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Optimization of Reformer Hydrogen Production Process Based on GA-BP Network

Author: ZuoWeiZuo
Tutor: ZhaoGuoCai
School: Liaoning Technical University
Course: Control Theory and Control Engineering
Keywords: K Hydrogen Reformer Neural Network Genetic Algorithm Control Volume Optimization
CLC: TP273
Type: Master's thesis
Year: 2011
Downloads: 16
Quote: 0
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Abstract


Establishment of large industrial plant optimization model is one of the cutting-edge technologies in process control area due to practical significance in the industry. The stability, robustness and efficiency of the reformer perform a critical role to the H2 generation in hydrogen production processes. Consequently, intelligent control and furnace operation optimization have both theoretical and practical significance.Referring to Liaoyang Petroleum Company refinery hydrogen unit, a soft measurement model based on a genetic algorithm—BP neural network is proposed by analyzing Hydrogen furnace operation and control process and combination of intelligent control theory. On-line measurement of methane content could be achieved by utilizing the soft measurement model. Simulation results illustrate that the soft measurement method has a preferable accuracy. Applying the methane concentration in the production as minimum target, reforming furnace control volume is optimized.This paper applies genetic algorithms and neural networks in the soft measurement of methane gas content, with the deep research of control volume optimization. The model could greatly decrease power consumption and cost, and have potential application to other industrial models.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Automatic control,automatic control system
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