A data‐driven rolling‐horizon online scheduling model for diesel production of a real‐world refinery |
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| Authors: | Cao Cuiwen Gu Xingsheng Xin Zhong |
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| Affiliation: | 1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, , Shanghai, 200237 P.R. China;2. State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, , Shanghai, 200237 P.R. China |
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| Abstract: | A rolling‐horizon optimal control strategy is developed to solve the online scheduling problem for a real‐world refinery diesel production based on a data‐driven model. A mixed‐integer nonlinear programming (MINLP) scheduling model considering the implementation of nonlinear blending quality relations and quantity conservation principles is developed. The data variations which drive the MINLP model come from different sources of certain and uncertain events. The scheduling time horizon is divided into equivalent discrete time intervals, which describe regular production and continuous time intervals which represent the beginning and ending time of expected and unexpected events that are not restricted to the boundaries of discrete time intervals. This rolling‐horizon optimal control strategy ensures the dimension of the diesel online scheduling model can be accepted in industry use. LINGO is selected to be the solution software. Finally, the daily diesel scheduling scheme of one entire month for a real‐world refinery is effectively solved. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1160–1174, 2013 |
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| Keywords: | rolling‐horizon optimal control strategy data‐driven online scheduling diesel production uncertainty |
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