kurimur: a-an expert system to select acid gas treating

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An expert system to select acid gas treating

processes for natural gas processing plants

Hideki Kurimura, Gary T. Rochelle* and Kamy Sepehrnoorit

Teikoku Oil Company, l-31 -10 Hatagaya, Shibuya-ku, Tokyo 151, Japan

*Department of Chemical Engineering, The University of Texas at Austin, Austin,

78712, USA

tDepartment of Petroleum Engineering, The University of Texas at Austin, Austin,

78712, USA

Received 31 July 1992; revis ed 25 January 1993

Texas

Texas

An expert system was developed to select near-optimum acid gas treating processes in

natural gas processing plants using heuristic knowledge from experts and literature. The

near-optimum processes are defined as highly applicable processes for given conditions.

The following subtasks are carried out in order by the system: determination of process

combinations; selection of acid gas removal processes; selection of individual acid gas

removal processes; selection of sulfur recovery units; selection of tail gas clean-up units. The

selection of acid gas removal processes is performed with both fuzzy and crisp (conven-

tional two-value) logic. The other subtasks are executed with the crisp logic. The

developed expert system can select near-optimum processes. For the selection of acid gas

removal processes, fuzzy logic is better than crisp logic because it provides more rigorous

and realistic solutions.

Keywords: expert system; acid gas treatment; natural gas processing

Introduction

It is more important, but more complex, to select acid

gas treating processes, such as acid gas removal pro-

cesses, sulfur recovery units and tail gas clean-up units,

now than ever before due to current environmental

issues, low gas prices and many more processes to be

considered. The natural gas industry has long recognized

the importance of selection guidelines. Previous papers

on this subjectle3 did not consider sulfur recovery and

tail gas clean-up units. None of the previous work has

structured the selection guidelines into an expert system.

Furthermore, the technology has changed since the most

recent publication.

The purpose of this work was to develop an expert

system to select near-optimum acid gas treating pro-

cesses and to provide more rigorous and realistic guide-

lines for the selection problem. Expert systems, the most

successful applications of Artificial Intelligence (AI)

technology, are computer programs designed to emulate

a human expert solving relatively complex problems in

an area of expertise. The following tasks were completed

in the development of the expert system:

1 Literature review for general knowledge acquisition

identifying the available processes.

*Author to whom all correspondence should be directed.

0950-4214/93/030151-08

@ 1993 Butterworth-Heinemann Ltd

Development of methods of solution based on the

literature review.

Heuristic knowledge acquisition from experts and

literature.

Coding in an expert system shell.

Testing.

In this paper, an overview of the expert system and

results from its use are presented. Details of this work

are presented in the thesis by Kurimura4.

Processes included

Acid gas treating processes in natural gas processing

plants can be categorized as follows:

1 Acid gas removal processes.

2 Sulfur recovery units.

3 Tail gas clean-up units.

The acid gas removal processes remove acid gases such

as CO* and H$ from a sour natural gas stream (or

hydrocarbon rich stream) to the level of product specifi-

cations necessary for pipeline transportation or cryo-

genic downstream processing. The following primary

classification of acid gas removal processes is used in this

paper:

1 Aqueous alkanolamine solvents.

2 Potassium carbonate solvents.

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An expert system to select acid gas treating processes: H Kurimura et al.

3 Physical solvents.

4 Hybrid solvents.

5 Redox processes.

6 Batch H,S scavengers.

7 Molecular sieves.

8 Cryogenic extractive distillation process.

9 Membranes.

10 Membranes followed by amines.

The first four processes above are widely used absorp-

tion/stripping processes. Aqueous alkanolamine and

potassium carbonate solvents remove acid gases with

chemical reactions. Organic solvents remove acid gases

by physical absorption. Hybrid solvents are mixtures of

amines and physical solvents to provide intermediate

characteristics between chemical and physical absorp-

tion. In the abso~tionlstripping processes, aqueous

alkanolamines are most common. Redox processes re-

move H,S to form elemental sulfur directly in the liquid

phase by oxidation and reduction reactions. Batch H,S

scavengers remove relatively small amounts of H,S by

chemical reaction along with spent chemical disposal.

Molecular sieves which have large polar surfaces adsorb

not only acid gases but also water vapour to an ex-

tremely low level and can be regenerated by pressure

reduction or heating. The cryogenic extractive distilla-

tion process is used to remove large amounts of CO* and

includes additives which prevent CO, from freezing.

Membranes are basically polymeric media to permeate

acid gases and water vapour selectively from a sour

natural gas stream to the low-pressure permeate gas

stream. Since pressure difference across the medium is

the primary driving force in the membranes, membranes

can also be used for bulk acid gas removal before amine

processes.

A sulfur recovery unit recovers sulfur compounds,

primarily H,S, from the low-pressure treated acid gas

stream out of the acid gas removal process to meet

environmental regulations on emissions or to recover

saleable product, usually elemental sulfur. The following

classification of sulfur recovery units is used in this

paper:

1 Claus.

2 Selectox.

3 Redox.

4 Batch H,S scavengers.

5 H$ enrichment plus Claus.

The Claus process produces elemental sulfur by partial

oxidation of H2S. It consists of a thermal stage followed

by two or three catalytic stages. In the thermal stage,

one-third of H,S is burned to SO*. The remaining part

of the H,S reacts subsequently with the SO2 to form

elemental sulfur in the thermal and catalytic stages. Two

primary process configurations, straight-through and

split-flow, are used depending on the H,S/C02 ratio in

the feed gas. Sulfur recovery is at most 98%. The

Selectox process reacts a low concentration of H,S

directly with oxygen over a catalyst rather than using a

combustion furnace as in the Claus process. Redox and

batch H2S scavengers are the same as those of the acid

gas removal processes. As a rule, large amounts of H,S

(more than 30 tons per day) in an acid gas stream are

treated with a Claus process. However, a low H,S

concentration prohibits the use of a Claus process. In

this case, some H,S enrichment process, such as amines

152 Gas Separation &

Purification 1993 Vol 7 No 3

for selective H,S removal, is needed prior to a Claus

plant.

A tail gas clean-up process is defined as a process

which recovers sulfur compounds, primarily SOz and

H,S, from the gas stream out of the Claus process to

meet environmental regulations on emissions. Generally,

the regulations are given in terms of the amount of total

emitted sulfur and/or the con~ntration of the sulfur.

Consequently, if the required sulfur recovery given by

the regulations exceeds the sulfur recovery (e.g. 98%)

achieved with a sulfur recovery unit, then a tail gas

clean-up unit should be installed. The following classifi-

cation is used in this paper:

1

Catalytic oxidation.

2 Catalytic oxidation with adsorption.

3 Hydrogenation plus oxidation.

4 Hydrogenation plus absorption.

5 Incineration plus absorption.

Expert systems

A typical expert system consists of a data base, a

knowledge base and an inference engine. A data base,

also known as a working memory, or short-term mem-

ory, stores data for each specific task of the expert

system. A knowledge base, or long-term memory,

contains general knowledge or rules relevant to the

problem domain and is the heart of the expert system.

The most common type of knowledge representation in

the knowledge base is the production rule or the

IF-THEN rule. The inference engine processes the input

data by sequentially matching rules from the knowledge

base with the input data or with the conclusions of

preceding matches, ultimately to arrive at final con-

clusions.

Histo~cally, the development languages of expert

systems have been LISP and PROLOG because of their

capability to handle symbols and lists, while an algorith-

mic language such as FORTRAN or BASIC was less

used. It should be noted that though the developed

program tends to be much more complicated, any expert

system can be developed using the algorithmic

languages. Recently, numerous expert system shells (de-

velopment tools) have become commercially available,

They provide highly sophisticated user interfaces to

develop the knowledge base without advanced program-

ming skills and have their own inference capability.

Therefore, the development of a prototype model can be

achieved more rapidly than before even without knowl-

edge engineers.

One of the key factors that contributes to the success

or failure of expert systems development projects is the

selection of problems. In addition to the selection of a

suitable problem, the placement of a number of bound-

aries around the problem may result in a workable

expert system6.

Although expert systems attempt to solve non-al-

gorithmic problems in limited domains, uncertainties of

rules and criteria of decision parameters and of user

input data make the systems less useful for real prob-

lems. The larger the domain becomes, the more uncer-

tainties the expert system may have.

There are several sources of imprecision and uncer-

tainty in the domain of an expert system. The process of

acquiring knowledge is quite imprecise because it is


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An expert system to select acid gas treating processes: H. K~rimura et al.

Table 1 sulfur recovery processes to be selected for tight miSSiOn regulations

Sulfur loading (t d-

H,S/CO,

30

Straight-through Claus

Split-flow Claus

likely that the knowledge acquired is not exactly the

same as the experts and that the reasoning process of an

expert is often not a precise process. Moreover, the

process of coding the acquired knowledge and the

info~ation provided by users may yield additional

uncertainties.

Thus, proper means to handle the uncertainties in a

consistent manner are needed for almost all expert

systems. One of the common methods is fuzzy logic,

which is used in this work. Fuzzy logic was invented in

the mid 1960s as an alternative to two-valued logic (or

crisp logic) and probability theory by offering alterna-

tives to traditional notions of set membership and logic.

Numerous expert systems have been developed which

incorporate fuzzy sets and logic especially in the control

area. Since fuzzy inference (approximate reasoning) can

deal with the special case of two-valued logic or crisp

sets, conventional expert systems based on crisp logic

can be regarded as a special case of fuzzy expert systems.

Currently, most of the usable fuzzy expert systems are

rule-based systems which utilize fuzzy production rulesg.

It is claimed that the num~r of rules in fuzzy expert

systems are ten or 100 times smaller than those in

conventional expert systems because fuzzy expert sys-

tems attempt to solve the problems approximately using

small numbers of essential rules. However, numerous

rules may be required to differentiate cases in conven-

tional expert systems. In fact, the successful fuzzy expert

system that controls the subway in the city of Sendai,

Japan uses only 24 rulesg.

Method of solution

The selection problem is solved through the following

steps:

Determination of process combinations.

Selection of classified acid gas removal processes.

Selection of individual acid gas removal processes.

Selection of sulfur recovery units.

Selection of tail gas clean-up units.

The following decision parameters which provide

bases for the selection problem are used in the expert

system:

Select ion of acid gas removal processes

Removal objectives.

Acid gas partial pressures.

Sulfur loading.

H,S/CO, ratio.

Amount of acid gas to be removed.

Required removal of COS and mercaptans.

CZ+ hydrocarbon content.

Water removal ability.

Additional parameters.

Select ion of sulfur recovery units

1 Sulfur loading.

2 H,S/C02 ratio in treated acid gases.

3 Total pressure.

Select ion of tai l gas clean u p units

1 Required overall sulfur recovery.

2 Sulfur loading.

3 CO, concentration in tail gases.

The selection of sulfur recovery units and tail gas

clean-up units involves fewer decision parameters

than

that of acid gas removal processes and is solved with

crisp logic in the expert system. However, the selection

of acid gas removal processes involves more decision

parameters and is solved with fuzzy logic not only to

consider the uncertainties but to obtain heuristic knowl-

edge from experts with less difficulty. Knowledge was

obtained from the literature and from a questionnaire

completed by six experts. Table 1 shows a sample of the

heuristic knowledge used for the selection of sulfur

recovery units by crisp logic. The questionnaire and

final knowledge base for fuzzy logic consisted of an

applicability matrix for each of the decision parameters

above. Table 2 is a sample applicability matrix deter-

mined by combining the knowledge from the literature

and the experts. It shows functionally and economically

determined applicabilities.

The knowledge expressed with the applicability tables

was directly translated into rules in Nexpert Object

which is an expert system shell on a VAX workstation

3540 running VMS and DEC windows.

Expert system implementation

The expert system shell provides its inference capability

and an augmented rule format and, hence, expert sys-

tems can be developed by simply entering rules into an

empty knowledge base. In order to know the effect of the

fuzzy theory on the results, we also developed an expert

system in which all the rules are written using crisp

logic.

Table 2 Applicability matrix for H,S partial pressure in the feed for

removal of H,S alone and for selective H,S removal

H,S partial

pressure (psia)

>lOO 30-100 2-30 0.2-2


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The translation of the heuristic knowledge such as

Table 2 is straightforward and is not included in the

discussion that follows. The translation of applicability

matrices such as Tabl e 2 into the rules will be discussed

using examples. The fuzzy membership functions used

for fuzzy rules will also be discussed.

For the crisp rules, the value of applicability is 1 or

0 (or Yes or No). Thus, if we consider the following

sample applicability matrix for H$ partial pressure

taken from Tabl e 2:

Very high High Medium Low Very low

Hybrid H

H M L VL

The corresponding applicability for the crisp rules is

given by:

Very high High Medium Low Very low

Hybrid Y Y Y N N

The translated rules with the above applicabilities are:

IF H2S partial pressure is Very High, High OR Medium,

THEN hybrid solvents are applicable.

IF HrS partial pressure is Low OR Very Low, THEN

hybrid solvents are inapplicable.

In practice, the first and second rules are decomposed

into three and two rules, respectively in Nexpert Object

due to its inability to handle disjunctive conditions in a

rule. With the crisp rules, inapplicable processes are

eliminated and, hence, processes which are not elimi-

nated are selected.

For the fuzzy rules, fuzzy membership functions must

be defined for each category in each fuzzy decision

parameter to give intermediate values. Figure I shows

the fuzzy membership functions for CZ+ hydrocarbon

content. The degrees of membership of Very High and

Very Low are calculated by the square of High and Low,

respectively . Depending on a q uantitative input value,

the degree of membership takes the value between 0 and

1. For example, with 10% CZ+, the degree of member-

ship is 0.36, 0.6 and 0.0 for Low, Medium and High

applicability, respectively and the degree of membership

is 0.13 and 0.0 for Very Low and Very High, respectively.

In order to compare the results with both rules, the

membership functions are determined in such a way that

consistency between them is maintained as much as

possible. For the previous sample applicability matrix,

the corresponding fuzzy rules are:

IF H,S partial pressure is High, THEN hybrid solvents

have High applicability.

IF H$ partial pressure is Medium, THEN hybrid

solvents have Medium applicability.

IF HJ partial pressure is Low, THEN hybrid solvents

have Low applicability.

IF H2S partial pressure is Very Low, THEN hybrid

solvents have Very Low applicability.

Corresponding actions are:

A degree of Very High applicability of hybrid solvents

is assigned with zero.

An original degree of High applicability of hybrid

solvents is updated by Min(degree of High PHZS,degree

of High applicability).

154 Gas Separation & Purification 1993

Vol

7 No 3

0.0 1 I I 0 0 Z/i 1

1

a 0 1

0 5 10 15

20 25

C2-plus hydrocarbon content (s)

Figure 1

Fuzzy

membership functions for

C

hydrocarbon

content

That is, the degree of High applicability is set equal to

the least of the degree of High P,, and the current

degree of High applicability.

An original degree of Medium applicability of hybrid

solvents is updated by Min(degree of Medium PH2s,

degree of Medium applicability).

An original degree of Low applicability of hybrid

solvents is updated by Min(degree of Low PH2s, degree

of Low applicability).

An original degree of Very Low applicability of hybrid

solvents is updated by Min(degree of Very Low PH2s,

degree of Very Low applicability).

Pi.,,s is the H,S partial pressure.

In the fuzzy rules above, the relationship IF-THEN is

expressed by the minimum value. On the contrary, the

relationship OR is expressed by the maximum value. In

addition, the relationship AND is expressed by the

minimum value. Using the above fuzzy rules, the degree

of each applicability is updated or reduced depending on

a given H2S partial pressure. Furthermore, the applica-

bilities are evaluated sequentially with all fuzzy decision

parameters used for a given removal objective. For

example, all applicabilities are evaluated sequentially

with CO, partial pressure, amount of acid gas to be

removed, and C,, hydrocarbon content for removal of

1 0

CL

3 0.8

3

s 0.6

;

0 0.4

20 40 60 80

Overall applicability ( )

Figure Fuzzy membership functions for overall applicability


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An exper t system to

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CO2

alone. With the above process, intersections of all

fuzzy sets for applicabilities are obtained.

The final degrees of applicability for Very High, High,

Medium, Low and Very Low for each classified acid gas

removal process are obtained as a result of the sequential

applicability evaluations with all fuzzy decision par-

ameters.

Figure 2

shows the fuzzy membership functions

of the applicabilities for any of the processes. Since a

direct comparison of the applicabilities for each process

is required, an overall applicability must be determined

using the membership functions. This process is known

as defuzzification. Although several defuzzification

methods have been proposed, the height method is used

due to its simplicity. In the height method, a fuzzy set

having the highest degree (non-zero) is chosen among

the five sets, and the average value of the applicability

corresponding to the highest degree is calculated giving

the overall applicability having the value between 0 and

1 for each process. If the medium applicability of a

process gives the maximum value among the five appli-

cabilities, the calculated overall applicability is always

0.5 regardless of the degree of the membership in the

medium applicability. To facilitate differentiation of

applicabilities of acid gas removal processes, in the case

where more than one of the processes have 0.5 of

maximum overall applicability, the process having the

maximum degree will be selected. If the highest degree

is zero, there is no applicable process for the given

conditions.

expert system. The underlined acid gas removal pro-

cesses represent processes selected by the crisp expert

system.

These results are presented to illustrate the trends in

the logic included in the expert systems. These cases have

not been verified by the experts, but the knowledge base

does incorporate their input.

The fuzzy expert system normally selects the acid gas

removal process having the highest overall applicability

Tabl es 3-6).

However, there are four cases (3, 4, 7

and 12) when the fuzzy system selects other processes on

the basis of special rules included in the logic which are

not accounted for quantitatively in the estimation of

applicability. In Case 3, the following water removal

related rule for the cryogenic specification is applied

resulting in the selection of molecular sieves after appli-

cability is evaluated with all the other decision par-

ameters:

IF the difference between molecular sieves and a maxi-

mum applicability is less than 0.35, THEN molecular

sieves are selected due to their simultaneous water

removal ability to meet cryogenic specifications.

In Case 4, the following water removal related rule for

the pipeline specification is used to select membranes

after all overall applicabilities are calculated:

IF the difference between membranes and a maximum

applicability is less than 0.2, THEN membranes are

selected due to their simultaneous water removal ability

to meet pipeline specifications.

Results and discussion

Typical results obtained by the expert system are shown

in

Tables 3-6

for the following cases:

1 Removal of CO2 alone.

2 Removal of H$ alone.

3 Simultaneous H$ and CO2 removal.

4 Selective H,S removal.

In Case 7, only hybrid solvent is selected because it

has the maximum degree of Medium applicability

among the processes having 0.5 overall applicability. In

Case 12, the following CO, injection related rule select-

ing cryogenic processes is applied after the completion of

applicability evaluation with all the other decision par-

ameters:

In these tables, the abbreviations AGRP, SRU, TGCU

and Me/amine are used for acid gas removal processes,

sulfur recovery units, tail gas clean-up units and mem-

branes followed by amines, respectively. Acid gas re-

moval processes expressed with bold letters represent

processes selected with the fuzzy logic. Each number

(from 0 to 1) with selected processes represents the

applicability of the process as determined by the fuzzy

IF the difference between cryogenic processes and a

maximum applicability is less than 0.2 AND the treated

CO2 is to be injected, THEN cryogenic processes are

selected.

The consistency of the results between the fuzzy expert

system and the crisp expert system is maintained except

for Cases 1 and 3. In Case 1, the following water removal

related rule for the pipeline specification is applied by the

Table 3 Results for the removal of COz alone (no sulfur compounds

in the feed gas, 1000 psig total pressure, CO, vented)

I

Case 1 Case 2

Case 3 Case 4

Feed conditions

CO, ( )

C,+-hydrocarbons ( )

Total volume (mm scfd)

Product specifications

RWXlltS

bold-faced: fuzzy

underlined: crisp

Selected AG R P

Selected amine

10

10 0.4 20

8

8 10 10

50

50 10 50

Pipeline Cryogenic

Cryogenic

Pipeline

Amine

0.89

Hybrid 0.77

Melamine 0.77

Membrane 0.5

Physical 0.16

Cryogenic 0.08

MDEA-based for bulk

CO, removal

Amine 0.89 Amine 0.84

Me/amine Ki&Zeve

Cryogenic 0.91

0.79 0.5 Hybrid 0.83

Physical 0.24 Membrane 0.83

Cryogenic 0.05 Me/amine 0.83

Me/amine 0.05 Physical 0.83

Amine 0.5

MDEA-based for maximum MDEA-based for maximum

CO, removal CO, removal

Million standard cubic feet per day

Gas Separation & Purification 1993 Vol 7 No 3 155


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An expert system to select acid gas treat ing pro cesses: H. Kurim ura

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Table 4 Results for the removal of H,S alone (no CO* or COS present, 1000 psig of total pressure, pipeline specifications, 8 C,,)

Case 5 Case 6

Case 7

Case 8

Feed conditions

H,S ( )

0.01

0.1

1

10

RSH (ppm)

None None

30

50

Total volume (mm scfd) 15

30 50

50

RSH (ppm)

None None

16

Maximum sulfur emission (lb sd) 10 50 2:: 500

Results

bold faced: fuzzy

underlined: crisp

Selected AG t?P

Batch Amine

ti&rcf 0.5

Hybrid 0.9

0.5 Physical

0.5

Me/amine 0.5 Me/amine 0.5

Physical 0.15

Selected amine MDEA DGA

Selected SRU

Lo-Cat

Straight-through Claus Straight-through Claus

Selected TGCU UCAP, Clintox, SCOT, Hydro. plus Lo-Cat

Sulften, BSR/MDEA

crisp expert system giving the selection of membranes

Removal of CO2 alone

after the elimination of inapplicable acid gas removal

processes is completed with the other decision par-

ameters:

IF membranes are applicable to the given conditions,

THEN they are most applicable due to their water

removal ability to meet pipeline specifications.

In Case 3, molecular sieves are eliminated with the other

decision parameters by the crisp expert system, but the

fuzzy expert system selects molecular sieves by applying

the following similar rule:

IF molecular sieves are applicable to the given condition,

THEN they are most applicable due to their water

removal ability to meet cryogenic specifications.

From the results of the developed expert system, the

following generalizations are obtained for each of the

removal objectives.

Only acid gas removal processes are selected for removal

of CO, alone. Among acid gas removal processes, the

following processes are important for these applications:

1 Amines.

2 Membranes.


4 Molecular sieves.

Amines are the most important processes in these appli-

cations and, in particular, MDEA-based solvents are

most important among the amines. Probably, rigorous

economical and technical process evaluation with

amines, membranes, and membranes followed by amines

would be required considering current progress of mem-

brane technology such as permeability, selectivity and

durability. In addition, water removal ability and mem-

brane plasticization caused by C,, hydrocarbor would

Table 5 Results for simultaneous H,S and CO, removal

Case 9

Feed conditions

CHost;;

CbS (ppm)

5

None

RSH (ppm) None

C,, hydrocarbons ( ) 8

Total pressure (psig) 1000

Total volume (mm scfd) 50

Product specifications Pipeline

CDS (ppm) None

RSH (ppm) None

CO, to be injected

No

Maximum sulfur emission (lb sd) 300

Results

bold-faced: fuzzy

underlined: crisp

Selected AG R P Amine 0.79

Hybrid 0.5

Me/amine 0.5

Membrane 0.22

Physical 0.15

Selected amine

DEA

Selected SRU Split-flow Claus

Selected TGCU Superclaus plus or

MODOP

Case 10 Case 11 Case 12

10.1 5 70

None 50

None 30 ::

10 15.0 6

1000 1000 500

50 50 50

Pipeline

Cryogenic

Pipeline

None 16 16

None 16 16

No No

Yes

50 500 1000

Amine 0.89 Amine 0.77 Hybrid 0.96

Cryogenic 0.79 Me/amine 0.5 Physical 0.96

Hybrid 0.77 Cryogenic 0.22 Cryogenic 0.78

Me/amine 0.77 Physical 0.11 Amine 0.5

Potacarb 0.5 Me/amine 0.5hysical 0.16

MDEA-based for bulk DGA

CO, removal

Lo-Cat Straight-through Claus Straight-through Claus

None UCAP, Clintox, SCOT,

Sulften, BSR/MDEA

156

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An expert system to select acid gas treat ing pr ocesses: H. Kurimur a et al.

Table 6 Results for selective H,S removal (2 CO,, 1000 psig total pressure, 50 mm scfd volume, pipeline spec., CD2 vented)

Case 13

Case 14

Case 15 Case 16

Feed conditions

H,S ( )

10

2

0.1 20

COS (ppm)

50

20

None 20

RSH (ppm)

40

20

None None

C,, hydrocarbons ( )

10

6

5

5

COS (ppm) 16 16 None 16

RSH (ppm)

16

16

None None

Maximum sulfur emission

(lb sd) 500

500

100 2500

Results

bold-faced: fuzq

underlined: crisp

Selected AGRP

Hybrid

0.79 Amine

0.98 SulFerox

1 .o

Physical

0.5 Hybrid

Physical

0.78 Amine

Physical 0.5 Hybrid

::o

Selected amine

MDEA

Selected SRU Straight-through Claus

Straight-through Claus None

Straight-through Claus

Selected TGCU Hydro. plus Lo-Cat Superclaus plus or

None

UCAP, Clintox, SCOT,

MODOP Sulften, BSR/MDEA

be taken into account in the evaluation mentioned

above.

Removal of H2S alone

For removal of HIS alone, acid gas removal processes,

sulfur recovery units and tail gas clean-up units may be

selected. In sulfur recovery units, only straight-through

Claus processes are selected for relatively large sulfur

loading (more than 15 tons per day). In acid gas removal

processes, the following processes are important for this

type of application:

1 Batch processes.

2 Molecular sieves.

3 Amines.

4 Hybrid solvents.

MDEA and DGA are important among amines. Hybrid

solvents are highly applicable if feed gases contain a

significant amount of organic sulfur compounds.

Simultaneous H_S and CO2 removal

Acid gas removal processes, sulfur recovery units and

tail gas clean-up units may be selected for simultaneous

H$ and CO, removal. In acid gas removal processes, the

following processes are important:

1 Amines.

2 Physical solvents.

3 Hybrid solvents.


Since no technical and economical evaluation of mem-

branes plus amines has been reported for the appli-

cations, the evaluation of an effect on sulfur recovery

units with CH, in permeate gases would be required.

Selective H2S removal

When selective H,S removal is required, acid gas re-

moval processes, sulfur recovery units and tail gas

clean-up units may be selected. Considering sour gas

compositions in the US, redox processes are very import-

ant due to abundance of low H,S concentration sour

gases. Besides redox processes, the following acid gas

removal processes are important:

1 Amines.

2 Hybrid solvents.

Physical solvents are not necessarily highly applicable

unless feed gases have high H,S/C02 ratios and high acid

gas partial pressures. Because amines for selective HIS

removal cannot remove organic sulfur compounds sufll-

ciently, hybrid solvents are especially important for the

feeds containing a significant amount of organic sulfur

compounds. However, very few papers concerning hy-

brid solvents have been reported and, hence, technical

and economical evaluation with hybrid solvents would

be required.

Conclusions

The following conclusions are drawn from this work:

The acid gas treating process selection problem is

relatively complicated and is suitable for the use of

expert systems. For any given problem of acid gas

treating, the fuzzy expert system was useful in select-

ing a subset of acid gas removal processes for more

rigorous technical and economical evaluation.

The method using applicability matrices to develop

fuzzy expert systems is straightforward and should be

applicable to other selection problems in which crisp

criteria cannot be easily obtained.

In the selection of acid gas removal processes, fuzzy

logic provides a relative applicability among appli-

cable processes and takes into account uncertainties of

criteria. The consistency of results between the fuzzy

and crisp systems is maintained in most cases.

Acknowledgements

We thank Mr. Robert McKee, M. W. Kellogg Co., for

providing his decision tree for the acid gas treating

process selection.

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