(373p) Effective Systematic Optimization Approach for Integrated Energy Systems: LNG Case Study

Mary A. Katebah¹, Mohamad M. Hussein¹, Abdulla R. Al-Hajri², Easa I. Al-musleh¹*

¹Chemical Engineering Department, Qatar University

²Qatargas Operating Company

*Corresponding author. Tel. +974 44034148. E-mail address: e.almusleh@qu.edu.qa (E. Al-musleh)

International Energy Agency predictions show that the global energy demand is anticipated to grow by more than 25% in the next two decades, necessitating an investment of over
$2 trillion a year in new energy supplies.¹ Energy consumption increased by 2.3%, a growth rate that doubled over the past 10 years. Fossil fuels met almost 70%| of the demand, with natural gas (NG) being the largest supplier. Labeled as 2018’s global fuel of choice, NG contributed to almost half of the demand’s increase. This is attributed to its cleaner burning properties, low carbon intensity and costs. Liquefaction is the most effective method for terawatt-hour NG transportation over long distances. However, liquefied natural gas (LNG) processes are complex integrated systems associated with significant energy demands, which in turn lead to high carbon dioxide emissions.

In 2018, delivered LNG volumes reached 319 million tonnes, with an increase of 27 million tonnes due to capacity additions between 2011-2015. Planned projects with a capacity of 211 million tonnes per annum (MTPA) are expected to take final investment decisions (FIDs) in 2019.³ With its increase in liquefaction capacity, Qatar is the leading global LNG exporter, followed by Australia and the United States. Intense competition between LNG projects is calling for efficient LNG plants’ design and operation. Within the processing plant, approximately 80% of the carbon dioxide emissions emanate from the cold section. Generally, this section is an integrated process composed of natural gas liquids (NGL) recovery, liquefaction cycles, helium extraction and nitrogen removal units.⁴ The cold section is also characterized by many decision variables (more than 52 variables) which makes its optimization a challenging task. Therefore, this research focuses on developing a systematic methodology for the operational optimization of integrated systems such as the cold section of a baseload LNG plant. The studied liquefaction process was a propane pre-cooled mixed refrigerant (C3MR) system integrated with a scrub column for NGL recovery, nitrogen rejection via cryogenic distillation, and helium recovery by means of a self-refrigeration scheme.

Aspen Plus^® was used to build a base-case model. To minimize convergence and optimization issues, a simplified plant model was proposed and found to reflect the performance of an actual process. Base case simulation results showed that 123 MW of compression power was required for a base-load plant of 3.65 MTPA capacity. This was followed by a degree of freedom (DOF) analysis to identify the optimization variables. Sensitivity analyses were performed to obtain insights and operating windows for energy reduction. A new optimization methodology was developed comprising of a number of successive steps. Optimum values of the DOFs and power of the individual units were successively identified while representing the interaction between the different units by simplified predictive models. In addition to its relative simplicity, this methodology capitalized on using Aspen Plus^® built-in features without the utilization of external optimization solver. Furthermore, it resulted in beneficial process trends that otherwise may not be easy to obtain using sophisticated mathematical approaches. Results showed that optimizing the operating degrees of freedom decreased the compression power by 6%, leading to an additional profit of ~4.3 million USD/ year. The method was also tested against other sophisticated approaches⁵ and found to give effective results with much less effort.

References:

(1) World Energy Outlook 2019 https://www.iea.org/weo2018/ (accessed Mar 23, 2019).

(2) Global Energy & CO2 Status Report 2018 https://www.iea.org/geco/ (accessed Mar 23, 2019).

(3) LNG Outlook 2019. Breakthrough year? http://www.gasstrategies.com/downloads/lng-outlook-2019/ (accessed Mar 23, 2019).

(4) Rabeau, P.; Paradowski, H.; Launois, J. How To Reduce CO₂ Emissions in the LNG Chain. LNG15 Int. Conf. 2007, 1–19.

(5) Rao, H.; Karimi, I.; A superstructure-Based Model for Multistream Heat Exchanger Design Within Flow Sheet Optimization. AIChE J. 2017.