A new stepwise and piecewise optimization approach for CO 2 pipeline

Zhao, Dongya; Tian, Qunhong; Li, Zhaomin; Zhu, Quanmin

doi:10.1016/j.ijggc.2016.03.005

Cited by 33 publications

(11 citation statements)

References 22 publications

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“…Remark In the first‐step,

D_{i d e a l}

t_{i d e a l}

are selected as the decision variables; in the second‐step,

P_{i n l e t}

and

N_{p u m p}

are the decision variables. Note that the two‐step optimization method can address the NPS effectively . In comparison with, the main advantages of the NINP algorithm can be given as follows: The proposed algorithm can deal not only with a single uncertainty but also multiple uncertainties.…”

Section: Two‐objective Two‐level and Two‐step Optimization Model Formentioning

confidence: 99%

“…Note that the two‐step optimization method can address the NPS effectively . In comparison with, the main advantages of the NINP algorithm can be given as follows: The proposed algorithm can deal not only with a single uncertainty but also multiple uncertainties.

L C_{M} false(D_{v a r}, boldU false)

adapts the pipeline optimization design well to the whole CCUS process, and with the optimization indexes of

L C_{M} false(D_{v a r}, boldU false)

and

L C_{W} false(D_{v a r}, boldU false)

, the pipeline can be designed flexibly.…”

Section: Two‐objective Two‐level and Two‐step Optimization Model Formentioning

confidence: 99%

See 1 more Smart Citation

A nonlinear interval number programming algorithm for CO₂ pipeline transportation design under uncertainties

Tian

Zhao²,

Wang

et al. 2019

Greenhouse Gases

Self Cite

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Carbon capture, utilization and storage (CCUS) technology includes three sub‐systems of CO2 capture: transportation, utilization, and storage. Pipeline transportation is the middle link of CCUS, and its optimization is closely connected with the other two sub‐systems. However, technical and economic parameter uncertainties strongly affect the optimal pipeline cost, which creates a need for flexibility in pipeline design. To solve the flexible optimization design problem in CO2 pipeline transportation, this paper proposes an interval number optimization algorithm. Average levelized cost and system robustness are given as the optimization objectives. A two‐objective, two‐level, two‐step optimization problem is established and solved using a quantum genetic algorithm (QGA). The proposed interval number optimization algorithm makes the optimization process with good decision space, the decision makers can flexibly make decisions based on experimental analysis and subjective preference, and the designed pipeline transportation is flexible and can be combined with the optimization of the other sub‐systems. It can also attain the goal of coordination and unification of CCUS optimization. Numerical studies show that the proposed method can solve the flexible optimization problem effectively in the presence of uncertainties. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…Remark In the first‐step,

D_{i d e a l}

t_{i d e a l}

are selected as the decision variables; in the second‐step,

P_{i n l e t}

and

N_{p u m p}

Section: Two‐objective Two‐level and Two‐step Optimization Model Formentioning

confidence: 99%

L C_{M} false(D_{v a r}, boldU false)

adapts the pipeline optimization design well to the whole CCUS process, and with the optimization indexes of

L C_{M} false(D_{v a r}, boldU false)

and

L C_{W} false(D_{v a r}, boldU false)

, the pipeline can be designed flexibly.…”

Section: Two‐objective Two‐level and Two‐step Optimization Model Formentioning

confidence: 99%

A nonlinear interval number programming algorithm for CO₂ pipeline transportation design under uncertainties

Tian

Zhao²,

Wang

et al. 2019

Greenhouse Gases

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sophisticated modelling methods based on hybrid frameworks [40,41], advanced neural computing models [42][43][44][45][46] and genetic programming frameworks [47][48][49] are required to quantify the multi-physics mechanisms for an improved battery design. There could be another factor such as the sudden mechanical impact (buckling, shearing and compression tests) which can also influence the battery capacity estimation.…”

Section: Interactions Among Multi-physics In Battery Packmentioning

confidence: 99%

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Garg

Vijayaraghavan

Zhang

et al. 2017

Int. J. Energy Res.

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Summary The improvement in the operating range of electric vehicles can be accomplished by robust modelling of the design and optimization of the energy storage capacity of the battery pack system. In this work, the authors have conducted a comprehensive survey on battery modelling methods and identified critical areas of improvement vital for estimating the battery capacity. This work proposes the artificial intelligence approach of automated neural networks search (ANS) in development of the robust battery capacity models for the lithium ion batteries based on the inputs (temperature and discharge rates). The robustness in the models is introduced by incorporating uncertainties in the inputs (the temperature and discharge rates, the architecture of algorithm and the models). The statistical analysis and validation of the models reveal that the models formulated using an ANS approach outperform the response surface regression models with correlation coefficient achieved as high as 0.97. The uncertainty analysis based on normal distribution of the inputs suggests that the models formulated from ANS are least sensitive to change in the input conditions when compared to response surface regression models. The global sensitivity analysis reveals that the temperature is a dominant factor for accurate battery capacity estimation. Copyright © 2017 John Wiley & Sons, Ltd.

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“…The mechanism of the FSW process is complex and involves heat mechanisms [20,21]; therefore, the assumption of the model structure induces ambiguity in the extrapolation ability of the model. An alternative route is to apply advanced optimization tools [22], such as artificial neural networks and its variants [23][24][25], genetic programming (GP) and its variants [26,27], hybrid optimization methods of GP [28,29], and molecular dynamics [30,31]. These methods complement the limitations of RSM.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed

et al. 2016

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Friction stir welding (FSW) process has gained attention in recent years because of its advantages over the conventional fusion welding process. These advantages include the absence of heat formation in the affected zone and the absence of large distortion, porosity, oxidation, and cracking. Experimental investigations are necessary to understand the physical behavior that causes the high tensile strength of welded joints of different metals and alloys. Existing literature indicates that tensile properties exhibit strong dependence on the rotational speed, traverse speed, and axial force of the tool that was used. Therefore, this study introduces the experimental procedure for measuring tensile properties, namely, ultimate tensile strength (UTS) and tensile elongation of the welded AA 7020 Al alloy. Experimental findings suggest that a welded part with high UTS can be achieved at a lower heat input compared with the high heat input condition. A numerical approach based on genetic programming is employed to produce the functional relationships between tensile properties and the three inputs (rotational speed, traverse speed, and axial force) of the FSW process. The formulated models were validated based on the experimental data, using the statistical metrics. The effect of the three inputs on the tensile properties was investigated using 2D and 3D analyses. A high UTS was achieved, including a rotational speed of 1050 r/min and traverse speed of 95 mm/min. The results also indicate that 8 kN axial force should be set prior to the FSW process.

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A new stepwise and piecewise optimization approach for CO 2 pipeline

Cited by 33 publications

References 22 publications

A nonlinear interval number programming algorithm for CO₂ pipeline transportation design under uncertainties

A nonlinear interval number programming algorithm for CO₂ pipeline transportation design under uncertainties

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed

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A new stepwise and piecewise optimization approach for CO 2 pipeline

Cited by 33 publications

References 22 publications

A nonlinear interval number programming algorithm for CO2 pipeline transportation design under uncertainties

A nonlinear interval number programming algorithm for CO2 pipeline transportation design under uncertainties

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed

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Product

Resources

About

A nonlinear interval number programming algorithm for CO₂ pipeline transportation design under uncertainties

A nonlinear interval number programming algorithm for CO₂ pipeline transportation design under uncertainties