Abnormal working condition detection-based high-sulfur natural gas purification process modeling optimization method

Abstract

The invention discloses an abnormal working condition detection-based high-sulfur natural gas purification process modeling optimization method, which comprises the following steps of extracting independent components by utilizing independent component analysis, and computing corresponding SPE (squared prediction error) statistics; then, comparing the SPE statistics with set control limits; judging sample data collected under the abnormal working condition, and rejecting the sample data; establishing a high-sulfur natural gas purification desulfurization process model by taking operating parameters of a purification process as input variables of an extreme learning machine, wherein model output is the content of H2S and CO2 in purified gas; performing optimization on the model structure of the extreme learning machine by adopting particle swarm optimization; different physical quantities, such as energy consumption and yield, are designed under the same measure criterion by physical programming preference functions, and Pareto optimal solutions corresponding to the process operating parameters, the energy consumption and the yield can be realized by MOGA (multi-objective genetic algorithm). According to the abnormal working condition detection-based high-sulfur natural gas purification process modeling optimization method disclosed by the invention, the high-sulfur natural gas purification desulfurization process statistic model is established by utilizing the extreme learning machine of the particle swarm optimization, so that the accuracy of the model is improved; meanwhile, multi-objective optimization of the energy consumption and the yield which conflict with each other is also realized.

Description

technical field [0001] The invention belongs to the intelligent energy-saving and production-increasing technology in the desulfurization production process of high-sulfur natural gas, and relates to a modeling optimization method for high-sulfur natural gas purification process based on detection of abnormal working conditions. Background technique [0002] The industrial process of high-sulfur natural gas is complex, and it is a typical chemical system with complex nonlinear dynamic characteristics. The purification and desulfurization process of high-sulfur natural gas mainly includes the following parts: the main absorption tower MDEA solution absorbs acidic components H2S and CO2, the hydrolysis reactor removes (COS), the regeneration tower MDEA solution circulation regeneration and heat exchange process, and the specific process flow process Such as image 3 shown. How to establish an accurate and reliable industrial process model for purification and desulfurization ...

AI Technical Summary

Problems solved by technology

However, output and energy consumption conflict with each other, and the optimization of one of the objectives must be at the expense of the other, and the units of each objective are often inconsistent, so it is difficult to objectively evaluate the pros and cons of the solutions to the two objective problems

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