An optimization-simulation model for a simple LNG process

Aspelund, Audun; Gundersen, Truls; Myklebust, J.; Nowak, Matthias; Tomasgård, Asgeir

doi:10.1016/j.compchemeng.2009.10.018

Cited by 195 publications

(149 citation statements)

References 11 publications

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“…The remaining fixed condition specifications are given in Table 2. The cases 1 -2 and 3 -4 in the present work correspond to cases 1 -2 and 4 -5, respectively, in Aspelund et al (2010). Case 2 and 4 have an additional constraint for the maximum value for UA (48.6 MW/°C) compared with Case 1 and 3, respectively.…”

Section: Optimization Specificationsmentioning

confidence: 99%

mentioning

confidence: 99%

“…Results of the present work will be compared with Aspelund et al (2010) and Jensen and Skogestad (2008). Aspelund et al (2010) reported a setup for the PRICO process where the simulations were performed with Aspen HYSYS. The results from this work should be directly comparable with the results from Aspelund et al (2010) since the same process simulator has been used.…”

mentioning

confidence: 99%

“…Aspelund et al (2010) reported a setup for the PRICO process where the simulations were performed with Aspen HYSYS. The results from this work should be directly comparable with the results from Aspelund et al (2010) since the same process simulator has been used. Jensen and Skogestad (2008) used a PRICO process with a different framework for the evaluation of the flowsheet, which we do not have available.…”

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confidence: 99%

“…Mokarizadeh Haghighi Shirazi and Mowla (2010) used genetic algorithms for the PRICO process. Aspelund et al (2010) used tabu search combined with Nelder-Mead downhill simplex. Morin et al (2011) used evolutionary search for similar problems.…”

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confidence: 99%

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Optimization of a simple LNG process using sequential quadratic programming

Wahl

Løvseth

Mølnvik

2013

Computers & Chemical Engineering

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The efficiency of using sequential quadratic programming (SQP) for the optimization of a PRICO process for the production of liquefied natural gas (LNG) is demonstrated. Most of the returned objective values have been better, and the execution times much lower, than in most previously published work on similar optimization cases. The optimization runs discussed in this paper require around 5 minutes of execution time.

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Section: Optimization Specificationsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Optimization of a simple LNG process using sequential quadratic programming

Wahl

Løvseth

Mølnvik

2013

Computers & Chemical Engineering

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Control structure synthesis for operational optimization of mixed refrigerant processes for liquefied natural gas plant

Husnil

Lee

2014

AIChE Journal

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The best control structures for the energy optimizing control of propane precooled mixed refrigerant (C 3 MR) processes were examined. A first principles-based rigorous dynamic model was developed to analyze the steady-state and dynamic behaviors of the C 3 MR process. The steady-state optimality of the C 3 MR process was then examined in a whole operation space for exploring the feasibility of the energy optimizing control for possible control structures. As a result, the temperature difference (TD) between the warm-end inlet and outlet MR streams was exploited as a promising controlled variable to automatically keep the liquefaction process close to its optimum. The closed-loop responses were finally evaluated for every possible control structure candidate. Based on the steady-state optimality and the dynamic performance evaluation, several control structures with a TD loop were proposed to be most favorable for the energy optimizing control of the C 3 MR process. The proposed optimality approach can be applied to any natural gas liquefaction process for determining a proper controlled variable for optimizing operation.

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Toward more cost‐effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation‐based optimization

You

2014

AIChE Journal

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This paper presents a novel process design for a more cost-effective, greener process for making chemicals from shale gas and bioethanol. The oxidative coupling of methane (OCM) and cocracking technologies are considered for converting methane and light natural gas liquids (NGLs), into value-added chemicals. Overall, the process includes four process areas: gas treatment, gas to chemicals, methane-to-ethylene, and bioethanol-to-ethylene. We develop a simulation-optimization method based on the NSGA-II algorithm for the life cycle optimization (LCO) of the process modelled in the Aspen HYSYS. An energy integration model is also fluidly nested using the mixed-integer linear programming. The results show that for a "good choice" optimal design, the minimum ethylene selling price is $655.1/ton and the unit global-warming potential of ethylene is 0.030 kg CO 2 -eq/kg in the low carbon shale gas scenario, and $877.2/ton and 0.360 kg CO 2 -eq/kg in the high carbon shale gas scenario.

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An optimization-simulation model for a simple LNG process

Cited by 195 publications

References 11 publications

Optimization of a simple LNG process using sequential quadratic programming

Optimization of a simple LNG process using sequential quadratic programming

Control structure synthesis for operational optimization of mixed refrigerant processes for liquefied natural gas plant

Toward more cost‐effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation‐based optimization

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