通过RSM和机器学习的协同应用,利用碳基吸附剂从液体燃料中去除硫的优化和建模。

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Karim Maghfour Sarkarabad, Mohsen Shayanmehr, Ahad Ghaemi
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引用次数: 0

摘要

利用人工神经网络(ANN)和响应面法(RSM)对液体燃料的吸附脱硫进行了研究。通过对温度、浓度、比表面积、燃料/吸附剂和时间五个重要因素的分析,预测了脱硫效果。我们采用径向基函数(RBF)和多层感知器(MLP)算法进行人工神经网络建模。使用Levenberg-Marquardt (Trainlm)算法的最优MLP配置由三个隐藏层组成,分别包含20、17和9个神经元,而最优RBF网络包含43个神经元。MLP网络在30个epoch上的决定系数(R2)为0.98,均方误差(MSE)为0.0028。RBF网络在40个epoch上的R2为0.98,MSE为0.0026。两因素交互设计作为RSM模型的基础,其R2为0.91。使用平均绝对相对偏差对RSM、MLP和RBF模型进行比较,表明人工神经网络模型,特别是RBF模型,比RSM模型产生更准确的预测。结果表明,温度和浓度是影响脱硫效率最显著的两个因素。总体而言,人工神经网络在预测脱硫性能方面优于RSM方法,为优化脱硫工艺提供了更可靠的建模工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning.

Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning.

Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning.

Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning.

This research investigates the adsorption desulfurization of liquid fuels using artificial neural networks (ANN) and response surface methodology (RSM) approaches. The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area, fuel/adsorbent, and time. We employed radial basis function (RBF) and multilayer perceptron (MLP) algorithms for ANN modeling. The optimal MLP configuration, utilizing the Levenberg-Marquardt (Trainlm) algorithm, consisted of three hidden layers with 20, 17, and 9 neurons, respectively, while the optimal RBF network contained 43 neurons. The MLP network's determination coefficient (R2) was 0.98 over 30 epochs, and its mean squared error (MSE) was 0.0028. The RBF network also obtained an R2 of 0.98 and an MSE of 0.0026 over 40 epochs. A two-factor interaction design served as the basis for the RSM model, which produced an R2 of 0.91. A comparison of the RSM, MLP, and RBF models, using the average absolute relative deviation, indicated that the ANN models, particularly the RBF model, produced more accurate predictions than the RSM model. The findings show that temperature and concentration were the two most significant factors influencing sulfur removal efficiency. Overall, artificial neural networks outperformed the RSM approach in predicting desulfurization performance, providing a more reliable modeling tool for optimizing the sulfur removal process.

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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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