《China Petroleum Processing and Petrochemical Technology》征订启事

China Petroleum Processing and Petrochemical Technology(中国炼油与石油化工)(ISSN 1008-6234;CN 11-4012/TE)创刊于1999年,季刊,是中国出版的炼油和石油化工方面的第一份英文期刊,由石油化工科学研究院主办,属综合(指导)类科技期刊,报道内容以中国国内信息为主,兼顾世界各地的重要科技动态。

MY WORK FOR UPDATING PETROCHEMICAL CATALYSIS AND PROCESSING

After graduating from Zhejiang University in 1952, I came to work at the Institute of Petroleum Processing (which was renamed the Dalian Institute of Chemical Physics in 1959) under the CAS in Dalian City and served as a research chemist there. In the 1950s,my research interest was mainly concentrated on synthetic liquid fuels. After the discovery of a large oilfield in the Daqing area in 1960, a great impetus was brought to the

Emerging importance of shale gas to both the energy & chemicals landscape

This perspectives article is intended highlight the growing importance and emergence of shale gas as an energy resource and as a source of chemicals. Over the next decades huge amounts of newly discovered deposits of trapped gas are expected to be produced not only in the USA but elsewhere providing a wealth of methane and ethane not only used for energy production, but also for conversion to lower hydrocarbon chemicals. This manuscript seeks to focus on the potential of trapped natural gas around the world. The potential new volumes of trapped gas within shale or other mineral strata coming to the marketplace offer a tremendous opportunity if scientists can invent new, cost effective ways to convert this methane to higher value chemicals. Understanding how to selectively break a single C-H bond in methane while minimizing methane conversion to C02 is critical.

Problems and Strategies in Applications of Advanced Process Control in China

Advanced Process Control （APC） is necessary for oil refining and chemical process in China, but some problems have emerged in the application of APC techniques in this field. This paper discusses the conditions of APC application concerning process design, distributed control system （DCS） choice and regular control. It analyzes the problems and strategies in APC application. Some suggestions are proposed for the enterprise to benefit from APC application.

Energy efficiency and savings analysis with multirate sampling for petrochemical process using convolutional neural network-based transfer learning

Energy efficiency in the petrochemical industry is crucial in reducing energy consumption and environmental impact.An accurate energy efficiency model will provide valuable insight for supporting operational adjustment decisions.In practice,due to inconsistent sampling intervals in the petrochemical industry,the traditional approach for obtaining energy efficiency may be unreliable and difficult to handle these multirate data char-acteristics.Therefore,in this paper,a multi-channel convolutional neural network model integrating a model parameter-based transfer learning approach is proposed to improve the prediction of energy efficiency under inconsistent sampling intervals.The multi-channel structure aims to recognize a different pattern from the dataset by convolving the information along the time dimension.Concurrently,transfer learning allows the model to learn a new pattern of input after the model is fully trained.Finally,the performance for energy ef-ficiency prediction and saving analysis is validated by applying it to the vinyl chloride monomer production case study.The result shows that the proposed model outperformed traditional models and typical convolutional neural network structures in terms of accuracy and reproducibility,with an r-square of 0.97.The utilization of transfer learning prevents a significant drop in performance and enhances adaptability in model learning on real-time energy tracking.Moreover,the energy gap analysis of the prediction result identified a significant energysaving potential,which would decrease annual energy consumption by 7.25%on average and a 5,709-ton reduction in carbon dioxide emissions.

Explainable deep transfer learning for energy efficiency prediction based on uncertainty detection and identification

Energy efficiency is an important aspect of increasing production capacity, minimizing environmental impact, and reducing energy usage in the petrochemical industries. However, in practice, data quality can be degraded by measurement malfunction throughout the operation, leading to unreliable and inaccurate prediction results. Therefore, this paper presents a transfer learning fault detection and identification-energy efficiency predictor (TFDI-EEP) model formulated using long short-term memory. The model aims to predict the energy efficiency of the petrochemical process under uncertainty by using the knowledge gained from the uncertainty detection task to improve prediction performance. The transfer procedure resolves weight initialization by applying partial layer freezing before fine-tuning the additional part of the model. The performance of the proposed model is verified on a wide range of fault variations to thoroughly examine the maximum contribution of faults that the model can tolerate. The results indicate that the TFDI-EEP achieved the highest r-squared and lowest error in the testing step for both the 10% and 20% fault variation datasets compared to other conventional methods. Furthermore, the revelation of interconnection between domains shows that the proposed model can also identify strong fault-correlated features, enhancing monitoring ability and strengthening the robustness and reliability of the model observed by the number of outliers. The transfer parameter improves the prediction performance by 9.86% based on detection accuracy and achieves an r-squared greater than 0.95 on the 40% testing fault variation.