Steady-state and dynamic simulation of gas phase polyethylene process

Gas-phase polyethylene(PE)processes are among the most important methods for PE production.A deeper understanding of the process characteristics and dynamic behavior,such as properties of PE and reactor stability,holds substantial interest for both academic researchers and industries.In this study,both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform,which can be used to study a variety of operation tasks for commercial solution polyethylene processes,such as new product development,process control and real-time optimization.The copolymerization kinetic parameters are fitted by industrial data.Additionally,a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed reactor.The accuracy in predicting melt index and density of the polymer,and the dynamic behavior of the developed models are verified by real plant data.Moreover,the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments,since temperature control is important in industrial production.The results reveal that all four schemes effectively track the setpoint temperature.However,only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Recent advances in chemical adsorption and catalytic conversion materials for Li–S batteries

Owing to their low cost,high energy densities,and superior performance compared with that of Li-ion batteries,Li–S batteries have been recognized as very promising next-generation batteries.However,the commercialization of Li–S batteries has been hindered by the insulation of sulfur,significant volume expansion,shuttling of dissolved lithium polysulfides(Li PSs),and more importantly,sluggish conversion of polysulfide intermediates.To overcome these problems,a state-of-the-art strategy is to use sulfur host materials that feature chemical adsorption and electrocatalytic capabilities for Li PS species.In this review,we comprehensively illustrate the latest progress on the rational design and controllable fabrication of materials with chemical adsorbing and binding capabilities for Li PSs and electrocatalytic activities that allow them to accelerate the conversion of Li PSs for Li–S batteries.Moreover,the current essential challenges encountered when designing these materials are summarized,and possible solutions are proposed.We hope that this review could provide some strategies and theoretical guidance for developing novel chemical anchoring and electrocatalytic materials for high-performance Li–S batteries.