An expert system to select acid gas treating processes for natural gas processing plants

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; revised 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’ (conventional 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 processes, 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 processes and to provide more rigorous and realistic guidelines 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 specifications necessary for pipeline transportation or cryogenic downstream processing. The following primary classification of acid gas removal processes is used in this paper:

1 Aqueous alkanolamine solvents. 2 Potassium carbonate solvents.

Gas Separation & Purification 1993 Vol 7 No 3 151

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 absorption/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 absorption. In the abso~tionlstripping processes, aqueous alkanolamines are most common. Redox processes remove 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 distillation 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 classification 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 memory, 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 conclusions.

Histo~cally, the development languages of expert systems have been LISP and PROLOG because of their capability to handle symbols and lists, while an algorithmic 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 (development 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 knowledge 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-algorithmic problems in limited domains, uncertainties of rules and criteria of decision parameters and of user input data make the systems less useful for real problems. The larger the domain becomes, the more uncertainties 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

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, <O.l 0.1-5 5-15 15-30 >30

Straight-through Claus Split-flow Claus

likely that the knowledge acquired is not exactly the same as the expert’s 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 systems attempt to solve the problems approximately using small numbers of essential rules. However, numerous rules may be required to differentiate cases in conventional 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:

Selection 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.

Selection of sulfur recovery units

1 Sulfur loading. 2 H,S/C02 ratio in treated acid gases. 3 Total pressure.

Selection of tail gas clean -up 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 knowledge 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 determined 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 systems 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 <0.2

Amine M M VH H M Physical VH VH VL VL Hybrid H H kl L VL M/amine H M VL VL VL

Applicability: VH, Very high; H, High; M, Medium; L, Low; VL, Very low. M/amine: membranes followed by amines

Gas Separation 81 Purification 1993 Vol 7 No 3 153


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 Table 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 Table 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 eliminated 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 membership 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).


0.0 1 6 I I ’ 0 ’ 0 ‘Z/i I ’ 1 ’ ’ ’ ‘1 I 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 applicabilities 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 2 Fuzzy membership functions for overall applicability


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 parameters. 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 applicabilities, 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 processes 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 (Tables 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 applicability is evaluated with all the other decision parameters:

IF the difference between molecular sieves’ and a maximum 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 parameters:

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 membranes followed by amines, respectively. Acid gas removal 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



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 parameters:

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. 3 Membranes followed by amines. 4 Molecular sieves.

Amines are the most important processes in these applications 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 membrane technology such as permeability, selectivity and durability. In addition, water removal ability and membrane plasticization caused by C,, hydrocarbor# would

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

Case 9

Feed conditions

CHost I;; CbS (ppm)

5 1 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 0.1 5 5 70 1 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 0.5 Physical 0.16

MDEA-based for bulk DGA CO, removal Lo-Cat Straight-through Claus Straight-through Claus None UCAP, Clintox, SCOT,

Sulften, BSR/MDEA



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. 4 Membranes followed by amines.

Since no technical and economical evaluation of membranes plus amines has been reported for the applications, 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 removal processes, sulfur recovery units and tail gas clean-up units may be selected. Considering sour gas compositions in the US, redox processes are very important 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 hybrid 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 applicable 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.

References

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An expert system to select acid gas treating processes for natural gas processing plants