Production planning optimization of an ethylene plant considering process operation and energy utilization

Chemically Stable Hafnium-Based Metal-Organic Framework for Highly Efficient C2H6/C2H4 Separation under Humid Conditions

Realization of ethane-selective porous materials for efficient ethane/ethylene (C₂H₆/C₂H₄) separation is an important task in the petrochemical industry. Although a number of C₂H₆-selective adsorbents have been realized, their adsorption capacity and selectivity might be mostly dampened under humid conditions due to structure decomposition or co-adsorption of water vapor. A desired material should have simultaneously high C₂H₆ uptake and selectivity, excellent water stability, and ultralow water adsorption uptake for industrial applications, but such a material is elusive. Herein, we report a chemically stable hafnium-based material (Hf)DUT-52a, featuring the suitable pore apertures and less hydrophilicity for highly efficient C₂H₆/C₂H₄ separation under humid conditions. Gas sorption results reveal that (Hf)DUT-52a exhibits both high ethane adsorption capacity (4.02 mmol g^-1) and C₂H₆/C₂H₄ selectivity (1.9) at 296 K and 1 bar, which are comparable to the majority of the top-performing materials. Most importantly, the less pore hydrophilicity enables (Hf)DUT-52a to exhibit a negligible water uptake of 0.036 g g^-1 before 40% relative humidity (RH), effectively minimizing the impact of humidity on separation capacity. This material thus shows excellent separation capacity even under 40% RH with a high polymer-grade ethylene production capacity up to 8.43 L kg^-1 at ambient conditions, as evidenced by the breakthrough experiments.

Integrated Modeling of Transfer Learning and Intelligent Heuristic Optimization for Steam Cracking Process

The construction and expansion of steam cracking plants and feedstock diversification have resulted in a significant demand for the numerical simulation and optimization of models to achieve molecular refining and intelligent manufacturing. However, the existing models cannot be widely applied in industrial practice because of the high computational expense, time-consumption, and data size requirements. In this paper, a high-performance optimization process, which integrates transfer learning and a heuristic algorithm, is proposed for the optimization of furnaces for various feedstocks. An effective transfer learning structure, based on motif feature of the reaction network, is designed and subsequent product distribution prediction program is compiled. Then a hybrid genetic algorithm and particle swarm optimization method is applied for the coil outlet temperature (COT) curve optimization using the derived prediction model, and the results are obtained for different pricing policies of products. The results are determined based on the weight coefficients of prices for different products, and could be further explained by the yield distribution pattern and reaction mechanism.