capture, where mathematically simple and reliable data-driven models are applied to predict CO2 solubility. The IL design problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with the objective of maximizing the CO2 solubility of ILs under prespecified conditions. Global optimal solutions are successfully obtained due to model simplicity. Moreover, to prevent misleading results led by poor extrapolability of data-driven models, multiple data-driven models are trained from the same experimental solubility database. These models are then adopted in different batches of the MINLP formulation. For each batch, the optimization problem is solved to generate top IL candidates. Only the ILs that repeatedly appear in different batches are considered as reliable solutions falling into the validity domain of the data-driven models. Such a new strategy can effectively enhance design reliability. The CO2 capture performance of the designed ILs is finally confirmed using density functional theory calculations. The applicability of the proposed method is illustrated in a case study of post-combustion carbon capture.

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Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics

Cited by 15 publications

References 56 publications

Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids

Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Data-Driven Ionic Liquid Design for CO₂ Capture: Molecular Structure Optimization and DFT Verification

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Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics

Cited by 15 publications

References 56 publications

Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids

Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Data-Driven Ionic Liquid Design for CO2 Capture: Molecular Structure Optimization and DFT Verification

Contact Info

Product

Resources

About

Data-Driven Ionic Liquid Design for CO₂ Capture: Molecular Structure Optimization and DFT Verification