Digital Chemical Engineering最新文献

{"title":"Assessment of forward and forward–backward Bayesian filters","authors":"Daniel Martins Silva,&nbsp;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}

{"title":"Polynomial Neural Networks for improved AI transparency: An analysis of their inherent explainability (operational rationale) capabilities","authors":"Donovan Chaffart ,&nbsp;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}

{"title":"Operability for process flowsheet analysis","authors":"Ulysses Guilherme Ferreira ,&nbsp;Sérgio Mauro da Silva Neiro ,&nbsp;Luís Cláudio Oliveira-Lopes ,&nbsp;Thiago Vaz da Costa ,&nbsp;Heleno Bispo ,&nbsp;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}

{"title":"Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures","authors":"Tanuj Karia,&nbsp;Gustavo Chaparro,&nbsp;Benoît Chachuat,&nbsp;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^bD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAM^bD 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}

{"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}

{"title":"Revolutionizing perfume creation: PTD's innovative approach","authors":"Asma Iqbal,&nbsp;Mohammad Amil Bhat,&nbsp;Qazi Muneeb,&nbsp;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}

{"title":"Microwave drying of basil (Ocimum sanctum) leaves with chitosan coating pretreatment: Bibliometric analysis and optimization","authors":"Heri Septya Kusuma,&nbsp;Debora Engelien Christa Jaya,&nbsp;Nafisa Illiyanasafa,&nbsp;Endah Kurniasari,&nbsp;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^-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.</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}

{"title":"Hyperbox Mixture Regression for process performance prediction in antibody production","authors":"Ali Nik-Khorasani ,&nbsp;Thanh Tung Khuat ,&nbsp;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}

{"title":"Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study","authors":"Leila Zeinolabedini ,&nbsp;Forough Ameli ,&nbsp;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}

{"title":"Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems","authors":"Hasan Nikkhah ,&nbsp;Zahir Aghayev ,&nbsp;Amir Shahbazi ,&nbsp;Vassilis M. Charitopoulos ,&nbsp;Styliani Avraamidou ,&nbsp;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}