The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tu...The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is proposed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the "excellent" infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Finally, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.展开更多
基金Supported by the Major State Basic Research Development Program of China(2012CB720500)the National Natural Science Foundation of China(Key Program:U1162202),the National Natural Science Foundation of China(61174118)+2 种基金the National High-Tech Research and Development Program of China(2012AA040307)Shanghai Key Technologies R&D program(12dz1125100)Shanghai Leading Academic Discipline Project(B504)
文摘The scheduling process of cracking furnace feedstock is important in an ethylene plant. In this paper it is described as a constraint optimization problem. The constraints consist of the cycle of operation, maximum tube metal temperature, process time of each feedstock, and flow rate. A modified group search optimizer is proposed to deal with the optimization problem. Double fitness values are defined for every group. First, the factor of penalty function should be changed adaptively by the ratio of feasible and general solutions. Second, the "excellent" infeasible solution should be retained to guide the search. Some benchmark functions are used to evaluate the new algorithm. Finally, the proposed algorithm is used to optimize the scheduling process of cracking furnace feedstock. And the optimizing result is obtained.
