Reconciling ＂Micro＂ and ＂Macro＂ through Meso-Science

In the early 1990s, I was asked to review a manuscript by Profes- sors Jingbai Li and Mooson Kwauk （Chinese Academy of Sciences） in which the energy minimization multiscale （EMMS） concept was introduced. This theory had been presented at various conferences and had been the object of Li＇s PhD thesis.

Energy and AI

Intelligence is an attribute of human beings,it is an understanding,analyzing and decision-making process based on people’s perception of the world.Intelligence is enhanced by the interactions among different human beings and the physical world,and becomes the driving force for the development of our traditional society consisting of a physical space and a human social space.The rules of our traditional society are also formed and developed during such interactions.

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.

The equality, diversity and inclusion in energy and AI: Call for actions

Introduction“Energy and AI”is a cutting-edge,interdisciplinary research area at the interface of two very relevant scientific topics.AI is a fast-growing technology already showing profound impact to the global economy and society.Energy is a sector where drastic changes are urgently needed for the net zero transition and AI will play the enabling role[1]in such transition.

Ethics in Engineering and the Role of Responsible Technology

The idea of writing an Editorial on Responsible Technology sprung from the plenary lecture that I gave at the at The First International Conference on Energy and AI in Tianjin,China,where this Journal was launched.It was January 2020 and it was only a few weeks before a major shock hit the world:nobody could have predicted the approach of a pandemic that would affect the lives of every single person in the world.Has the pandemic changed what I wanted to say in my Editorial?