Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

Design,scaling-up,and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience,which is usually time-consuming,high cost,and high risk.Although numerical simulation can reproduce high resolution details of hydrodynamics,thermal transfer,and reaction process in reactors,it is still challenging for industrial reactors due to huge computational cost.In this study,by combining the numerical simulation and artificial intelligence(AI)technology of machine learning(ML),a method is proposed to efficiently predict and optimize the performance of industrial reactors.A gas–solid fluidization reactor for the methanol to olefins process is taken as an example.1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model,and thus,the reactor performance data set is constructed.To develop an efficient reactor performance prediction model influenced by multiple factors,the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique,which has better performance than the methods based on the artificial neural network.Furthermore,the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization problems.Results show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield,while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure drop.The two objectives are thus conflicting,and the practical operations need to be compromised under different circumstance.

Progress and key challenges in catalytic combustion of lean methane

As a primary type of clean energy,methane is also the second most important greenhouse gas after CO_(2)due to the high global warming potential.Large quantities of lean methane(0.1–1.0 vol%)are emitted into the atmosphere without any treatment during coal mine,oil,and natural gas production,thus leading to energy loss and greenhouse effect.In general,it is challenging to utilize lean methane due to its low concentration and flow instability,while catalytic combustion is a vital pathway to realize an efficient utilization of lean methane owing to the reduced emissions of polluting gases(e.g.,NOxand CO)during the reaction.In particular,to efficiently convert lean methane,it necessitates both the designs of highly active and stable heterogeneous catalysts that accelerate lean methane combustion at low temperatures and smart reactors that enable autothermal operation by optimizing heat management.In this review,we discuss the in-depth development,challenges,and prospects of catalytic lean methane combustion technology in various configurations,with particular emphasis on heat management from the point of view of material design combined with reactor configuration.The target is to describe a framework that can correlate the guiding principles among catalyst design,device innovation and system optimization,inspiring the development of groundbreaking combustion technology for the efficient utilization of lean methane.