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 int...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.展开更多
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 c...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.展开更多
基金the Engineering and Physical Sciences Research Council(EPSRC)for their financial support through the grant ASPECT supported by the Scottish Manufacturing Institute(SMI)at Heriot-Watt University
文摘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.
基金The work is supported by the PhD studentship provided by the Department of Chemical Engineering,Loughborough University.Jin Xuan would like to acknowledge the support from EPSRC under the grant numbers EP/V042432/1 and EP/V011863/1.
文摘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.
