Digital Chemical Engineering最新文献_第10页

Traveling of multiple salesmen to dynamically changing locations for satisfying multiple goals 多名推销员前往动态变化的地点，以实现多重目标

Digital Chemical Engineering Pub Date : 2024-03-30 DOI: 10.1016/j.dche.2024.100149

Anubha Agrawal, Manojkumar Ramteke

{"title":"Traveling of multiple salesmen to dynamically changing locations for satisfying multiple goals","authors":"Anubha Agrawal, Manojkumar Ramteke","doi":"10.1016/j.dche.2024.100149","DOIUrl":"10.1016/j.dche.2024.100149","url":null,"abstract":"<div><p>Polymer grade scheduling, maritime surveillance, e-food delivery, e-commerce, and military tactics necessitate multiple agents (e.g., extruders, speed boats, salesmen) capable of visiting (or completing) dynamically changing locations (or tasks) in minimum time and distance. This study proposes a novel methodology based on clustering and local heuristic-based evolutionary algorithms to address the dynamic traveling salesman problem (TSP) and the dynamic multi-salesman problem with multiple objectives. The proposed algorithm is evaluated on 11 benchmark TSP problems and large-scale problems with up to 10,000 instances. The results show the superior performance of the proposed methodology called the dynamic two-stage evolutionary algorithm as compared to the dynamic hybrid local search evolutionary algorithm. Furthermore, the algorithm's applicability is illustrated through various scenarios involving up to four salesmen and three objectives with dynamically changing locations. To demonstrate real-world relevance, a maritime surveillance problem employing a helideck monitoring system is solved, wherein the objective is to minimize the patrolling route while visiting faulty vessels that threaten marine vessels. This study provides a general framework of TSP which finds application in several sectors, including planning and scheduling in chemical and manufacturing industries, the defense sector, and the e-commerce sector. Finally, the results showcase the effectiveness of the proposed methodology in solving the dynamic multiobjective, and multiple salesmen problem, which represents a more generalized version of the TSP.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000115/pdfft?md5=475e498f67dd75c5f125ba5c42e19411&pid=1-s2.0-S2772508124000115-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140401922","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}

引用次数: 0

Virtual reality-based bioreactor digital twin for operator training 用于操作员培训的基于虚拟现实的生物反应器数字孪生系统

Digital Chemical Engineering Pub Date : 2024-03-26 DOI: 10.1016/j.dche.2024.100147

Mahmudul Hassan , Gary Montague , Muhammad Zahid Iqbal , Jack Fahey

引用次数: 0

Editorial: Special issue on emerging stars in digital chemical engineering 社论：数字化学工程新星特刊

Digital Chemical Engineering Pub Date : 2024-03-21 DOI: 10.1016/j.dche.2024.100148

Jin Xuan , Jinfeng Liu

引用次数: 0

Experiences with enhancing data sharing in a large disciplinary engineering journal, 加强大型学科工程期刊数据共享的经验、

Digital Chemical Engineering Pub Date : 2024-03-13 DOI: 10.1016/j.dche.2024.100146

David S. Sholl

引用次数: 0

Corrigendum to ‘Ammonia-based green corridors for sustainable maritime transportation’ [Digital Chemical Engineering 6 (2023) 100082] 基于氨的可持续海上运输绿色通道"[数字化学工程 6 (2023) 100082] 更正

Digital Chemical Engineering Pub Date : 2024-03-01 DOI: 10.1016/j.dche.2023.100121

Hanchu Wang, Prodromos Daoutidis, Qi Zhang

引用次数: 0

Robust reduced-order machine learning modeling of high-dimensional nonlinear processes using noisy data 利用噪声数据对高维非线性过程进行稳健的降阶机器学习建模

Digital Chemical Engineering Pub Date : 2024-02-23 DOI: 10.1016/j.dche.2024.100145

Wallace Gian Yion Tan, Ming Xiao, Zhe Wu

{"title":"Robust reduced-order machine learning modeling of high-dimensional nonlinear processes using noisy data","authors":"Wallace Gian Yion Tan, Ming Xiao, Zhe Wu","doi":"10.1016/j.dche.2024.100145","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100145","url":null,"abstract":"<div><p>Autoencoder-based reduced-order machine learning models have been developed for modeling and predictive control of nonlinear chemical processes with high dimensionality such as discretization of reaction–diffusion processes. However, in the presence of data noise, autoencoders may over-fit the training data and subsequently learn an inaccurate low-dimensional representation of the process variables. This leads to an inaccurate prediction model when the models are integrated with model predictive control (MPC). To address this issue, this work develops a novel machine-learning-based reduced-order modeling method by integrating SpectralDense layers into autoencoders and incorporating them with recurrent neural networks. We demonstrate that the new architecture of autoencoders using SpectralDense layers is more robust against over-fitting than conventional autoencoders in the presence of data noise, which improves the prediction accuracy in MPC. A diffusion–reaction process simulation example is used to demonstrate that the robust autoencoders outperform those using conventional layers for reduced-order modeling in predictive control.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"11 ","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000073/pdfft?md5=cdabfdcb0e5c07a2bd798e279578f2c3&pid=1-s2.0-S2772508124000073-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976050","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}

引用次数: 0

-30°C cold start optimization of PEMFC based on a data-driven surrogate model and multi-objective optimization algorithm -基于数据驱动代用模型和多目标优化算法的 PEMFC -30°C 冷启动优化技术

Digital Chemical Engineering Pub Date : 2024-02-01 DOI: 10.1016/j.dche.2024.100144

Fan Zhang , Xiyuan Zhang , Bowen Wang , Haipeng Zhai , Kangcheng Wu , Zixuan Wang , Zhiming Bao , Wanli Tian , Weikang Duan , Bingfeng Zu , Zhengwei Gong , Kui Jiao

{"title":"-30°C cold start optimization of PEMFC based on a data-driven surrogate model and multi-objective optimization algorithm","authors":"Fan Zhang , Xiyuan Zhang , Bowen Wang , Haipeng Zhai , Kangcheng Wu , Zixuan Wang , Zhiming Bao , Wanli Tian , Weikang Duan , Bingfeng Zu , Zhengwei Gong , Kui Jiao","doi":"10.1016/j.dche.2024.100144","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100144","url":null,"abstract":"<div><p>Cold start is a critical operating scenario for the proton exchange membrane fuel cell (PEMFC), particularly in the field of transportation. Under sub-freezing temperatures, the water inside the cell will freeze and obstruct gas flow paths as well as cover catalyst reaction sites, resulting in a failed startup. This study proposes an optimization method for the -30°C cold start of PEMFC based on a data-driven surrogate model to improve cold start performance and reduce irreversible damage to the cell. A validated PEMFC cold start mechanism model is utilized as the basis for developing an extreme learning machine (ELM) based data-driven surrogate model, which is trained using data collected from the mechanism model and has higher computational efficiency compared with the original model. In addition, the NSGA-II multi-objective optimization algorithm is employed to optimize the current loading strategies and operating parameters using the surrogate model as fitness function. The objectives are to enhance the minimum voltage and reduce startup duration time. Moreover, experimental validation confirms the effectiveness of the proposed method. The test results demonstrate that a cold start from -30°C is achieved within 97 s, with the minimum voltage reaching 0.44 V. Notably, there is a reduction in startup time by 26 s and an increase in the minimum voltage by 0.06 V compared to the base case. This study establishes a foundation for researchers to adjust operating settings during cold start based on diverse applications and requirements.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000061/pdfft?md5=e12420b70e8952fb11b8c8b1052f6837&pid=1-s2.0-S2772508124000061-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139710161","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}

引用次数: 0

Accelerated modeling and design of a mixed refrigerant cryogenic process using a data-driven approach 利用数据驱动方法加速混合制冷剂低温工艺的建模和设计

Digital Chemical Engineering Pub Date : 2024-01-30 DOI: 10.1016/j.dche.2024.100143

Hosein Alimardani , Mehrdad Asgari , Roohangiz Shivaee-Gariz , Javad Tamnanloo

{"title":"Accelerated modeling and design of a mixed refrigerant cryogenic process using a data-driven approach","authors":"Hosein Alimardani , Mehrdad Asgari , Roohangiz Shivaee-Gariz , Javad Tamnanloo","doi":"10.1016/j.dche.2024.100143","DOIUrl":"https://doi.org/10.1016/j.dche.2024.100143","url":null,"abstract":"<div><p>Cryogenic processes with mixed refrigerants are prevalent in energy-intensive chemical industries, enhancing energy efficiency while reducing costs and unit size. However, the curse of dimensionality and process design constraints pose significant hurdles for effective screening and optimization. To tackle this, we developed a neural network model for natural gas liquefaction prediction. Trained on an extensive Aspen HYSYS database, our ML model accurately simulates LNG processes, with an impressive <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> test value of 99.63, operating almost ten million times faster than HYSYS. It effectively addresses vital process design constraints, including liquid slugging and temperature cross, crucial for optimization. By integrating the ML model with genetic and Nelder–Mead algorithms, we achieve an 8.9% reduction in total exergy, outperforming Aspen HYSYS within the same time frame. Our study underscores ML’s significance in modeling energy-intensive chemical processes, providing insights into the exergy profile and enabling feature importance analysis.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277250812400005X/pdfft?md5=cb4dcb0cfd121a5b865f2a5c7ff25e37&pid=1-s2.0-S277250812400005X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693908","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}

引用次数: 0

Integrating transfer learning within data-driven soft sensor design to accelerate product quality control 在数据驱动的软传感器设计中整合迁移学习，加速产品质量控制

Digital Chemical Engineering Pub Date : 2024-01-26 DOI: 10.1016/j.dche.2024.100142

Sam Kay , Harry Kay , Max Mowbray , Amanda Lane , Cesar Mendoza , Philip Martin , Dongda Zhang

{"title":"Integrating transfer learning within data-driven soft sensor design to accelerate product quality control","authors":"Sam Kay , Harry Kay , Max Mowbray , Amanda Lane , Cesar Mendoza , Philip Martin , Dongda Zhang","doi":"10.1016/j.dche.2024.100142","DOIUrl":"10.1016/j.dche.2024.100142","url":null,"abstract":"<div><p>The measurement of batch quality indicators in real time operation is plagued with many challenges, hence soft sensing has become a promising solution within industrial research. However, small data has traditionally been a severe problem, hindering the ability to create accurate, reliable soft sensors, especially within industrial research and development for new product formulations. Nevertheless, it is often the case that modelling knowledge is available for a related system. In order to exploit this, we have developed a generalisable transfer learning methodology which takes advantage of previous modelling efforts to accelerate and improve the construction of models for new systems. Specifically, we adapted a recently developed advanced data-driven soft sensing methodology made for an existing process formulation and integrated a feature-based transfer learning approach to facilitate the modelling of two new industrial process systems, each of which containing notable differences to the original. The performance of the transfer soft sensors was tested rigorously and compared to a benchmark approach under different data availability conditions. It was shown that, the proposed transfer mechanism yielded high accuracy, and is robust to small data scenarios, indicating its potential for use in soft sensing of novel systems.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000048/pdfft?md5=c15ad978e84f38981439079c12f32afa&pid=1-s2.0-S2772508124000048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633564","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}

引用次数: 0

Design of microfluidic chromatographs through reinforcement learning 通过强化学习设计微流控色谱仪

Digital Chemical Engineering Pub Date : 2024-01-24 DOI: 10.1016/j.dche.2024.100141

Mohammad Shahab , Raghunathan Rengaswamy

{"title":"Design of microfluidic chromatographs through reinforcement learning","authors":"Mohammad Shahab , Raghunathan Rengaswamy","doi":"10.1016/j.dche.2024.100141","DOIUrl":"10.1016/j.dche.2024.100141","url":null,"abstract":"<div><p>Chromatography is one of the most valuable techniques chemists possess at their disposal, conducive to everything from developing vaccines, food, beverage, and drug testing to catching criminals. The diverse applications allow it to be used for analytical and preparative purposes. On the other hand, droplet microfluidics has significantly evolved from simple droplet generators to complex and integrated tasks through specially designed networks. Microfluidics finds itself at the center of various Lab-on-Chip studies, enabling single-cell analysis, biochemical synthesis, etc. We demonstrate a microfluidic chromatograph machine that can produce an ordered droplet arrangement for a large number of drops. The droplets are sent into the device using a novel methodology where the conventional droplet train is made into smaller batches. The study describes the use of droplet batch methodology and compares it with the traditional droplet train approach. Using this platform, different droplet sequences are sent through the chromatograph, which preferably allows some droplets to exit first while others take a longer time to flow across the chromatograph based on the droplet properties and device design. The droplet sequences contain various drops; however, the type of drops in these sequences is limited to 2. The chromatograph can handle any number of drops in a single machine is enough for handling diverse droplet sequences. The stability of the microfluidic chromatography is also studied by varying the droplet properties and the droplet batch size.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"10 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000036/pdfft?md5=cfa177e155cab5c162a4e0b7e3220cf1&pid=1-s2.0-S2772508124000036-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638475","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}

引用次数: 0