Pub Date : 2025-03-20DOI: 10.1016/j.dche.2025.100232
Jong Nam Kim , Chun Bae Ma , Hyok Jo , Un Chol Han , Hyon-Tae Pak , Son Il Hong , Ri Myong Kim
In the batch polymerization process, temperature control is generally a challenging task. In this paper, a new switching model predictive control algorithm that can be effectively used for the temperature control of batch polymerization process is developed and its effectiveness is verified by introducing it to industrial batch polyvinyl chloride polymerization process. Firstly, a general analysis of the polymerization process is conducted, and based on this, the reaction starting point is determined. Secondly, a switching model identification method considering the reaction starting point and the reaction heat generated after the reaction starts is proposed. Finally, a switching model predictive control algorithm that determines the optimal manipulated value based on the on-line updated step response model is constructed, and a cascade control system using this algorithm is introduced to the temperature control of batch polyvinyl chloride suspension polymerization process. The results show that the proposed control system can significantly improve temperature control performance (overshoot: 0.2%, root mean square error: 0.3) compared to before introduction (overshoot: 1.1%, root mean square error: 1.2ྟC) .
{"title":"Study on the Switching Model Predictive Control Algorithm in Batch Polymerization Process","authors":"Jong Nam Kim , Chun Bae Ma , Hyok Jo , Un Chol Han , Hyon-Tae Pak , Son Il Hong , Ri Myong Kim","doi":"10.1016/j.dche.2025.100232","DOIUrl":"10.1016/j.dche.2025.100232","url":null,"abstract":"<div><div>In the batch polymerization process, temperature control is generally a challenging task. In this paper, a new switching model predictive control algorithm that can be effectively used for the temperature control of batch polymerization process is developed and its effectiveness is verified by introducing it to industrial batch polyvinyl chloride polymerization process. Firstly, a general analysis of the polymerization process is conducted, and based on this, the reaction starting point is determined. Secondly, a switching model identification method considering the reaction starting point and the reaction heat generated after the reaction starts is proposed. Finally, a switching model predictive control algorithm that determines the optimal manipulated value based on the on-line updated step response model is constructed, and a cascade control system using this algorithm is introduced to the temperature control of batch polyvinyl chloride suspension polymerization process. The results show that the proposed control system can significantly improve temperature control performance (overshoot: 0.2%, root mean square error: 0.3) compared to before introduction (overshoot: 1.1%, root mean square error: 1.2ྟC) .</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100232"},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.dche.2025.100227
Austin Braniff , Sahithi Srijana Akundi , Yuanxing Liu , Beatriz Dantas , Shayan S. Niknezhad , Faisal Khan , Efstratios N. Pistikopoulos , Yuhe Tian
The ongoing digital transformation has created new opportunities for chemical manufacturing with increasing plant interconnectivity and data accessibility. This paper reviews state-of-the-art research developments which offer the potential for real-time process safety and systems decision-making in the digital era. An overview is first presented on online process safety management approaches, including dynamic risk analysis and fault diagnosis/prognosis. Advanced operability and control methods are then discussed to achieve safely optimal operations under uncertainty (e.g., flexibility analysis, safety-aware control, fault-tolerant control). We highlight the connections between systems-based operation and process safety management to achieve operational excellence while proactively reducing potential safety losses. We also review the developments and showcases of digital twins paving the way to actual cyber–physical integration. Outstanding challenges and opportunities are identified such as safe data-driven control, integrated operability, safety and control, cyber–physical demonstration, etc. Toward this direction, we present our ongoing developments of the REal-Time Risk-based Optimization (RETRO) framework for safe and smart process operations.
{"title":"Real-time process safety and systems decision-making toward safe and smart chemical manufacturing","authors":"Austin Braniff , Sahithi Srijana Akundi , Yuanxing Liu , Beatriz Dantas , Shayan S. Niknezhad , Faisal Khan , Efstratios N. Pistikopoulos , Yuhe Tian","doi":"10.1016/j.dche.2025.100227","DOIUrl":"10.1016/j.dche.2025.100227","url":null,"abstract":"<div><div>The ongoing digital transformation has created new opportunities for chemical manufacturing with increasing plant interconnectivity and data accessibility. This paper reviews state-of-the-art research developments which offer the potential for real-time process safety and systems decision-making in the digital era. An overview is first presented on online process safety management approaches, including dynamic risk analysis and fault diagnosis/prognosis. Advanced operability and control methods are then discussed to achieve safely optimal operations under uncertainty (e.g., flexibility analysis, safety-aware control, fault-tolerant control). We highlight the connections between systems-based operation and process safety management to achieve operational excellence while proactively reducing potential safety losses. We also review the developments and showcases of digital twins paving the way to actual cyber–physical integration. Outstanding challenges and opportunities are identified such as safe data-driven control, integrated operability, safety and control, cyber–physical demonstration, etc. Toward this direction, we present our ongoing developments of the REal-Time Risk-based Optimization (RETRO) framework for safe and smart process operations.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100227"},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.dche.2025.100228
Balázs Palotai , Gábor Kis , János Abonyi , Ágnes Bárkányi
Digital Twins (DTs) are transforming industrial processes by providing virtual models that mirror physical systems, enabling real-time monitoring and optimization. A major challenge in DTs in process industry, is maintaining the accuracy of flowsheet simulation models due to changes like equipment degradation and operational shifts. This study proposes a novel surrogate-based approach for the automated calibration of these models, which reduces reliance on manual adjustments and adapts to changes in the physical system. This study leverages surrogate models and particle swarm optimization to incorporate modeling considerations and measurement uncertainties, thereby automating model calibration and reducing manual interventions. In a refinery case study, our approach reduced calibration time for the sour water stripper Hysys model by 80% while maintaining the desired accuracy. These results highlight the method’s potential to enhance flowsheet model accuracy in digital twin systems and to support more robust and adaptable DT applications.
{"title":"Surrogate-based flowsheet model maintenance for Digital Twins","authors":"Balázs Palotai , Gábor Kis , János Abonyi , Ágnes Bárkányi","doi":"10.1016/j.dche.2025.100228","DOIUrl":"10.1016/j.dche.2025.100228","url":null,"abstract":"<div><div>Digital Twins (DTs) are transforming industrial processes by providing virtual models that mirror physical systems, enabling real-time monitoring and optimization. A major challenge in DTs in process industry, is maintaining the accuracy of flowsheet simulation models due to changes like equipment degradation and operational shifts. This study proposes a novel surrogate-based approach for the automated calibration of these models, which reduces reliance on manual adjustments and adapts to changes in the physical system. This study leverages surrogate models and particle swarm optimization to incorporate modeling considerations and measurement uncertainties, thereby automating model calibration and reducing manual interventions. In a refinery case study, our approach reduced calibration time for the sour water stripper Hysys model by 80% while maintaining the desired accuracy. These results highlight the method’s potential to enhance flowsheet model accuracy in digital twin systems and to support more robust and adaptable DT applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100228"},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.dche.2025.100224
Daniel Martins Silva, Argimiro Resende Secchi
This paper investigates a forward–backward filtering approach comprised of forward filters and backward smoothers assimilating estimations of a moving horizon estimation. Those evaluations were carried out for extended, unscented, and cubature combinations of the Kalman filters, besides a particle filter, an ensemble Kalman filter, and a moving horizon estimation. Three simulation scenarios were defined for two nonlinear case studies with different complexity to evaluate the estimation accuracy and computational time under different uncertainty conditions. The backward smoothing was found to degenerate for longer horizons; however, it improved the estimation accuracy with smaller horizons in most simulation scenarios in comparison to the respective filters alone. In addition, the method successfully reduced steady-state estimation bias under model mismatch with a small increase in computational time. The performance of the forward–backward filtering was found to be sensitive to active constraint; however, this drawback does not outweigh the meaningful performance improvements found in this study.
{"title":"Assessment of forward and forward–backward Bayesian filters","authors":"Daniel Martins Silva, Argimiro Resende Secchi","doi":"10.1016/j.dche.2025.100224","DOIUrl":"10.1016/j.dche.2025.100224","url":null,"abstract":"<div><div>This paper investigates a forward–backward filtering approach comprised of forward filters and backward smoothers assimilating estimations of a moving horizon estimation. Those evaluations were carried out for extended, unscented, and cubature combinations of the Kalman filters, besides a particle filter, an ensemble Kalman filter, and a moving horizon estimation. Three simulation scenarios were defined for two nonlinear case studies with different complexity to evaluate the estimation accuracy and computational time under different uncertainty conditions. The backward smoothing was found to degenerate for longer horizons; however, it improved the estimation accuracy with smaller horizons in most simulation scenarios in comparison to the respective filters alone. In addition, the method successfully reduced steady-state estimation bias under model mismatch with a small increase in computational time. The performance of the forward–backward filtering was found to be sensitive to active constraint; however, this drawback does not outweigh the meaningful performance improvements found in this study.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100224"},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.dche.2025.100230
Donovan Chaffart , Yue Yuan
The demand for reliable Artificial Intelligence (AI) models within critical domains such as Chemical Engineering has garnered significant attention towards the use and development of transparent AI methodologies. Nevertheless, the field of AI transparency has received an uneven level of attention, such that crucial aspects like explainability (i.e., the transparency of the AI's operational rationales) have remained understudied. To address this challenge, this study investigates the inherent explainability capabilities of Polynomial Neural Networks (PNNs) for applications within Chemical Engineering. PNNs, which implement higher-order polynomials in lieu of linear expressions within their hidden layer neurons, are inherently nonlinear, and thus do not require an activation function to accurately capture the behavior of a system. Accordingly, these neural networks provide continuous, closed-form algebraic expressions that can be used to ascertain the contributions of individual features in the AI architecture towards the network operational behavior. In order to study this behavior, the PNN method was adopted in this work to capture the relationships of noiseless and noisy data derived according to simple mathematical expressions. The PNN polynomials were then extracted and examined to highlight the insights they provide regarding the system operational rationales. The PNN method was furthermore applied to capture the behavior of a circulating fluidized bed reactor to fully showcase the explainative capability of this method within a Chemical Engineering application. These studies highlight the intrinsic explainability capabilities of PNNs and demonstrated their potential for reliable AI implementations for applications in Chemical Engineering.
{"title":"Polynomial Neural Networks for improved AI transparency: An analysis of their inherent explainability (operational rationale) capabilities","authors":"Donovan Chaffart , Yue Yuan","doi":"10.1016/j.dche.2025.100230","DOIUrl":"10.1016/j.dche.2025.100230","url":null,"abstract":"<div><div>The demand for reliable Artificial Intelligence (AI) models within critical domains such as Chemical Engineering has garnered significant attention towards the use and development of transparent AI methodologies. Nevertheless, the field of AI transparency has received an uneven level of attention, such that crucial aspects like <em>explainability</em> (i.e., the transparency of the AI's operational rationales) have remained understudied. To address this challenge, this study investigates the inherent <em>explainability</em> capabilities of Polynomial Neural Networks (PNNs) for applications within Chemical Engineering. PNNs, which implement higher-order polynomials in lieu of linear expressions within their hidden layer neurons, are inherently nonlinear, and thus do not require an activation function to accurately capture the behavior of a system. Accordingly, these neural networks provide continuous, closed-form algebraic expressions that can be used to ascertain the contributions of individual features in the AI architecture towards the network operational behavior. In order to study this behavior, the PNN method was adopted in this work to capture the relationships of noiseless and noisy data derived according to simple mathematical expressions. The PNN polynomials were then extracted and examined to highlight the insights they provide regarding the system operational rationales. The PNN method was furthermore applied to capture the behavior of a circulating fluidized bed reactor to fully showcase the <em>explainative</em> capability of this method within a Chemical Engineering application. These studies highlight the intrinsic <em>explainability</em> capabilities of PNNs and demonstrated their potential for reliable AI implementations for applications in Chemical Engineering.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100230"},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1016/j.dche.2025.100229
Ulysses Guilherme Ferreira , Sérgio Mauro da Silva Neiro , Luís Cláudio Oliveira-Lopes , Thiago Vaz da Costa , Heleno Bispo , Fernando Vines Lima
Operability establishes the relationship between available input and achievable output sets through a system's mathematical representation. This work aims to develop a Flowsheet Operability analysis for a chemical process using rigorous models in a process simulator. The analysis focuses on a typical Air Separation Unit (ASU) in UniSim® Design (Honeywell) and integrates the simulator with the open-source Python operability tool (Opyrability) developed at West Virginia University. The performed assessment incrementally adds the output space of the process flowsheet units and examines how one group of units output space affects downstream units. The results underscore the importance of Flowsheet Operability analysis and the inclusion of inter-unit operability spaces for efficiently identifying unfavorable operating conditions that traditional Plantwide Operability analysis might overlook.
{"title":"Operability for process flowsheet analysis","authors":"Ulysses Guilherme Ferreira , Sérgio Mauro da Silva Neiro , Luís Cláudio Oliveira-Lopes , Thiago Vaz da Costa , Heleno Bispo , Fernando Vines Lima","doi":"10.1016/j.dche.2025.100229","DOIUrl":"10.1016/j.dche.2025.100229","url":null,"abstract":"<div><div>Operability establishes the relationship between available input and achievable output sets through a system's mathematical representation. This work aims to develop a Flowsheet Operability analysis for a chemical process using rigorous models in a process simulator. The analysis focuses on a typical Air Separation Unit (ASU) in UniSim® Design (Honeywell) and integrates the simulator with the open-source Python operability tool (Opyrability) developed at West Virginia University. The performed assessment incrementally adds the output space of the process flowsheet units and examines how one group of units output space affects downstream units. The results underscore the importance of Flowsheet Operability analysis and the inclusion of inter-unit operability spaces for efficiently identifying unfavorable operating conditions that traditional Plantwide Operability analysis might overlook.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100229"},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.dche.2024.100200
Tanuj Karia, Gustavo Chaparro, Benoît Chachuat, Claire S. Adjiman
The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.
{"title":"Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures","authors":"Tanuj Karia, Gustavo Chaparro, Benoît Chachuat, Claire S. Adjiman","doi":"10.1016/j.dche.2024.100200","DOIUrl":"10.1016/j.dche.2024.100200","url":null,"abstract":"<div><div>The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAM<sup>b</sup>D). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAM<sup>b</sup>D optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the <em>in silico</em> design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100200"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-22DOI: 10.1016/j.dche.2025.100222
Anup Paul
The SMILES notation provides a digital way to represent any chemical structure in the form of a string of ASCII characters, therefore, a preferred data medium for machine learning models. As Chomsky type-2 language, SMILES notation is supported with context-free grammar, raising errors for invalid string arrangements. Numerous efforts have been made to recover chemical structures in invalid SMILES strings. Exploring the flexibility of SMILES notations of real molecules would give critical information related to SMILES string reorganizations and sources of errors. Present study examined the potential for reading SMILES notation from right-to-left, known as dextrosinistral reading, and evaluated the effect of new character combinations on the representative chemical structures. The study developed a set of string operations to reverse the order of characters in the SMILES string while maintaining the context-free grammar of SMILES notation. These operations were tested on SMILES notation of over two hundred natural products, resulting in diverse changes at the chemical structure level, including reverting to the original structure, reconfiguring into an isomeric structure, or generating compounds having valency errors. The DFS-tree profiled the changes in chemical structures from reorganizations of SMILES strings and identified the source of atoms with valence errors. Molecular Mechanics (mm2) calculations showed that a group of newly generated chemical structures has total energy in a range of transition state molecular complexes. While the analyses of machine learning models showed the need for cheminformatics tools, such as RDKit and OpenBabel libraries, to develop modules that can fingerprint the reorganized SMILES strings containing atoms of explicit valences. The outcome of the present study highlighted the diversity and flexibility of SMILES notation, and may provide a new source of data required for developing the cheminformatics functionalities necessary to advance machine learning-based chemical discovery.
{"title":"Dextrosinistral reading of SMILES notation: Investigation into origin of non-sense code from string manipulations","authors":"Anup Paul","doi":"10.1016/j.dche.2025.100222","DOIUrl":"10.1016/j.dche.2025.100222","url":null,"abstract":"<div><div>The SMILES notation provides a digital way to represent any chemical structure in the form of a string of ASCII characters, therefore, a preferred data medium for machine learning models. As Chomsky type-2 language, SMILES notation is supported with context-free grammar, raising errors for invalid string arrangements. Numerous efforts have been made to recover chemical structures in invalid SMILES strings. Exploring the flexibility of SMILES notations of real molecules would give critical information related to SMILES string reorganizations and sources of errors. Present study examined the potential for reading SMILES notation from right-to-left, known as dextrosinistral reading, and evaluated the effect of new character combinations on the representative chemical structures. The study developed a set of string operations to reverse the order of characters in the SMILES string while maintaining the context-free grammar of SMILES notation. These operations were tested on SMILES notation of over two hundred natural products, resulting in diverse changes at the chemical structure level, including reverting to the original structure, reconfiguring into an isomeric structure, or generating compounds having valency errors. The DFS-tree profiled the changes in chemical structures from reorganizations of SMILES strings and identified the source of atoms with valence errors. Molecular Mechanics (mm2) calculations showed that a group of newly generated chemical structures has total energy in a range of transition state molecular complexes. While the analyses of machine learning models showed the need for cheminformatics tools, such as RDKit and OpenBabel libraries, to develop modules that can fingerprint the reorganized SMILES strings containing atoms of explicit valences. The outcome of the present study highlighted the diversity and flexibility of SMILES notation, and may provide a new source of data required for developing the cheminformatics functionalities necessary to advance machine learning-based chemical discovery.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100222"},"PeriodicalIF":3.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-19DOI: 10.1016/j.dche.2025.100223
Asma Iqbal, Mohammad Amil Bhat, Qazi Muneeb, Muazam Javid
The Perfumery Ternary Diagram (PTD) is a powerful tool in perfumery for analyzing perfume mixtures comprising three fragrant components and a solvent base. It combines ternary diagrams with perfume pyramids to swiftly evaluate odor characteristics and composition in the headspace across various concentrations, bypassing time-consuming experimental processes. Using a diffusion model to simulate evaporation, this study utilizes PTDs to track changes in the liquid and gas-liquid interface. Using Python, we calculated the OVs of each component at 25 °C, based on molecular weight, saturated vapor pressure, and odor threshold. The data was processed and visualized in MATLAB, producing PTDs that highlighted the component with the highest OV at any given composition. Furthermore, initially as the mole fraction continues to rise, the percentage decrease in odor value is approximately 11.1 %, indicating a diminishing rate of change. The distribution of odor values is elaborated in the MATLAB diagrams that give a comprehensive representation of how the odor value varies with different compositions. The PTDs were effective in representing the critical role of individual components, making them valuable tools for perfumers and researchers. The PTD analysis revealed that limonene (top note) demonstrated the highest odor value (OV) at concentrations above 60 % within the mixture, while vanillin (base note) maintained stability at lower concentrations, supporting its role as a fixative. These findings validate PTDs as predictive tools, accurately reflecting odor value variations across different fragrance compositions. This study investigates whether Perfumery Ternary Diagrams (PTDs) can reliably predict odor value distributions within perfume mixtures, thus providing a practical and efficient tool for optimizing fragrance compositions.
{"title":"Revolutionizing perfume creation: PTD's innovative approach","authors":"Asma Iqbal, Mohammad Amil Bhat, Qazi Muneeb, Muazam Javid","doi":"10.1016/j.dche.2025.100223","DOIUrl":"10.1016/j.dche.2025.100223","url":null,"abstract":"<div><div>The Perfumery Ternary Diagram (PTD) is a powerful tool in perfumery for analyzing perfume mixtures comprising three fragrant components and a solvent base. It combines ternary diagrams with perfume pyramids to swiftly evaluate odor characteristics and composition in the headspace across various concentrations, bypassing time-consuming experimental processes. Using a diffusion model to simulate evaporation, this study utilizes PTDs to track changes in the liquid and gas-liquid interface. Using Python, we calculated the OVs of each component at 25 °C, based on molecular weight, saturated vapor pressure, and odor threshold. The data was processed and visualized in MATLAB, producing PTDs that highlighted the component with the highest OV at any given composition. Furthermore, initially as the mole fraction continues to rise, the percentage decrease in odor value is approximately 11.1 %, indicating a diminishing rate of change. The distribution of odor values is elaborated in the MATLAB diagrams that give a comprehensive representation of how the odor value varies with different compositions. The PTDs were effective in representing the critical role of individual components, making them valuable tools for perfumers and researchers. The PTD analysis revealed that limonene (top note) demonstrated the highest odor value (OV) at concentrations above 60 % within the mixture, while vanillin (base note) maintained stability at lower concentrations, supporting its role as a fixative. These findings validate PTDs as predictive tools, accurately reflecting odor value variations across different fragrance compositions. This study investigates whether Perfumery Ternary Diagrams (PTDs) can reliably predict odor value distributions within perfume mixtures, thus providing a practical and efficient tool for optimizing fragrance compositions.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100223"},"PeriodicalIF":3.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study optimized microwave drying of Ocimum sanctum (basil) leaves with chitosan coating pretreatment to improve drying efficiency and environmental impact. A bibliometric analysis revealed limited research on microwave-assisted drying methods combined with pretreatments. Using the Box-Behnken Design (BBD) within the Response Surface Methodology (RSM), the study evaluated the effects of drying time, microwave power, basil leaf mass, and chitosan concentration. Results showed that the optimum drying parameters were: drying time of 240 s, microwave power of 264.03 W, basil leaf mass of 14.36 g, and chitosan concentration of 1.39 %. Under these conditions, the moisture removal efficiency reached 61.6184 %, with relative energy consumption of 0.9698 kWh g-1 and CO2 emissions of 0.7758 kg g-1. The findings demonstrate that microwave drying with chitosan coating reduces energy consumption and environmental emissions while maintaining product quality.
本研究优化了壳聚糖包衣预处理罗勒叶微波干燥工艺，提高了干燥效率和对环境的影响。文献计量学分析显示，微波辅助干燥方法与预处理相结合的研究有限。采用响应面法（RSM）中的Box-Behnken设计（BBD），研究了干燥时间、微波功率、罗勒叶质量和壳聚糖浓度的影响。结果表明，最佳干燥参数为：干燥时间240 s，微波功率264.03 W，罗勒叶质量14.36 g，壳聚糖浓度1.39%。在此条件下，除湿效率达到61.6184%，相对能耗为0.9698 kWh g-1， CO2排放量为0.7758 kg g-1。研究结果表明，壳聚糖涂层微波干燥在保持产品质量的同时，降低了能耗和环境排放。
{"title":"Microwave drying of basil (Ocimum sanctum) leaves with chitosan coating pretreatment: Bibliometric analysis and optimization","authors":"Heri Septya Kusuma, Debora Engelien Christa Jaya, Nafisa Illiyanasafa, Endah Kurniasari, Kania Ludia Ikawati","doi":"10.1016/j.dche.2025.100225","DOIUrl":"10.1016/j.dche.2025.100225","url":null,"abstract":"<div><div>This study optimized microwave drying of <em>Ocimum sanctum</em> (basil) leaves with chitosan coating pretreatment to improve drying efficiency and environmental impact. A bibliometric analysis revealed limited research on microwave-assisted drying methods combined with pretreatments. Using the Box-Behnken Design (BBD) within the Response Surface Methodology (RSM), the study evaluated the effects of drying time, microwave power, basil leaf mass, and chitosan concentration. Results showed that the optimum drying parameters were: drying time of 240 s, microwave power of 264.03 W, basil leaf mass of 14.36 g, and chitosan concentration of 1.39 %. Under these conditions, the moisture removal efficiency reached 61.6184 %, with relative energy consumption of 0.9698 kWh g<sup>-1</sup> and CO<sub>2</sub> emissions of 0.7758 kg g<sup>-1</sup>. The findings demonstrate that microwave drying with chitosan coating reduces energy consumption and environmental emissions while maintaining product quality.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"15 ","pages":"Article 100225"},"PeriodicalIF":3.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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