{"title":"Empowering Capacitive Devices: Harnessing Transfer Learning for Enhanced Data-Driven Optimization","authors":"Teslim Olayiwola, Revati Kumar, Jose A. Romagnoli","doi":"10.1021/acs.iecr.4c01171","DOIUrl":null,"url":null,"abstract":"Developing data-driven models has found successful applications in engineering tasks, such as material design, process modeling, and process monitoring. In capacitive devices like deionization and supercapacitors, there exists potential for applying this data-driven machine learning (ML) model in optimizing its potential use in energy-efficient separations or energy generation. However, these models are faced with limited datasets, and even in large quantities, the datasets are incomplete, limiting their potential use for successful data-driven modeling. Here, the success of transfer learning in resolving the challenges with limited datasets was exploited. A two-step data-driven ML modeling framework named <i>ImputeNet</i> involving training with ML-imputed datasets and then with clean datasets was explored. Through data imputation and transfer learning, it is possible to develop a data-driven model with acceptable metrics mirroring experimental measurements. By using the model, optimization studies using the genetic algorithm were implemented to analyze the solution under the Pareto optimality. This early insight can be used in the initial stage of experimental measurements to rapidly identify experimental conditions worthy of further investigation. Moreover, we expect that the insights from these results will drive accurate predictive modeling in other fields including healthcare, genomic data analysis, and environmental monitoring with incomplete datasets.","PeriodicalId":39,"journal":{"name":"Industrial & Engineering Chemistry Research","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial & Engineering Chemistry Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1021/acs.iecr.4c01171","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Developing data-driven models has found successful applications in engineering tasks, such as material design, process modeling, and process monitoring. In capacitive devices like deionization and supercapacitors, there exists potential for applying this data-driven machine learning (ML) model in optimizing its potential use in energy-efficient separations or energy generation. However, these models are faced with limited datasets, and even in large quantities, the datasets are incomplete, limiting their potential use for successful data-driven modeling. Here, the success of transfer learning in resolving the challenges with limited datasets was exploited. A two-step data-driven ML modeling framework named ImputeNet involving training with ML-imputed datasets and then with clean datasets was explored. Through data imputation and transfer learning, it is possible to develop a data-driven model with acceptable metrics mirroring experimental measurements. By using the model, optimization studies using the genetic algorithm were implemented to analyze the solution under the Pareto optimality. This early insight can be used in the initial stage of experimental measurements to rapidly identify experimental conditions worthy of further investigation. Moreover, we expect that the insights from these results will drive accurate predictive modeling in other fields including healthcare, genomic data analysis, and environmental monitoring with incomplete datasets.
开发数据驱动模型已成功应用于材料设计、流程建模和流程监控等工程任务中。在去离子和超级电容器等电容式设备中,应用这种数据驱动的机器学习(ML)模型来优化其在高能效分离或能源生产中的潜在应用存在潜力。然而,这些模型面临的数据集有限,即使是大量的数据集也不完整,这限制了它们在成功的数据驱动建模中的潜在用途。在这里,我们利用了迁移学习在解决有限数据集挑战方面的成功经验。我们探索了一个名为 ImputeNet 的两步数据驱动 ML 建模框架,其中包括使用 ML 估算的数据集进行训练,然后使用干净的数据集进行训练。通过数据归因和迁移学习,可以开发出一种数据驱动模型,其可接受的指标与实验测量结果一致。通过使用该模型,利用遗传算法实施了优化研究,以分析帕累托最优下的解决方案。这种早期洞察力可用于实验测量的初始阶段,以快速确定值得进一步研究的实验条件。此外,我们还期望这些结果的见解能推动其他领域的精确预测建模,包括医疗保健、基因组数据分析和不完整数据集的环境监测。
期刊介绍:
ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.