Sensitivity analysis and multi-objective optimization of CO 2 CPU process using response surface methodology

Koohestanian, Esmaeil; Samimi, Abdolreza; Mohebbi-Kalhori, Davod; Sadeghi, Jafar

doi:10.1016/j.energy.2017.01.129

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…25 C 3 is determined by the nature of CO 2 itself, in that CO 2 solidification and pipe blockage would occur when the stream temperature is lower than the three-phase freezing point. 25,26 2.2.3. Operating Disturbances.…”

Section: Casementioning

confidence: 99%

“…As a consensus for CO 2 capture technologies, C 1 aims at recovering more CO 2 to meet the environmental requirements and reduce CO 2 emissions to the atmosphere. , C 2 derives from the demand of CO 2 transportation and storage, and this CO 2 quality would realize acceptable energy consumption from compression and separation . C 3 is determined by the nature of CO 2 itself, in that CO 2 solidification and pipe blockage would occur when the stream temperature is lower than the three-phase freezing point. , …”

Section: Self-optimizing Control Structure Designmentioning

confidence: 99%

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Self-Optimizing Control Structure and Dynamic Behavior for CO₂ Compression and Purification Unit in Oxy-fuel Combustion Application

Liu

Jin

Zhao

et al. 2019

Ind. Eng. Chem. Res.

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A CO2 compression and purification unit (CPU) using the phase separation method is a promising option to obtain high-purity CO2 products from oxy-fuel combustion power plants. For such CPU systems, a self-optimizing control method is applied for the first time with the objectives of minimizing energy consumption and economic cost. Although the set of controlled variables is determined identically for self-optimizing control structures based on the targets of specific energy consumption (case 1) and specific economic cost (case 2), their control pairings are different. The optimized control structures are feasible for implementation of the operating targets with low energy and cost penalties. Case 1 would be the most suitable control strategy, because its absolute amplitude of variations for specific energy consumption and specific economic cost are only 14.02% and 10.29% of those for case 2, respectively. The results provide a new solution to achieve energy-efficient and cost-effective operations for oxy-fuel combustion systems.

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Section: Casementioning

confidence: 99%

Section: Self-optimizing Control Structure Designmentioning

confidence: 99%

Self-Optimizing Control Structure and Dynamic Behavior for CO₂ Compression and Purification Unit in Oxy-fuel Combustion Application

Liu

Jin

Zhao

et al. 2019

Ind. Eng. Chem. Res.

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show abstract

“…Con ello se contemplan diferentes variables de respuesta, otorgándoles una jerarquía subjetiva para realizar un análisis simple y determinar la mejor combinación de tratamientos para una variable transformada. En ese orden de ideas, es muy común el uso de RSM junto con la optimización multiobjetivo, puesto que es necesario determinar las mejores condiciones que permitan lograr los objetivos propuestos (Ye, Yang y Liu, 2018;Koohestanian, Samimi, Mohebbi-Kalhori y Sadeghi, 2017). No obstante, puede que la optimización de un objetivo conlleve al empeoramiento del enésimo objetivo.…”

Section: Antecedentesunclassified

“…No obstante, puede que la optimización de un objetivo conlleve al empeoramiento del enésimo objetivo. Como ejemplo, en el sector petrolero es ampliamente utilizada para mejorar los procesos de refinamiento de combustibles y así optimizar el rendimiento de determinados motores (Koohestanian, Samimi, Mohebbi-Kalhori y Sadeghi, 2017;Awad et al, 2017).…”

Section: Antecedentesunclassified

Optimización de la rugosidad y la dureza del acero 12l-14 empleando la metodología de superficie de respuesta

Arenas

Martínez

et al. 2019

Ing. ind.

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Este estudio empleó la técnica de superficie de respuesta para optimizar la dureza y rugosidad del acero 12L-14 a partir del tiempo y la temperatura de austenizado. La aplicación multiobjetivo se realizó a partir de un escalamiento con la función de deseabilidad y ponderación, obtenida con la estrategia del proceso analítico jerárquico AHP. Los resultados muestran que la máxima dureza al someter el material a una temperatura de 846,49 °C durante 48,61 minutos.

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“…Computational simulation is a well-organized and excellent device to comprehend and nd the best possible procedures like energy saving, separation, or capture of carbon dioxide in process engineering area [9][10][11]. One of the separation's true applications is the CO 2 's mole fraction in gas ows like natural gas, air, ue gas, etc., which is very important [12][13][14][15][16]. Many techniques, including machine learning, can be used to model data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

Mansouri

Shahraki

Sadeghi

et al. 2023

Preprint

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There is a general industrial procedure called compression and refining unit to catch CO2 from the flue gases produced during oxyfuel combustion. This research discusses the application of decision trees, Adaboosting, random forests, machines that support vectors, and k-nearest neighbor classifiers and gradient boosting in predicting CO2’s mole fracion from flue gases of oxyfuel’s combustion emitted from the power plant. First of all, a training and test dataset was developed using the different variables. Then, a total of 491 simulations were performed and the mole fraction of CO2 was examined. The anticipated outcomes suggested that six machine learning algorithms that rank performance from excellent to poor, RF, GB, AB, DT, KNN, and SVM can be picked to forecast the mole fraction of CO2. Important features were detected by SHAP and the best algorithm was chosen by cross-validation. Results were shown that The RF algorithm enjoyed a great CO2 mole fraction ability to predict and displayed the very best ability for generalization and most reliable prediction precision among all four with an accuracy of 97%. After that LIME was used to explain the results of the RF algorithm. Out of the various variables studied, the pressure of the multistage compressor had the highest effect on the CO2 mole fraction. These results show that machine learning can be used as a reliable predictor of CO2 performance capture within the CPU process.

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Sensitivity analysis and multi-objective optimization of CO 2 CPU process using response surface methodology

Cited by 12 publications

References 13 publications

Self-Optimizing Control Structure and Dynamic Behavior for CO₂ Compression and Purification Unit in Oxy-fuel Combustion Application

Self-Optimizing Control Structure and Dynamic Behavior for CO₂ Compression and Purification Unit in Oxy-fuel Combustion Application

Optimización de la rugosidad y la dureza del acero 12l-14 empleando la metodología de superficie de respuesta

Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

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Sensitivity analysis and multi-objective optimization of CO 2 CPU process using response surface methodology

Cited by 12 publications

References 13 publications

Self-Optimizing Control Structure and Dynamic Behavior for CO2 Compression and Purification Unit in Oxy-fuel Combustion Application

Self-Optimizing Control Structure and Dynamic Behavior for CO2 Compression and Purification Unit in Oxy-fuel Combustion Application

Optimización de la rugosidad y la dureza del acero 12l-14 empleando la metodología de superficie de respuesta

Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms

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Product

Resources

About

Self-Optimizing Control Structure and Dynamic Behavior for CO₂ Compression and Purification Unit in Oxy-fuel Combustion Application

Self-Optimizing Control Structure and Dynamic Behavior for CO₂ Compression and Purification Unit in Oxy-fuel Combustion Application