The technology of producing chemicals directly from crude oil involves the direct catalytic cracking of crude oil into chemical raw materials. This innovative process bypasses traditional atmospheric and vacuum distillation units and hydrogenation units, directly reducing both equipment investment and energy consumption. Consequently, this leads to lower production costs and brings significant economic benefits. The direct conversion method not only streamlines the production process but also minimizes the need for complex infrastructure, making it a more efficient alternative to conventional methods. With the continuous advancement of the national dual carbon goals-aiming to peak carbon dioxide emissions and achieve carbon neutrality-accelerating the development of technology for direct catalytic cracking of crude oil to produce chemicals is of paramount importance. This technology holds the potential to significantly reduce process energy consumption and contribute to carbon emission reduction efforts. By optimizing the cracking process, it is possible to achieve higher yields of desirable chemicals while minimizing the formation of by-products like coke, which are less valuable and contribute to increased emissions. As research on the modeling of technology for producing chemicals directly from crude oil deepens, establishing intelligent models to guide process production becomes increasingly crucial. These models can optimize process operations by fine-tuning parameters in real-time, thereby achieving a balance between economic benefits and environmental sustainability. Intelligent models leverage data-driven insights to predict outcomes and adjust variables dynamically, ensuring that the production process remains efficient and aligned with both economic and environmental targets. This study established a robust process simulation model in Aspen HYSYS based on industrial trial data from direct catalytic cracking of crude oil. Through detailed case analysis, single-factor analysis was conducted on four critical process parameters:preheating temperature, reaction temperature, regeneration temperature, and catalyst equilibrium activity. Each of these parameters played a vital role in determining the efficiency and yield of the cracking process. By systematically varyied these factors, researchers can identify optimal conditions that maximize the production of key chemicals such as ethylene and propylene while minimizing unwanted by-products like coke. To enhance the predictive capabilities of the model, a neural network was implemented using Python programming. This neural network model was trained on a comprehensive dataset derived from the process simulations. The model's ability to predict product distribution under different operating conditions was rigorously tested and validated. Furthermore, a multi-objective optimization algorithm, NSGA-II, was integrated into the deep learning framework. This algorithm focuses on maximizing the yield of low-carbon olefins while minimizing coke production, providing a balanced approach to optimizing the overall process. Compared to traditional optimization methods, the established surrogate model offers higher computational efficiency and faster optimization solution times. It enables the decoupling of multiple operational variables, allowing for more precise control over the process. This real-time optimization capability is particularly beneficial in dynamic production environments where conditions can change rapidly. The optimization results demonstrated notable improvements:coke yield decreased by 0.23%, while the yields of ethylene and propylene increased by 1%. In conclusion, the intelligent agent model developed in this study not only enhances solution efficiency and prediction accuracy but also provides valuable insights for guiding process production. Its application could lead to more sustainable and cost-effective chemical manufacturing processes, aligning with both economic and environmental objectives.

Developing efficient, stable, and inexpensive catalysts for the preferential CO oxidation in H₂ (CO-PROX) over a wide temperature range in the presence of CO₂ and H₂O is indispensable for the hydrogen purification process. Herein, CuO was introduced to the CeMnO₂-supported Pt catalyst to modulate the oxygen activation capacity and provide the available number of active sites in CO-PROX. One part of the CuO species doped into CeO₂ strongly interacts with Ce, thus enhancing the oxygen transfer capacity of the catalyst. The other part of CuO species located on the surface of the catalyst provides extra Cu⁺ sites available for low-temperature CO adsorption. This synergistic interaction with Pt sites further enhances CO and O₂ activation, broadening the temperature window of high activity. The optimal Pt-10CuO/CeMnO₂ catalyst exhibits complete CO conversion (CO/O₂ ratio of 1:1) within the practical temperature range of 130–190 °C, even in the presence of CO₂ and H₂O, and remains stable at 150 °C for 76 h testing without any deactivation. This work will give a novel approach for the design of highly efficient inexpensive catalysts for industrial preferential oxidation of CO in H₂, especially in the presence of CO₂ and H₂O.

With the continuous development of the chemical industry, the concept of advocating green development has become increasingly popular. Glycolic acid (GA), serving as the monomer for biodegradable plastic polyglycolic acid, plays a crucial role in combating plastic pollution and fostering an eco-friendly society. The selective oxidation of ethylene glycol (EG) to produce GA represents a novel green production technology. Controlling reaction parameters to achieve multi-objective optimization of product distribution and direct CO₂ emissions is crucial for scaling up the process. With the advent of the big data era, the integration of the chemical industry with artificial intelligence to achieve engineering scale-up is an important trend. This study proposes a neural network model for production prediction and optimization. The model is trained using experimental data, reaction mechanism data, and physical information, enabling rapid prediction of GA production. After validating with 40% of experimental data and 16% of reaction mechanism data, the model's prediction error was within ±5%, and the linear correlation coefficient R² between the predicted values and actual values was 0.998. Furthermore, this study integrated a multi-objective optimization algorithm based on the model, enabling surrogate optimization of reaction parameters during production. After optimization, the direct CO₂ emissions were reduced by over 99% and overall greenhouse gas emissions were reduced by 4.6%. The research paradigm proposed in this research can offer guidance and technical support for the optimized operation of EG selective oxidation to GA.