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}
Pub Date : 2025-02-15DOI: 10.1016/j.dche.2025.100221
Ali Nik-Khorasani , Thanh Tung Khuat , Bogdan Gabrys
This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data’s complexity and high dimensionality. We propose a novel Hyperbox Mixture Regression (HMR) model that employs hyperbox-based input space partitioning to enhance predictive accuracy while managing uncertainty inherent in bioprocess data. The HMR model is designed to dynamically generate hyperboxes for input samples in a single-pass process, thereby improving learning speed and reducing computational complexity. Our experimental study utilizes a dataset that contains 106 bioreactors. This study evaluates the model’s performance in predicting critical quality attributes in monoclonal antibody manufacturing over a 15-day cultivation period. The results demonstrate that the HMR model outperforms comparable approximators in accuracy and learning speed and maintains interpretability and robustness under uncertain conditions. These findings underscore the potential of HMR as a powerful tool for enhancing predictive analytics in bioprocessing applications.
{"title":"Hyperbox Mixture Regression for process performance prediction in antibody production","authors":"Ali Nik-Khorasani , Thanh Tung Khuat , Bogdan Gabrys","doi":"10.1016/j.dche.2025.100221","DOIUrl":"10.1016/j.dche.2025.100221","url":null,"abstract":"<div><div>This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data’s complexity and high dimensionality. We propose a novel Hyperbox Mixture Regression (HMR) model that employs hyperbox-based input space partitioning to enhance predictive accuracy while managing uncertainty inherent in bioprocess data. The HMR model is designed to dynamically generate hyperboxes for input samples in a single-pass process, thereby improving learning speed and reducing computational complexity. Our experimental study utilizes a dataset that contains 106 bioreactors. This study evaluates the model’s performance in predicting critical quality attributes in monoclonal antibody manufacturing over a 15-day cultivation period. The results demonstrate that the HMR model outperforms comparable approximators in accuracy and learning speed and maintains interpretability and robustness under uncertain conditions. These findings underscore the potential of HMR as a powerful tool for enhancing predictive analytics in bioprocessing applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100221"},"PeriodicalIF":3.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436459","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}
In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.
{"title":"Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study","authors":"Leila Zeinolabedini , Forough Ameli , Abdolhossein Hemmati-Sarapardeh","doi":"10.1016/j.dche.2025.100220","DOIUrl":"10.1016/j.dche.2025.100220","url":null,"abstract":"<div><div>In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100220"},"PeriodicalIF":3.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143198410","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 : 2025-01-17DOI: 10.1016/j.dche.2025.100218
Hasan Nikkhah , Zahir Aghayev , Amir Shahbazi , Vassilis M. Charitopoulos , Styliani Avraamidou , Burcu Beykal
Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework, a data-driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results indicate that DOMINO-NOMAD consistently achieves superior performance compared to DOMINO-ARGONAUT by identifying lower planning costs and generating more feasible solutions across multiple runs. Overall, this study demonstrates DOMINO’s ability to optimize production targets, meet market demands, and address large-scale EWO problems.
计划和调度是企业范围优化（EWO）的关键组成部分。为了成功地实施EWO，至关重要的是将企业运营视为一个整体决策问题，由不同的相互关联的要素或层次组成，以最有效地利用过程工业中的资源。在操作决策的不同层次中，计划和调度通常是顺序处理的，从而导致不切实际的解决方案。为了解决这一问题，利用双层编程等综合方法同时优化这两层。然而，由于缺乏有效的算法，这种相互依赖的整体公式的双层优化仍然很困难，特别是在处理混合整数非线性规划（MINLP）问题时。本文采用数据驱动优化双级混合整数非线性问题（Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems， DOMINO）框架来解决单领导者单追随者双级混合整数问题，这是一种用于处理单领导者单追随者双级别混合整数问题的数据驱动算法。我们将多米诺应用于多产品甲基丙烯酸甲酯聚合过程的连续生产中，该过程被表述为一个旅行推销员问题，并证明了它在获得近最优保证可行解决方案方面的能力。在此基础上，我们将该策略扩展到解决高维、高约束的非线性原油炼油厂运行问题，这是以前在此背景下尚未解决的问题。我们的研究进一步评估了在DOMINO框架中使用局部NOMAD（通过网格自适应直接搜索的非线性优化）和全局数据驱动优化器ARGONAUT（约束灰盒计算的全局优化算法）的效果，并从解决方案质量和计算费用两方面描述了它们的性能。结果表明，与DOMINO-ARGONAUT相比，DOMINO-NOMAD通过确定更低的规划成本并在多次运行中生成更可行的解决方案，始终具有更优越的性能。总体而言，本研究证明了DOMINO优化生产目标、满足市场需求和解决大规模EWO问题的能力。
{"title":"Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems","authors":"Hasan Nikkhah , Zahir Aghayev , Amir Shahbazi , Vassilis M. Charitopoulos , Styliani Avraamidou , Burcu Beykal","doi":"10.1016/j.dche.2025.100218","DOIUrl":"10.1016/j.dche.2025.100218","url":null,"abstract":"<div><div>Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework, a data-driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results indicate that DOMINO-NOMAD consistently achieves superior performance compared to DOMINO-ARGONAUT by identifying lower planning costs and generating more feasible solutions across multiple runs. Overall, this study demonstrates DOMINO’s ability to optimize production targets, meet market demands, and address large-scale EWO problems.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100218"},"PeriodicalIF":3.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159674","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 : 2025-01-16DOI: 10.1016/j.dche.2025.100219
Xiaojie Li , Mingxue Yan , Xuewen Zhang , Minghao Han , Adrian Wing-Keung Law , Xunyuan Yin
Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data-driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors.
{"title":"Efficient data-driven predictive control of nonlinear systems: A review and perspectives","authors":"Xiaojie Li , Mingxue Yan , Xuewen Zhang , Minghao Han , Adrian Wing-Keung Law , Xunyuan Yin","doi":"10.1016/j.dche.2025.100219","DOIUrl":"10.1016/j.dche.2025.100219","url":null,"abstract":"<div><div>Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data-driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100219"},"PeriodicalIF":3.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159036","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}
Water pollution, worsened by rapid industrialization, poses severe challenges to global water management, particularly in developing countries like India. Conventional water quality monitoring methods, which rely on manual sampling and laboratory analysis are, inadequate for handling the dynamic and real-time nature of industrial wastewater contamination. To address this issue, this research article presents the state-of-the-art IoT-based autonomous real-time monitoring system (APAH), a scalable and frugal solution for industrial wastewater management. APAH integrates multi-parameter sensors to continuously monitor critical water quality parameters such as pH, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), turbidity, and temperature. The system's layered architecture, comprising a sensing layer, edge layer, and application layer, enables data acquisition, processing, and remote access via APAH i.e. developed Android mobile application, respectively. APAH utilizes advanced technologies including, the Internet of Things (IoT) and Machine learning (ML) to provide real-time monitoring and control of wastewater treatment processes. Automated valve controls and real-time alerts enable timely intervention, preventing contamination and ensuring compliance with environmental standards. The system's performance was validated through field tests at four industrial wastewater treatment plants in Maharashtra, India particularly directed towards textile, dairy, and greywater effluents, demonstrating significant improvements in water quality post-treatment. The APAH system offers a promising solution for enhancing industrial wastewater treatment efficiency and ensuring sustainable water resource management. By integrating IoT technologies, real-time monitoring, and predictive analytics, APAH can contribute to addressing the urgent need for effective water quality management in industrial environments, particularly in regions facing acute water scarcity and pollution challenges.
{"title":"APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater","authors":"Nishant Chavhan , Resham Bhattad , Suyash Khot , Shubham Patil , Aditya Pawar , Tejasvi Pawar , Palomi Gawli","doi":"10.1016/j.dche.2025.100217","DOIUrl":"10.1016/j.dche.2025.100217","url":null,"abstract":"<div><div>Water pollution, worsened by rapid industrialization, poses severe challenges to global water management, particularly in developing countries like India. Conventional water quality monitoring methods, which rely on manual sampling and laboratory analysis are, inadequate for handling the dynamic and real-time nature of industrial wastewater contamination. To address this issue, this research article presents the state-of-the-art IoT-based autonomous real-time monitoring system (APAH), a scalable and frugal solution for industrial wastewater management. APAH integrates multi-parameter sensors to continuously monitor critical water quality parameters such as pH, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), turbidity, and temperature. The system's layered architecture, comprising a sensing layer, edge layer, and application layer, enables data acquisition, processing, and remote access via APAH i.e. developed Android mobile application, respectively. APAH utilizes advanced technologies including, the Internet of Things (IoT) and Machine learning (ML) to provide real-time monitoring and control of wastewater treatment processes. Automated valve controls and real-time alerts enable timely intervention, preventing contamination and ensuring compliance with environmental standards. The system's performance was validated through field tests at four industrial wastewater treatment plants in Maharashtra, India particularly directed towards textile, dairy, and greywater effluents, demonstrating significant improvements in water quality post-treatment. The APAH system offers a promising solution for enhancing industrial wastewater treatment efficiency and ensuring sustainable water resource management. By integrating IoT technologies, real-time monitoring, and predictive analytics, APAH can contribute to addressing the urgent need for effective water quality management in industrial environments, particularly in regions facing acute water scarcity and pollution challenges.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100217"},"PeriodicalIF":3.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159062","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 : 2025-01-02DOI: 10.1016/j.dche.2024.100216
Yiming Lang , Michelle Xin Yi Ng , Kai Xiang Yu , Binghui Chen , Peng Chee Tan , Khang Wei Tan , Weng Hoong Lam , Parthiban Siwayanan , Kek Seong Kim , Thomas Shean Yaw Choong , Joon Yoon Ten , Zhen Hong Ban
Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of historical data is required to train the source localization model, as gas dispersion is affected by wind speed and wind direction. Furthermore, sensor placement critically affects precise detection and prediction. This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. The MILP was used to optimize sensor placement, while the ANN model was used to optimize sensor number. The source localization model was again realized by the ANN model with optimized sensors data. The trained model was able to identify the unknown illegal electronic waste treatment locations with 97.22 % accuracy in this study. This method allows for the rapid detection of gas sources, as well as the execution of an emergency response, in line with Sustainable Development Goal Target 3.9.
{"title":"A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations","authors":"Yiming Lang , Michelle Xin Yi Ng , Kai Xiang Yu , Binghui Chen , Peng Chee Tan , Khang Wei Tan , Weng Hoong Lam , Parthiban Siwayanan , Kek Seong Kim , Thomas Shean Yaw Choong , Joon Yoon Ten , Zhen Hong Ban","doi":"10.1016/j.dche.2024.100216","DOIUrl":"10.1016/j.dche.2024.100216","url":null,"abstract":"<div><div>Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of historical data is required to train the source localization model, as gas dispersion is affected by wind speed and wind direction. Furthermore, sensor placement critically affects precise detection and prediction. This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. The MILP was used to optimize sensor placement, while the ANN model was used to optimize sensor number. The source localization model was again realized by the ANN model with optimized sensors data. The trained model was able to identify the unknown illegal electronic waste treatment locations with 97.22 % accuracy in this study. This method allows for the rapid detection of gas sources, as well as the execution of an emergency response, in line with Sustainable Development Goal Target 3.9.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100216"},"PeriodicalIF":3.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160287","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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