Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Diagnosis of the Argon Plasma in a PECVD Coating Machine

A Langmuir probe plasma diagnostic system was developed to measure the plasma parameters in a PECVD vacuum coating machine. The plasma was a capacitively coupled plasma （CCP） driven by a radio-frequency （RF） power supply. To avoid the disturbance of radio-frequency field on the Langmuir probe measurement, a passive compensation method was applied. This method allowed the ＇dc＇ component to be passed and measured in the probe circuit. It was found that the electron temperature in the range from 2.7 eV to 6.4 eV decreased with the increase in RF power. The measured plasma density ranged from 8×10^16 m^-3 to 0.85×10^15 m^-3 and increased with the increase in RF power.

Machine learning combined with Langmuir probe measurements for diagnosis of dusty plasma of a positive column

This paper reports the use of machine learning to enhance the diagnosis of a dusty plasma.Dust in a plasma has a large impact on the properties of the plasma.According to a probe diagnostic experiment on a dust-free plasma combined with machine learning,an experiment on a dusty plasma is designed and carried out.Using a specific experimental device,dusty plasma with a stable and controllable dust particle density is generated.A Langmuir probe is used to measure the electron density and electron temperature under different pressures,discharge currents,and dust particle densities.The diagnostic result is processed through a machine learning algorithm,and the error of the predicted results under different pressures and discharge currents is analyzed,from which the law of the machine learning results changing with the pressure and discharge current is obtained.Finally,the results are compared with theoretical simulations to further analyze the properties of the electron density and temperature of the dusty plasma.

Specialized Numerical Control Plasma Cutting Machine

The large thermal cutting equipment——The DHG. CNC numerical control plasma cutting machine is produced by The Ha’erbin Welding & Cutting Equipment Co. It specializes in the precise formation and baiting of nonferrous boards and thin carbon steel plates at a high speed. It avoids the disadvantage of flame cutting, which cannot cut nonferrous and thin steel plates.

A real-time neutron-gamma discriminator based on the support vector machine method for the time-of-flight neutron spectrometer

A new neutron-gamma discriminator based on the support vector machine(SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination(PSD) property. The SVM algorithm is implemented in field programmable gate array(FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

Plasma-enabled electrochemical jet micromachining of chemically inert and passivating material

Electrochemical jet machining(EJM)encounters significant challenges in the microstructuring of chemically inert and passivating materials because an oxide layer is easily formed on the material surface,preventing the progress of electrochemical dissolution.This research demonstrates for the first time a jet-electrolytic plasma micromachining(Jet-EPM)method to overcome this problem.Specifically,an electrolytic plasma is intentionally induced at the jet-material contact area by applying a potential high enough to surmount the surface boundary layer(such as a passive film or gas bubble)and enable material removal.Compared to traditional EJM,introducing plasma in the electrochemical jet system leads to considerable differences in machining performance due to the inclusion of plasma reactions.In this work,the implementation of Jet-EPM for fabricating microstructures in the semiconductor material 4H-SiC is demonstrated,and the machining principle and characteristics of Jet-EPM,including critical parameters and process windows,are comprehensively investigated.Theoretical modeling and experiments have elucidated the mechanisms of plasma ignition/evolution and the corresponding material removal,showing the strong potential of Jet-EPM for micromachining chemically resistant materials.The present study considerably augments the range of materials available for processing by the electrochemical jet technique.

Plasma current tomography for HL-2A based on Bayesian inference

An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma.In this study,plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile.Two different Bayesian probability priors are tried,namely the Conditional Auto Regressive(CAR)prior and the Advanced Squared Exponential(ASE)kernel prior.Compared to the CAR prior,the ASE kernel prior adopts nonstationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters,which can make the shape of the current profile more flexible in space.The results indicate that the ASE prior couples more information,reduces the probability of unreasonable solutions,and achieves higher reconstruction accuracy.

Investigation of the J-TEXT plasma events by k-means clustering algorithm

Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data.

Modelling effect of magnetic field on material removal in dry electrical discharge machining

One of the reasons for increased material removal rate in magnetic field assisted dry electrical discharge machining （EDM） is confinement of plasma due to Lorentz forces. This paper presents a mathematical model to evaluate the effect of external magnetic field on crater depth and diameter in single- and multiple-discharge EDM process. The model incorporates three main effects of the magnetic field, which include plasma confinement, mean free path reduction and pulsating magnetic field effects. Upon the application of an external magnetic field, Lorentz forces that are developed across the plasma column confine the plasma column. Also, the magnetic field reduces the mean free path of electrons due to an increase in the plasma pressure and cycloidal path taken by the electrons between the electrodes. As the mean free path of electrons reduces, more ionization occurs in plasma column and eventually an increase in the current density at the inter-electrode gap occurs. The model results for crater depth and its diameter in single discharge dry EDM process show an error of 9%-10% over the respective experimental values.

基于改进型U-Net的变色油墨血浆判别模型

目的:为了解决因主观判别尺度不一和计算响应过长,在血浆制备过程中易出现疑似溶血血浆误判输出和疑似非溶血血浆医学报废的现象,给患者的生命安全带来极大隐患或产生浪费的问题。方法:研制一种基于深度学习和变色油墨理念的限界法。利用改进型U-Net网络进行图像分割,引入改进型注意力机制、批量归一化和填充模块来解决空间映射关系中存在的估计均值偏移、计算效率低和感受野视场不足的问题,并利用自采样本数据集对该模型进行验证对比。结果:采用变色油墨限界法进行分类判别,在确保血浆样本识别精度为前提的同时,提升了血浆判别的计算效率、降低了判别时间,实验结果评价准确率为99.52%。结论:本研究模型的血浆判别精度优于其他判别模型,有望应用于临床。

基于JEC-FDTD等效循环神经网络的电磁建模和等离子体参数反演

磁化等离子体中的电磁波传播是重要的研究课题,针对特定场景下的电磁等离子耦合问题,进行有效且准确的方程建模与参数求解具有极强的研究价值和挑战性,这是探究电磁波与等离子体复杂非线性相互作用机制的关键。文中设计了一种可用于电磁等离子体正逆向建模的循环神经网络(recurrent neural network,RNN),该网络正向传播过程等价于任意磁倾角情况下的电流密度卷积时域有限差分(current density convolution finite-difference time-domain,JEC-FDTD)方法,因此可以求解给定的电磁建模问题,并易于大规模并行计算。通过构建前向可微模拟过程,JEC-FDTD方法可以使用自动微分技术准确且高效地计算梯度,然后通过训练网络来解决反问题。因此,该方法可以有效利用观测到的时域散射场信号反演重要的等离子体参数。JEC-FDTD方法和RNN相结合,形成了较强的协同效应,使得模型具有可解释性和高效的计算效率,受益于深度学习提供的优化策略和专用硬件支持,可以适用于不同仿真场景下的电磁建模和等离子体参数反演。

机器学习驱动的辉光放电等离子体降解碱性紫16性能研究

为了量化研究辉光放电等离子体(GDEP)降解碱性紫16(BV16)的效率影响因素以进一步提升其降解性能,共收集462条降解实验数据建立数据集,训练并评价了9种回归模型.结果表明,基于梯度提升树(GBDT)的集成学习模型预测性能优异,且以类别型特征提升(CatBoost)算法训练的模型性能最佳(R^(2)=0.988,MAE=2.050%).此外,沙普利加和解释方法(SHAP)对最佳模型的参数影响程度定量解析结果显示,反应时间(43.74%)、初始污染物浓度(23.00%)、氯化钾浓度(15.65%)和平均电流(12.63%)是影响BV16降解效率的关键因素.同时,基于部分依赖图(PDP)提出了参数交互影响优化方案.所建立的CatBoost-SHAP-PDP模型不仅能实现GDEP对BV16降解效果的模拟预测,而且是优化GDEP降解过程变量的有效方法,为GDEP降解染料废水复杂体系领域的建模与应用提供科学依据和技术支持.

基于集成学习的万古霉素血药浓度及不良反应预测研究

目的建立万古霉素血药浓度及不良反应预测的集成学习模型,为万古霉素的个体化用药提供参考。方法采集2021年至2023年湖南师范大学附属长沙医院患者的相关数据。使用逻辑回归、朴素贝叶斯、随机森林、支持向量机、梯度提升决策树、极端梯度提升6种机器学习算法分别建模,同时构造集成学习模型,选择最优特征子集,比较各模型预测能力。结果共纳入205例病例,基于最优特征子集的集成学习模型预测性能最佳。该模型血药浓度预测均方根误差为7.703,平均绝对误差为6.492;不良反应预测准确度为0.951,F_(1)分数为0.750,AUC为0.959,AUPR为0.850。结论基于最优特征子集的集成学习模型可准确预测万古霉素血药浓度及不良反应,为万古霉素的个体化精准用药提供依据,确保万古霉素抗感染治疗的有效性及安全性。

电解放电复合加工技术现状与展望

电解放电复合加工是实现电加工技术创新和突破的重要途径,可通过耦合放电与电解能场,利用其时/空协同效应实现在可加工性、加工精度及表面质量等方面的提升,故在介绍电解放电复合加工技术概念和原理分类的基础上,着重概述该领域近十年来的关键性研究进展和新技术创新,对除电火花-电解组合加工和电弧复合加工之外的各类电解放电复合加工技术的未来发展方向进行了展望,指出该技术的规模化工程应用仍需在性能突破、精确建模、加工过程智能控制、复合加工装备等方面取得突破性进展。

等离子切割技术在油罐拆除中的应用

随着国家科技能力的不断发展,对于不同油罐如何选择适合的切割工艺是迫切需要的问题。本文针对榆林市某油库油罐拆除工程,此工程从油罐构造、施工环境和经济效益多方面考虑,最终选用利用等离子切割技术对油罐进行切割。本文对此工程油罐拆除的方法选用与切割流程进行详细描述,同时对切割过程中等离子切割机与周围环境中存在的安全隐患进行分析并提出防范措施,以期对后续同类型油罐拆除提供更好的切割方案选择。

面向微束等离子选区熔化的在线检测系统

为解决微束等离子选区熔化过程中的自动化检测问题,开发出一套面向微束等离子选区熔化的在线检测系统.该系统以每一层的成形金属为研究对象,基于深度学习目标检测原理,对金属成形表面进行缺陷检测,并根据检测结果控制设备启停,确保每一层成形质量达到要求.同时,通过双目相机与线激光相配合,利用线阵扫描方式对成形表面形貌进行三维重建,并显示在自主开发的可视化软件上,实现缺陷检测和三维重建相结合的检测方案.实验结果表明,该系统能够快速、精确地检测出成形过程中产生的缺陷,实时性强、漏检率低,并可将结果及时反馈到系统,实现成形过程的主动、有效调控.

纵梁数控等离子切割质量研究

为改善纵梁数控等离子切割外观质量,以精益六西格玛DMAIC模型为研究框架,对导致纵梁数控等离子切割外观不合格的影响因素进行分析,建立试验模型,并通过MINITAB软件进行数据分析和结果预测,确定了导致纵梁数控等离子切割外观质量异常的关键因子,制定了纵梁数控等离子切割加工参数设定方案,现场试生产结果表明,优化方案能够有效降低纵梁数控等离子切割外观的不合格率,提升了重型载货汽车车架纵梁的生产质量。

等离子切割机在逆变焊割设备中的性能优化与应用

随着工业技术的不断进步和发展,逆变焊割设备在各行业中的应用日益广泛。作为逆变焊割设备的重要组成部分,等离子切割机在金属切割过程中具有独特的优势。通过优化等离子切割机的性能,可以提高设备的切割效率和质量,降低能源消耗和生产成本。以上海烽火焊机有限公司为例,对等离子切割机在逆变焊割设备中的性能优化与应用进行研究,探讨其对逆变焊割设备的影响和作用。