Digital Chemical Engineering最新文献

{"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}

{"title":"Efficient data-driven predictive control of nonlinear systems: A review and perspectives","authors":"Xiaojie Li ,&nbsp;Mingxue Yan ,&nbsp;Xuewen Zhang ,&nbsp;Minghao Han ,&nbsp;Adrian Wing-Keung Law ,&nbsp;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}

{"title":"APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater","authors":"Nishant Chavhan ,&nbsp;Resham Bhattad ,&nbsp;Suyash Khot ,&nbsp;Shubham Patil ,&nbsp;Aditya Pawar ,&nbsp;Tejasvi Pawar ,&nbsp;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}

{"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 ,&nbsp;Michelle Xin Yi Ng ,&nbsp;Kai Xiang Yu ,&nbsp;Binghui Chen ,&nbsp;Peng Chee Tan ,&nbsp;Khang Wei Tan ,&nbsp;Weng Hoong Lam ,&nbsp;Parthiban Siwayanan ,&nbsp;Kek Seong Kim ,&nbsp;Thomas Shean Yaw Choong ,&nbsp;Joon Yoon Ten ,&nbsp;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}

{"title":"Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review","authors":"Bastian Oetomo ,&nbsp;Ling Luo ,&nbsp;Yiran Qu ,&nbsp;Michele Discepola ,&nbsp;Sandra E. Kentish ,&nbsp;Sally L. Gras","doi":"10.1016/j.dche.2024.100211","DOIUrl":"10.1016/j.dche.2024.100211","url":null,"abstract":"<div><div>With the rapid growth of the biopharmaceutical sector in recent years, in conjunction with many recent successful developments in machine learning and artificial intelligence, the demand for the sector to shift to Industry 4.0 has emerged. Process Analytical Technology (PAT) makes it possible to monitor and control the manufacturing processes of monoclonal antibodies (mAbs), both in upstream and downstream processing. Despite downstream processing being responsible for approximately 60% of the cost of biological drug production, most of the recent developments focus on its upstream counterpart. This paper investigates existing literature on the application of machine learning and/or process control in downstream processing, with an emphasis on ultrafiltration/diafiltration (UF/DF) via tangential flow filtration (TFF). Literature on the intersection between control systems and machine learning will also be explored.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100211"},"PeriodicalIF":3.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159037","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":"Development of a software architecture for bioprocess modeling","authors":"Priscila Marques da Paz ,&nbsp;Caroline Satye Martins Nakama ,&nbsp;Galo Antonio Carrillo Le Roux","doi":"10.1016/j.dche.2024.100210","DOIUrl":"10.1016/j.dche.2024.100210","url":null,"abstract":"<div><div>Increasing the productivity of a biotechnological process becomes feasible through the development of Process Systems Engineering tools, which integrate experimental data with mathematical modeling. This work aims to develop a software architecture for modeling bioprocesses that is accessible to a multidisciplinary group. To achieve this aim, the software must be thoroughly designed based on an ontology that describes bioprocesses that can be apprehended by researchers from different fields. The ontological representation is carried out using Unified Modeling Language diagrams, whose use is demonstrated by a parameter estimation case study. It is concluded that good software development practices can be provided through the proposed architecture, since it guides simulations and parameter estimations of biotechnological processes in a structured way.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100210"},"PeriodicalIF":3.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159038","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":"Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration","authors":"Peter Jul-Rasmussen ,&nbsp;Mads Stevnsborg ,&nbsp;Xiaodong Liang ,&nbsp;Jakob Kjøbsted Huusom","doi":"10.1016/j.dche.2024.100212","DOIUrl":"10.1016/j.dche.2024.100212","url":null,"abstract":"<div><div>Digital twins are frequently discussed in a bio-manufacturing context, but actual realisations of digital twins are rare. To use digital twin instances, significant investments in digital infrastructure and high-fidelity mathematical models are required. This work presents a real-time implementation of an ensemble hybrid model with incremental learning for predicting dissolved oxygen concentration in a pilot-scale bubble column. A bootstrap-aggregated hybrid modelling framework is applied for constructing an ensemble of 1000 hybrid models using different partitions of the training/validation data, providing a measure of the parameter distributions and prediction uncertainty. Each model in the ensemble hybrid model has the same model structure relying on first-principles material balances and an Artificial Neural Network for prediction of the liquid phase volumetric mass transfer coefficient. Incremental learning is applied, efficiently enabling the model to adapt to new data acquired during runtime. The software implementation follows recent ISO issues using a modular structure allowing for flexible allocation of server resources and an intuitive User-Interface is developed for controlling the application. From a real-time prediction study, the models using incremental learning are found to have superior performance both at normal operating conditions, when interpolating, and when extrapolating compared to using only the pre-trained model.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100212"},"PeriodicalIF":3.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160286","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":"An approach to hybrid modelling in chromatographic separation processes","authors":"Foteini Michalopoulou ,&nbsp;Maria M. Papathanasiou","doi":"10.1016/j.dche.2024.100215","DOIUrl":"10.1016/j.dche.2024.100215","url":null,"abstract":"<div><div>Chromatographic separation process models are described by nonlinear partial differential and algebraic equations, often leading to high computational cost that limits their applicability in real-time applications. To address this, in this work we propose a hybrid modelling approach that integrates artificial neural networks with process knowledge to describe the system nonlinear dynamics. Specifically, the separation isotherm is maintained in its mechanistic form, while the need for spatial discretisation is eliminated, reducing computational effort by 97 % in the open-loop simulation. The resulting hybrid model relies solely on experimentally measurable variables and performs well both in interpolation and extrapolation tests. It is further utilised within a process optimisation framework, for the maximisation of process yield and product purity. The results demonstrate that the hybrid model accurately captures the intricate dynamics of chromatographic separations while providing a computationally efficient alternative, making it an effective tool for development in industrial applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100215"},"PeriodicalIF":3.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159672","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":"Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent","authors":"B.J. Chepkonga ,&nbsp;L. Koech ,&nbsp;R.S. Makomere ,&nbsp;H.L. Rutto","doi":"10.1016/j.dche.2024.100214","DOIUrl":"10.1016/j.dche.2024.100214","url":null,"abstract":"<div><div>In this study, hydrated lime (Ca(OH)₂) sorbent was prepared from industrial brine sludge waste using simple laboratory procedures and utilized in a laboratory-scale spray dry scrubber for desulfurization tests. The effects of key process parameters in spray drying (sorbent particle size, inlet gas phase temperature, and Ca:S ratio) on desulfurization efficiency were investigated using central composite design (CCD). Three machine learning (ML) models, multilayer perceptron (MLP), support vector regressor (SVR), and light gradient boosting machine (LightGBM), were assessed for their output estimation accuracy and compared to the CCD prediction model. The computational framework utilized experimental variables structured by CCD software as input metadata. Model performance was evaluated through generalization and accuracy measurements, including the coefficient of determination (R²), root mean square error (RMSE), mean square error (MSE), and mean squared logarithmic error (MSLE). Analysis of variance revealed that the Ca:S ratio had the most significant influence on SO₂ absorption. A quadratic model correlating the process variables with desulfurization efficiency was developed, yielding an R-squared value of 93.47%. Characterization of the final desulfurization products, particularly using XRD, showed the emergence of new phases such as hannebachite (CaSO₃.0·5H₂O), while FTIR analysis identified unreacted portlandite and calcite. Among the ML models, the MLP demonstrated superior performance over SVR and LightGBM, highlighting its efficacy in extracting and decoding information from the input data. The response surface methodology (RSM) model also proved to be a reliable forecasting tool, indicating its potential as a practical alternative to complex algorithmic computations in scenarios with limited raw data.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100214"},"PeriodicalIF":3.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160284","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}