High sulfur natural gas purifying process modeling and optimizing method based on extreme learning machine

Abstract

The invention discloses a high sulfur natural gas purifying process modeling and optimizing method based on an extreme learning machine. The method comprises the steps of determining the input variable of a model; acquiring production process data; preprocessing the production process data; conducting data normalization; conducting data modeling by means of the extreme learning machine to obtain a model of technological operation parameters to H2S and CO2 content; designing a preference function according to two output variables of the extreme learning machine model, and optimizing the input variable by means of the multi-objective genetic algorithm; applying input variable optimal solution sets to the extreme learning machine model in sequence to calculate two output values, namely the content of H2S and the content of CO2, of the model at the moment, comparing the output values with an average sample value, and observing the optimization effect. By the adoption of the method, an accurate and reliable high sulfur natural gas purification and desulfurization industrial process model can be established quickly, the yield of finished gas can be increased on this basis, energy consumption during desulfurization can be reduced, and the method has important practical significance in guiding actual industrial production.

Description

technical field [0001] The invention belongs to the energy-saving and production-increasing technology in the desulfurization production process of high-sulfur natural gas, and relates to a modeling and optimization method for the purification process of high-sulfur natural gas based on an extreme learning machine. Background technique [0002] The industrial process of high-sulfur natural gas is complex, with many process parameters, affected by uncertain factors such as temperature, pressure, flow rate, equipment aging and raw gas processing capacity, and is a typical complex nonlinear dynamic characteristic chemical system. 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 S...

AI Technical Summary

Problems solved by technology

However, there is a mutual constraint relationship between output and energy consumption. The optimization of one of the objectives must be at the expense of the

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