Modeling, validation and exergy evaluation of a thermally-integrated industrial cryogenic air separation plant in Colombia

Mora, Camilo A.; Orjuela, Álvaro

doi:10.1016/j.cherd.2022.07.005

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The primary modeling approach involves mechanistic modeling based on MESH equations (mass balance equation, phase-equilibrium equation, molar fraction summation equation, and heat-balance equation). The mechanistic modeling research encompasses various scenarios, including standard HIDiCs [6,29], heat-integrated reactive columns [23,30,31], heatintegrated pressure-swing columns [32][33][34], heat-integrated air-separation columns [19,[35][36][37], and more. The core of mechanistic modeling lies in describing the conditions for each tray, involving many differential and algebraic equations.…”

Section: Introductionmentioning

confidence: 99%

Global Modeling of Heat-Integrated Distillation Column Based on Limited Local Measurements

Cong,

Liu,

Ling

2024

Processes

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The heat-integrated distillation column (HIDiC) has more energy-saving potential than conventional distillation columns. However, its nonlinearity and coupling effects pose significant challenges for the online operation of the HIDiC. To overcome these challenges, it becomes necessary to utilize accurate nonlinear models for design optimization or control schemes. Traditional modeling methods require extensive tray information, implying the impractical use of numerous sensors in real-world applications. This paper proposes a modeling approach for the HIDiC based on a limited number of measurements. It only requires the measurement of a finite amount of tray information to construct a global model of the HIDiC. This method serves as an online observer, providing real-time information about the entire column, and also enables the prediction of tray concentration changes. The proposed model forms the basis for developing model-based online monitoring and control schemes. Experimental simulation results demonstrate that the proposed method achieves high accuracy in global observation and prediction for the HIDiC.

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Section: Introductionmentioning

confidence: 99%

Global Modeling of Heat-Integrated Distillation Column Based on Limited Local Measurements

Cong,

Liu,

Ling

2024

Processes

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“…Therefore, the practical applicability of heat integration technology in air separation is further evident [20][21][22]. Currently, some scholars have made progress in the design and optimization of HIASCs [23][24][25][26]. However, due to the unique thermal coupling structure, challenges arise in the dynamic modeling, control scheme design, and other aspects [27,28].…”

Section: Introductionmentioning

confidence: 99%

Reduced-Order Modeling and Control of Heat-Integrated Air Separation Column Based on Nonlinear Wave Theory

Cong,

2023

Processes

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The process of low-temperature air separation consumes a significant amount of energy. Internal heat-integrated distillation technology has considerable energy-saving potential. Therefore, the combination of low-temperature air separation and heat-integrated distillation technology has led to the development of a heat-integrated air separation column (HIASC). Due to the heat integration and the inherent complexity of air separation, the modeling and control of this process poses significant challenges. This paper first introduces the nonlinear wave theory into the HIASC, derives the expression for the velocity of the concentration distribution curve movement and the curve describing function, and then establishes a nonlinear wave model. Compared to the traditional mechanical models, this approach greatly reduces the number of differential equations and variables while ensuring an accurate description of the system characteristics. Subsequently, based on the wave model, a model predictive control scheme is designed for the HIASC. This scheme is compared with two conventional control schemes: PID and a general model control. The simulation results demonstrate that MPC outperforms the other control schemes from the response curves and performance metrics.

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