Performance design of a cryogenic air separation unit for variable working conditions using the lumped parameter model

Abstract

Large-scale cryogenic air separation units (ASUs), which are widely used in global petrochemical and semiconductor industries, are being developed with high operating elasticity under variable working conditions. Different from discrete processes in traditional machinery manufacturing, the ASU process is continuous and involves the compression, adsorption, cooling, condensation, liquefaction, evaporation, and distillation of multiple streams. This feature indicates that thousands of technical parameters in adsorption, heat transfer, and distillation processes are correlated and merged into a large-scale complex system. A lumped parameter model (LPM) of ASU is proposed by lumping the main factors together and simplifying the secondary ones to achieve accurate and fast performance design. On the basis of material and energy conservation laws, the piecewise-lumped parameters are extracted under variable working conditions by using LPM. Takagi–Sugeno (T–S) fuzzy interval detection is recursively utilized to determine whether the critical point is detected or not by using different thresholds. Compared with the traditional method, LPM is particularly suitable for “rough first then precise” modeling by expanding the feasible domain using fuzzy intervals. With LPM, the performance of the air compressor, molecular sieve adsorber, turbo expander, main plate-fin heat exchangers, and packing column of a 100000 Nm³ O₂/h large-scale ASU is enhanced to adapt to variable working conditions. The designed value of net power consumption per unit of oxygen production (kW/(Nm³ O₂)) is reduced by 6.45%.

Keywords

performance design / air separation unit (ASU) / lumped parameter model (LPM) / variable working conditions / T–S fuzzy interval detection

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (Grant Nos. 51775494, 51821093, and 51935009), the National Key Research and Development Project (Grant No. 2018YFB1700701), and Zhejiang Key Research and Development Project (Grant No. 2019C01141).

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