Intelligent solubility estimation of gaseous hydrocarbons in ionic liquids

The research focuses on evaluating how well new solvents attract light hydrocarbons,such as propane,methane,and ethane,in natural gas sweetening units.It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process.To address this challenge,the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network(ML-ANN),Support Vector Machines(SVM),and Least Square Support Vector Machine(LSSVM).The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Shuffled Complex Evolution(SCE).Data on the solubility of propane,methane,and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database.The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids.Among these,the hybrid SVM models perform exceptionally well,with the PSO-SVM hybrid model being particularly efficient computationally.To ensure a comprehensive analysis,different examples of hydrocarbons and their order are included.Additionally,a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis.The results demonstrate the superiority of the AI models,as they outperform traditional thermodynamic models across a wide range of data.In conclusion,this study introduces advanced artificial intelligence algorithms such as ML-ANN,SVM,and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids.The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy,precision,and reliability of these intelligent models.These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.

Electroplating for high aspect ratio vias in PCB manufacturing:enhancement capabilities of acoustic streaming

This paper considers the copper electrodeposit ion processes in microvias and investigates whether the quality of the electroplating process can be improved by acoustic streaming using megasonic transducers placed into a plating cell. The theoretical results show that acoustic streaming does not take place within the micro-via （either through or blind-via＇ s）, however it does help improve cupric ion transport in the area close to the mouth of a via. This replenishment of cupric ions at the mouth of micro-via leads to better quality filling of the micro-via through diffusion compared to basic conditions. Experiments showing the improved quality of the filling of vias are also presented.

高铁隧道无线通信系统中的信道测量与建模综述

高铁的迅速发展为现有的高铁无线通信系统带来了一些新的挑战.一个准确描述隧道信道特性的信道模型,对高铁通信系统的设计和评估意义重大.由于隧道狭长的空间、隧道本身的边界性以及产生的波导效应等,高铁隧道中的信道特性不同于其他的高铁场景.此外,隧道信道的一些特性目前还没有被充分地研究.因此,考虑大尺度和小尺度衰落特性的准确的隧道信道模型是十分必要和重要的.本文全面地综述了已开展的隧道信道测量及采用不同方法建模的隧道信道模型,讨论了未来高铁隧道信道测量及建模的一些研究方向.

Multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes

Catalytic chemical processes such as hydrocracking,gasification and pyrolysis play a vital role in the renewable energy and net zero transition.Due to the complex and non-linear behaviours during operation,catalytic chemical processes require a powerful modelling tool for prediction and optimisation for smart operation,speedy green process routes discovery and rapid process design.However,challenges remain due to the lack of an effective modelling and optimisation toolbox,which requires not only a precise analysis but also a fast optimisation.Here,we propose a hybrid machine learning strategy by embedding the physics-based continuum lumping kinetic model into the data-driven artificial neural network framework.This hybrid model is adopted as the surrogate model in the multi-objective optimisation and demonstrated in the benchmarking of a hydrocracking process.The results show that the novel hybrid surrogate model exhibits the mean square error less than 0.01 by comparing with the physics-based simulation results.This well-trained hybrid model was then integrated with non-dominated-sort genetic algorithm(NSGA-II)as the surrogate model to evaluate and optimise the yield and selectivity of the hydrocracking process.The Pareto front from the multi-objective optimisation was able to identify the trade-off curve between the objective functions which is essential for the decision-making during process design.Our work indicates that adopting the hybrid machine learning strategy as the surrogate model in the multi-objective optimisation is a promising approach in various complex catalytic chemical processes to enable an accurate computation as well as a rapid optimisation.