The Experts below are selected from a list of 19014 Experts worldwide ranked by ideXlab platform
Abdulaziz S Alidi - One of the best experts on this subject based on the ideXlab platform.
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a multiobjective optimization model for the waste management of the Petrochemical Industry
Applied Mathematical Modelling, 1996Co-Authors: Abdulaziz S AlidiAbstract:A multiobjective optimization model based on the goal programming approach is proposed in this paper to assist in the proper management of hazardous waste generated by the Petrochemical Industry. The analytic hierarchy process (AHP), a decision-making approach, incorporating qualitative and quantitative aspects of a problem, is incorporated in the model to prioritize the conflicting goals usually encountered when addressing the waste management problems of the Petrochemical Industry. The application of the model has been illustrated through a numerical example, using hypothetical but representative data.
Bo Wang - One of the best experts on this subject based on the ideXlab platform.
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machine learning based digital twin framework for production optimization in Petrochemical Industry
International Journal of Information Management, 2019Co-Authors: Qingfei Min, Yangguang Lu, Chao Su, Zhiyong Liu, Bo WangAbstract:Abstract Digital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in Petrochemical Industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the Petrochemical Industry. This paper proposes a framework and approaches for constructing a digital twin based on the Petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support Petrochemical and other process manufacturing industries to dynamically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the Petrochemical Industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a Petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the Petrochemical Industry.
Qingfei Min - One of the best experts on this subject based on the ideXlab platform.
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machine learning based digital twin framework for production optimization in Petrochemical Industry
International Journal of Information Management, 2019Co-Authors: Qingfei Min, Yangguang Lu, Chao Su, Zhiyong Liu, Bo WangAbstract:Abstract Digital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in Petrochemical Industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the Petrochemical Industry. This paper proposes a framework and approaches for constructing a digital twin based on the Petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support Petrochemical and other process manufacturing industries to dynamically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the Petrochemical Industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a Petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the Petrochemical Industry.
Sinopec Economics - One of the best experts on this subject based on the ideXlab platform.
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Some Thoughts Concerning Developing Strategies of China's Petrochemical Industry
Petrochemical Industry Trends, 2001Co-Authors: Sinopec EconomicsAbstract:Briefly introducing the status of China's Petrochemical Industry; stressing the analyses of the environmental characteristics of new development stage the Industry faces; finally putting forth suggestions about strategic measures to develop China's Petrochemical Industry.
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Century's Look-back and Prospect on Global Petroleum and Petrochemical Industry
Petrochemical Industry Trends, 2001Co-Authors: Sinopec EconomicsAbstract:The article gives a brief over all review on the course of development of world's petroleum and Petrochemical Industry in the 20th century as well as its achievements; analyses the significant role petroleum and Petrochemical Industry plays in the global economic development, the course of industrialization, and social progress; and briefly summarizes the primary experience of the development of petroleum and Petrochemical Industry in about 100 years. The article finally foresees the development and basic orientation of the 21st century petroleum and Petrochemical Industry from an over all, macro view.
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Countermeasure Suggestions on Protecting the Interests of China's Petrochemical Industry by Applying WTO
Petrochemical Industry Trends, 2001Co-Authors: Sinopec EconomicsAbstract:The article gives an introduction to the kinds and rules of WTO Subsidies and Countervailing Agreement, analyses their impacts on China's Petrochemical Industry and makes a couple of suggestions as to the protection of the interests of China's Petrochemical Industry.
Hongwei Li - One of the best experts on this subject based on the ideXlab platform.
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The Petrochemical Industry Production System Optimizing Simulation Based-on HLA
2010 International Conference on Management and Service Science, 2010Co-Authors: Yanfeng Li, Hongwei LiAbstract:By analyzing the characteristic and the application of distributed interactive simulation (DIS) and high level architecture (HLA) and researching the management of federation object model and data distribution in DIS, A new method is presented to develop Petrochemical Industry distributed interactive simulation adopting HLA technology. A simple Petrochemical Industry is modeled with HLA, the FOM and SOM are developed. The results of the instance research show that HLA plays an important role in developing distributed interactive simulation of complicated distributed system and the method is valid to solve the problem puzzling Petrochemical Industry.