The future of sustainable chemistry and process: Convergence of artificial intelligence, data and hardware

Sustainable chemistry for renewable energy generation and green synthesis is a timely research topic with the vision to provide present needs without compromising future generations.In the era of Industry 4.0,sustainable chemistry and process are undergoing a drastic transformation from continuous flow system toward the next level of operations,such as cooperating and coordinating machine,self-decision-making system,autonomous and automatic problem solver by integrating artificial intelligence,data and hardware in the cyber-physical systems.Due to the lack of convergence between the physical and cyber spaces,the open-loop systems are facing challenges such as data isolation,slow cycle time,and insufficient resources management.Emerging researches have been devoted to accelerating these cycles,reducing the time between multistep processes and real-time characterization via additive manufacturing,in-/on-line monitoring,and artificial intelligence.The final goal is to concurrently propose process recipes,flow synthesis,and molecules characterization in sustainable chemical processes,with each step transmitting and receiving data simultaneously.This process is known as‘closing the loop’,which will potentially create a future lab with highly integrated systems,and generate a service-orientated platform for end-to-end synchronization and self-evolving,inverse molecular design,and automatic science discovery.This perspective provides a methodical approach for understanding cyber and physical systems individually,enabled by artificial intelligence and additive manufacturing,respectively,in combination with in-/on-line monitoring.Moreover,the future perspective and key challenges for the development of the closed-loop system in sustainable chemistry and process are discussed.

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.