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Study on the Simulation and Optimization Method for Ethylene Cracking Furnaces

Author: GaoXiaoDan
Tutor: HeXiaoRong
School: Tsinghua University
Course: Chemical Engineering and Technology
Keywords: Ethylene Cracking Furnace Operation Optimization Multi-objective optimization Fuzzy matching Multi - objective evolutionary algorithm
CLC: TQ221.211
Type: PhD thesis
Year: 2008
Downloads: 351
Quote: 4
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Abstract


Ethylene is the most important one of the monomers, and the basis for the whole petrochemical industry. A direct impact on the economic benefits of an ethylene plant in the production of ethylene cracking furnace operation level and technical level. Propylene production in recent years has become a second ethylene plant in addition to ethylene production required to achieve the target. Accurate simulation of ethylene cracking furnace and implemented to optimize the operation of ethylene and propylene production target has a very important significance. Naphtha cracking process, reaction selectivity coefficient z lack of effective estimation method hinder the application of the simulation optimization method in the production of ethylene. Firstly, consider secondary reaction effect z projection methodology. This method can be calculated according to the measured distribution of cleavage product cracking feedstock oil z create standard database having a plurality of samples. Then proposed a fuzzy matching method to predict new feedstock oil z. Demonstrated This fuzzy matching method can accurately estimate the reaction selectivity coefficient naphtha. Full-cycle production operation optimization model and the proposed improvements, simulated annealing, genetic algorithms and sequential quadratic programming method (SQP) were optimized. Efficiency comparison of three methods show that the most efficient sequential quadratic programming method has obvious advantages in computing speed. Proved for complex industrial processes, based on the gradient of the quadratic programming method is a more effective method. This paper proposes a parallel hybrid multi-objective genetic algorithm. The algorithm combines the advantages of multi-objective evolutionary algorithms and linear weighting method to improve the efficiency of multi-objective algorithm, and makes the Pareto extensive significant improvement. This multi-objective optimization methods to solve complex nonlinear constrained efficient, so it can be extended to other areas of the multi-objective optimization problem. According to the actual needs of the production, the establishment of a multi-objective optimization model for ethylene cracking furnace B, propylene, and the solution had Pareto (Pareto solution set), the new hybrid algorithm and NSGA-II method. The comparison of the efficiency of the algorithm proving once again that the new hybrid algorithm has higher efficiency and significantly improve the understand the breadth of the set. Pareto get to choose from a variety of optimized operation program for the ethylene plant. Has been put into use in China, PetroChina Lanzhou Petrochemical Ethylene Cracking Furnace Simulation and Optimization System (EPSOS) Based on the above research developed. The long-term industrial tests showed that this software can not only be used to predict the raw oil product distribution and to optimize the operation of the full cycle obtained taking into account to optimize the operation of the program to increase ethylene and propylene yield. Participation in this research project has passed the organization's acceptance of the China Petroleum and acceptance and fully affirmed EPSOS to improve the level of operation and cost-effectiveness of the ethylene cracker important role.

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CLC: > Industrial Technology > Chemical Industry > Basic Organic Chemistry Industry > The production of aliphatic compounds ( acyclic compounds) > Aliphatic hydrocarbons > Unsaturated lipid hydrocarbon > Monoolefins > Ethylene
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