(590c) Optimal Design of Natural Gas Dryers
Design of Natural Gas Dryers
Yasser Al Wahedia*, Arwa H. Rabieb,
aGas Research Center, Abu Dhabi Petroleum Institute, P.O. Box 2533, Abu
Dhabi, United Arab Emirates
of Research & Technology, AbuDhabi Gas Industries Ltd. (GASCO),
Abu Dhabi, P.O. Box 665, Abu Dhabi, United Arab Emirates
cDepartment of Chemical Engineering and Materials Science, University of Minnesota,
Minneapolis, MN-55455, USA
Removal of moisture from gas streams is a commonly applied
technology in the fields of natural gas processing, natural gas liquefaction, air
separation, and air compression . The
degree of ?dryness? of the gas stream is commonly expressed in terms of the
water dew point [1,2]. In the field of natural gas
processing and for shallow Natural Gas Liquid recovery (i.e. recovery of C3+
components) glycol processes can achieve a dew point of -40oC, which
can adequately meet sales gas specifications . When ethane recovery is
sought or when liquefied natural gas (LNG) is the ultimate product, glycol
based processes fail to deliver the required gas dew point. For such
applications, the targeted dew point is achieved by usage of adsorption based
Molecular sieves (zeolites) are the adsorbents of choice when very
deep dew point depression is sought as in the case of LNG production. In
addition, the molecular sieving property of zeolites results in high
selectivity towards water molecules and high rejection of hydrocarbons.
Commonly used zeolites include Zeolite 3A and Na-X . The current practice in
designing natural gas dryers relies on empirical correlations and rules of thumb
. Optimality of such designs from a cost perspective is not guaranteed.
Minimizing the Net Present Value of the costs (NPVC) ensued due to these dryers
requires addressing three main challenges. Firstly, cyclic adsorption systems
are by definition dynamic and hence are governed by dynamic Partial
Differential Equations (PDEs) . The solution of the governing model is
required in order to evaluate the design constraints of absorption time, breakthrough
time, and the required regeneration time [4,5].
Secondly, the attainment of the cyclic steady state condition at the optimal
point has to be guaranteed [4,5]. Finally, the objective function is nonlinear,
contains both integer and continuous decision variables, and may also contain discontinuities
Recently, we have reported a novel approach in addressing similar
challenges in the context of optimizing the design of temperature swing
adsorption systems for Claus tail gas cleanup . Specifically, we focused on the
optimization of a model adsorption cycle. The cycle envisaged adsorption to
occur isothermally while regeneration was simulated as an analogue to a pure
component phase change. The evaluation of the breakthrough and required
regeneration times relied on re-casting the governing PDE model into a
semi-empirical form which is analytically solvable. The approach was shown to lead to substantial
savings in computational time. Furthermore, cost estimates of the proposed
technology proved its potential in competing with existing commercial
technologies in achieving the targeted removal at a substantially lower capital
and operating costs.
In the present work, we report the implementation of the same
approach in the design of natural gas dryers. Specifically, this work presents
a systematic methodology to design and schedule an optimal natural gas
dehydration network with the minimum Net Present Value of Costs (NPVC) while meeting
all process constraints. The proposed approach guarantees (local) optimality of
the design and through its computational speed allows an effective search for
optimal solutions for a large sample of initial guesses. Furthermore, the high fidelity of the model
employed provides higher confidence on the reliability of the design obtained
and hence on the cost estimate. A case study of a real industrial dehydration
network is presented and solved to illustrate the effectiveness of the devised
methodology. Such attributes are critical especially in times were cost
minimization is eagerly sought in the face of volatile energy markets.
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