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Use of optimization modelling to evaluate industrial waste reduction options: Application to a sour gas plant
Waste Management, Vol. 14, No. 7,649-654, 1994 Copyright © 1994 Elsevier Science Ltd Printed in the USA. All rights reserved 0956-053X/94 $6.00 + .00
Pergamon 0956-053X(94)OOO59-X
TECHNICAL
NOTES
USE OF OPTIMIZATION MODELLING TO EVALUATE INDUSTRIAL WASTE REDUCTION OPTIONS: APPLICATION TO A SOUR GAS PLANT HdlOne D. Roberge BEAK Consultants Ltd., 3285 Cavendish Blvd., Suite 610 , MontrOal, Qudbec, Canada, H4B 2L9
Rod P. Sikora Gulf Canada Resources Ltd., 401-9th Avenue S.W., P.O. Box 130, Calgary, Alberta, Canada, T2P 2H7
Brian W. Baetz* Department o f Civil Engineering, McMaster University, Hamilton, Ontario, Canada, L8S 4L7
A B S T R A C T . This note reports on a study of waste reduction options for the upstream oil and gas industry and involves
the application of a waste reduction optimization model to a generic sour gas plant. The waste reduction optimization model is meant as an aid for decision-making relating to the implementation of waste reduction options. The generic facility was developed from process knowledge provided by industry members of a project steering committee, as well as waste management information from industry manuals and represents a facility of average capacity and typical configuration. Several waste minimization options were modelled for selected waste streams. The selected streams were chosen based upon waste flows and disposal costs and their potential for waste reduction. The results of the modelling for the generic sour gas plant have shown that a set of cost-effective waste reduction options exist, there is significant potential for reducing the total quantity of waste to be managed and disposed of, and that implementation of the options would lead to considerable cost savings. The value and usefulness of the modelling approach lie not only in the generated results, but also in the fact that to construct the model, relevant waste flows and every possible manner that these waste flows can be minimized or processed are systematically identified. Once modelled, the parameters can be readily manipulated to determine various possible waste management strategies. To effectively use the modelling approach, the waste reduction team should have knowledge of the plant processes, existing waste management practices and costs, information on potential waste reduction options and technologies, as well as experience in mathematical modelling and analysis.
The oil and gas production industry, along with a range of other production industries, is interested in dealing with waste management problems in a proactive manner. The decommissioning of production facilities can cost millions of dollars for site cleanup and monitoring activities, which may have been avoided or significantly reduced if efforts had been previously put into waste reduction and effective waste management. The decommissioning experiences gained illustrate the problems resulting from the generation of large quantities of waste that must be dealt with at the end of a plant's operating life and the need for long-range waste m a n a g e m e n t planning to reduce generated waste quantities. A waste reduction model has been previously developed to assist in the long term planning of waste reduction and waste m a n a g e m e n t strategies (2). Because of the longer time horizon used in the pre-
INTRODUCTION Waste reduction involves in-plant practices that reduce, avoid or eliminate the generation of waste, so as to reduce risks to the environment and health (1). Potential waste reduction options may be changes to input materials, process modifications and recovery/reuse of on-site waste streams.
TECHNICAL NOTES is a section for concise, peer-reviewed papers containing useful, ableit sometimes narrowly focused, technical information. RECEIVED 29 MARCH 1994; ACCEPTED 13 SEPTEMBER 1994. *To whom correspondence may be addressed. Acknowledgments--The authors would like to acknowledge Environment Canada, the Federal Panel for Energy Research and Development, The Canadian Petroleum Association (CPA) and the Alberta Environmental Research Trust, for their financial support of this research project, and to the members of the project steering committee for their input.
649
650 vious work, a greater emphasis was placed upon development and expansion of on-site waste management facilities and the interaction between these t r e a t m e n t / d i s p o s a l facilities and the p r o d u c t i o n units with waste reduction potential. It is desired, however, to illustrate that this modelling approach could also be useful o v e r the short-to-intermediate term for the investigation of waste reduction options with no or low capital cost requirements, to reflect the realities of industrial waste management in difficult e c o n o m i c conditions. This note describes the application of the waste reduction optimization model to a generic sour gas plant for this type of waste management planning and is part of an overall study of waste reduction options for the upstream oil and gas industry (3,4). The optimization modelling approach is based on minimizing the overall cost of waste reduction and waste management for an industrial facility over a defined time period w h e r e a c o m b i n a t i o n o f m a n y potential options can be considered. The model is meant to be used as an aid in an overall decision-making framework. The structure of the note is as follows. In section two, the modelling approach is briefly summarized. The case study facility is discussed in section three, and the obtained modelling results for this generic facility are reported and discussed within section four. Concluding remarks are offered in section five. 2. B A C K G R O U N D ON M O D E L L I N G APPROACH The waste reduction model uses an optimization technique to determine an overall strategy for minimizing the cost of waste reduction, treatment and disposal options. The different options and their associated costs, as well as other economic and technical information, are required as input data. The problem is formulated as a mixed integer linear programming (MILP) problem. The program was run on a microcomputer using L I N D O , a commercial software package. In this application, the objective function represents the capital and operating costs of the existing and proposed waste reduction and waste management options, and the constraints represent the limitations present within the modelled facility. These aspects are briefly summarized below and are presented in greater detail in the previous work (2).
Objective Function The objective function in the model formulation represents the overall Net Present Value (NPV) cost of waste reduction and disposal and includes cost savings due to implementation of waste reduc-
H.D. ROBERGE, R. P. SIKORA, AND B. W. BAETZ tion options. The cost savings will be a function of the current waste disposal practices, as well as the proposed waste reduction options and the waste flow quantities. An overall planning horizon of 5 years was considered, with five increments of 1 year each. All costs were escalated at a rate of 5% and were discounted back to present dollar terms with a discount factor of 15%. The escalation rate value and the discount factor were consistent with those used by member companies of the Canadian Petroleum Association (CPA).
Constraints There are many different types of constraints that can be used to define a mathematical programming problem, and those considered for this industrial waste reduction case were the following (2): • mass balances on process units, waste reduction options, and flow junctions for all waste streams; • initial waste flow quantities; • waste management facility capacity constraints; • waste classifications (hazardous or nonhazardous). 3. FACILITY DESCRIPTION Figure 1 shows a simplified generic sour gas production facility with the process units and the waste streams that were c o n s i d e r e d for the modelling work carried out for this research. The generic facility has an average inlet flow and composition and results in hydrocarbon gas and liquid sales streams and includes sulphur recovery. The raw gas flowrate is 830,000 m3/day, with 4% HzS content. 45 tonnes/day of sulphur are generated in the sulphur plant (without tail gas clean-up), and 670,000 m3/day of sales grade gas are produced. Figure 1 also shows the waste streams that result from the various stages in the process. Table 1 gives the current waste disposal methods and costs, provided by the industry members of the project steering c o m m i t t e e , for the waste stream categories modelled for the generic facility. These costs vary, depending upon whether the streams are classified as hazardous or nonhazardous. In the generic facility, the sludges are classified as hazardous or n o n h a z a r d o u s , depending upon their source and nature, and the filters and solvent wash fluids are classified as hazardous. F o r the modelling base case, the sludges were classified as nonhazardous. The waste streams included in the base case were the following: process run-off • open drain wastewaters • boiler blowdown •
O~
I
I cIosed Drain ) Wastewaters 180m3/year
I 1700 m3/year /
I Process Runoff Nj 17000m3/yearf
LPGand ) Condensate 230m3/day
I
FIGURE 1. Generic sour gas plant schematic used for modelling.
Blowdown Water Qty. 300 m3/yr
I
Solvent Wash Fluids Qty. 3 m3/yr Disposal Cost $4.5K
Solvent Wash Fluids ] Qty. 9 m3/yr Disposal Cost $13.5KJ
.~,utmm~/Yr/l(and otheruses)
Solvent Wash Fluids] Qty. 2 m3/yr | Disposal Cost $3.0K j
I Laboratoryand Maintenance I i
Fractionation, Treatment andStorage
SweeteningLiquld
60 m3/day
110 m3/day
•
Solvent Wash Fluids ] Qty. 3 m3/yr Disposal Cost $4.5K I
I
Sales Gas N 670000 m3/day j v produced ~ "
DeepCut
Process Pond Sludge I Qty. 110 m3/yr Disposal Cost $20.0K
l Process Wastewater I Qty. 19200 m3/yr Disposal Cost $57.5K I
I Wastewater System I (DeepWell)
Hydrocarbon Lube Oil I Qty. 10 m3/yr I DisposalCost $3.5K I
Gas Drying Sludge Qty. 1 m3/yr Disposal Cost $2.5K
MEA Sludge Qty. 3 m3/yr Disposal Cost $7.5K
Hydrocarbon Lube Oil Qty. 25 m3/yr Disposal Cost $8.8K
I LIQUID (60 hal/day) Solvent Wash Fluids I I '
83oo00m3/,~y¢/I Separation
RawGasInlet~
GAS
Filters - Glycol Qty. 8/yr Disposal Cost $0.2K
Filters - Glycol I Qty. 37/yr I DisposalCost $0.9K I
Filters - MEA Qty. 246/yr Disposal Cost $5.9K
RefrigerationI---
Filters- Glycol Qty. 19/yr DisposalCost $0.5K
eL_
I Dehydration -~'[Glycol lujeetionJ
Gas Sweetenin
CompressiHon3000 =~~[ (Amine-MEA)
Sulphur
652
H . D . R O B E R G E , R . P . S I K O R A , A N D B. W. B A E T Z TABLE1 BreakdownofCurrentWasteDisposalCosts~rtheGenedcPlant Disposal Cost ($ per unit)
Categories of Waste
Unit
Classification
Disposal Location
In-Plant
Transport
Disposal
Total
Sludges
m3
Nonhazardous
#
Nonhazardous
98 56 2,450 1 12
Wastewater Solvent-wash fluids
m3 m3
Lube oil
m3
22 20 50 10 12 3 3 15 51
40 40
Filters
Reclaimer Landfill Chem Security Rocky Landfill Chem Security Deep Well Deep Well Chem Security Reclaimer
160 116 2,500 12 24 3 3 1,500 350
Nonhazardous Hazardous
1
1,485 248
51
1Waste costs per unit depend on how much is handled or disposed of at any one time. 2Disposal costs to Chem. Security (an off-site treatment/disposal facility) includes the transportation cost component.
• • • • • • •
sludge - - gas sweetening sludge - - gas drying closed drain w a s t e w a t e r s lubrication oils filters - - M E A filters - - glycol solvents
size and configuration of the generic facility. Operating cost estimates were made, based on available information, as well as estimates for any additional operating costs for new equipment and any applicable savings in energy c o n s u m p t i o n and raw materials. These choices were made by the project steering committee, based on collective experience and technical judgement.
Table 2 gives a s u m m a r y of the information on the waste reduction options considered. The options were primarily taken f r o m (3) where they are described in detail. Ranges were given for the waste reduction potential, as well as for the capital and o p e r a t i n g c o s t s for the various options. Values within the ranges were chosen, consistent with the
4. MODELLING RESULTS Table 3 gives the general results obtained after solving the mathematical p r o g r a m m i n g model for the base run, as well as for the second case where the
TABLE 2 Summary of Waste Reduction Options
Option (Description) Flow diversion/housekeeping Equipment change Process change (MEA to MDEA) Inlet gas separation Filter optimization (oberg) Equipment optimization Filter optimization (cuno) Process optimization Recycling/reuse Material substitution Mechanical dewatering (centrifuge) Mechanical dewatering (plate & frame) Freeze/thaw dewatering
Capital Cost
Operating Cost I
$85,000 $35,000
$20,000 ($200)
$1,000,000
($800,000)
Affected Stream(s)
Waste Minimization Potential 2
Wastewater pond sludge Glycol filters Gas drying sludge MEA filters
30% 20% 20% 25%
Gas sweetening sludge Glycol filters MEA filters Glycol filters MEA filters Lube oil Solvent Solvent All sludge streams
50% 40% 70% 50% 70% 15% 75% 100% 80%
$45,000 $75,000 $0 $100,000 $0 $85,000 $0 $0
$2,600 $35,000 $600 $15,000 ($7,600) ($4,800) $900 $50/m3 + $5,000
$160,000
$45,000
All sludge streams
90%
$14,000
$25,000
All sludge streams
70%
1( ) indicates a cost savings. The operating costs do not include savings due to decrease in disposal costs. 2Based on waste flow units (see Fig. 1). 31ndicates a waste stream becoming nonhazardous.
Type of Reduction Source Source Source Source and hazard 3 Source Source Source Source Source Source Post-generation Hazard Post-generation (volume) Post-generation (volume) Post-generation (volume)
OPTIMIZATION MODELLING
653
TABLE 3 Summary of Optimal Results From Modelling Sludge Streams (nonhazardous)
Sludge Streams (hazardous)
$2,268,400 $1,000,000 $864,000 1.15 years $68,000
$3,075,700 $1,000,000 $1,075,000 0.9 years $280,000
Net cost savings over 5 years l Capital costs paid out Annual cost savings (year 1) Payback period 2 Annual disposal cost 3 savings
~Difference between savings and costs incurred for optimal strategy (net present value dollars). ZPayback period = [capital costs paid out/annual cost savings (year 1)]. 3Disposal costs include in-plant handling, transportation, and disposal.
sludge streams were considered hazardous. These strategies represent the best combination of waste reduction options to minimize Net Present Value (NPV) costs.
savings from the reduced waste flow and the change in treatment/disposal requirements, is 1.15 years. Table 4 illustrates the chosen options for the optimal solution of the base run. The results of the modelling were validated by industry members of the project steering committee. The following is a summary of the waste reduction achieved: • Process change from MEA to MDEA, resulting in a 25% reduction of filters (and with the remaining filters being nonhazardous), as well as a 50% reduction of gas sweetening sludge; • Equipment optimization for glycol filters, resulting in a 50% reduction in glycol filters; • Process optimization for lubrication oil, resulting in a 15% reduction of waste oil; • Material substitution for the solvent stream, resuiting in a nonhazardous waste stream; • Mechanical dewatering (by centrifuge) of the process pond sludges, resulting in an 80% reduction in the volume of sludge.
Non-hazardous Sludges As shown in Table 3, the net cost savings from implementing the optimal strategy is approximately $2.25 million over the 5-year time period. This total net cost savings is the difference between the savings (incurred from the reduced disposal and operating costs), and the costs (capital and operating costs paid out) for the optimal strategy. This value was determined from performing two computer runs: (a) where all the waste reduction options were considered, and (b) where no options were available. The optimal result from run (a) minus the optimal result from run (b) yielded the total net cost savings. The capital cost paid out is $1,000,000, for the process change options to convert from an MEA to an MDEA amine system. A large portion of the cost savings is due to the high energy savings resulting from implementing this option. The overall payback period for the capital investment, including cost
Hazardous Sludges For a second case, the sludge streams were considered to be hazardous wastes. The results of this case indicate how the overall cost could be affected by regulatory changes, which could result in a more stringent classification of waste streams; or if the plant was located in a different jurisdiction, where sludges of this nature are considered as hazardous wastes. The results show that the same waste reduction options as for the base case yield the optimal strategy; however, the net cost savings would be approximately $3 million. This is to be expected because the disposal cost for hazardous sludges is approximately 20 times higher than that for nonhazardous sludges. For this case, the overall payback period for the capital investment, including cost savings from the reduced waste flow and the change in treatment/disposal requirements, is less than 1 year. The results for this case were
TABLE 4 Selected Waste Reduction Options
Option Process change (MEA to MDEA)
Equipment optimization Process optimization Material substitution Mechanical dewatering (centrifuge)
Capital Cost
Operating Cost ~
$1,000,000
($800,000)
$0 $0 $0 $0
$600 ($7,600) $900 $50/m 3 + $5,000
Affected Stream(s) MEA filters Gas sweetening sludge Glycol filters Lube oil Solvent All sludge streams
Waste Minimization Potential 2 25% 50% 50% 15% 100% 80%
1( ) indicates a cost savings. The operating costs do not include savings due to decrease in disposal costs. 2Based on waste flow units (see Fig. 1). 3Indicates a waste stream becoming nonhazardous.
Type of Reduction Source and Hazard 3 Source Source Source Hazard Post-generation (volume)
654
H. D. R O B E R G E , R. P. S I K O R A , A N D B. W. B A E T Z
also validated by an industry member of the project steering committee.
5. CONCLUDING REMARKS The waste reduction optimization modelling approach used in this note is potentially useful for the evaluation of waste reduction alternatives and for implementing a structured approach to waste management planning. For the application to the generic sour gas plant, the obtained results illustrate that a set of waste reduction options exist that are costeffective and that their implementation would lead to considerable cost savings. The generic plant results should not be used for the evaluation of actual plants, but the developed approach could be utilized with specific plant inputs to generate a set of waste reduction options for consideration. The results from this type of modelling are not meant to provide definitive answers on which options to implement and exactly when they should be implemented. Rather, these results should be considered as part of an overall evaluation that may require more rigorous analysis based on other criteria to determine a course of action. For example, the MEA to MDEA process change would require more detailed process engineering work to determine site-specific applicability. The process of developing the required information and applying a systematic approach allows for the questioning of current waste management practices and methods, as well as identification of areas where information and knowledge is lacking. The modelling approach is most useful when the information used in developing the flowchart and the mathematical formulation is as reliable and accurate as possible and when the problem being studied has a significant degree of complexity. In order to effectively use the model, the team doing the waste reduction study should have reasonable knowledge of the industrial processes, as well as the waste streams generated and the current waste management practices and costs. Information on potential waste reduction options and their associated costs, as well as experience in mathemat-
ical modelling and using the appropriate software, are also required. The potential of the modelling approach could be further exploited by studying the effect of alternative scenarios and conditions, such as: • incorporating capital availability as a budgetary constraint; • fixing a total waste reduction objective of a certain percentage to be achieved by a particular year (this could reflect company policy and may include some waste reduction options that would not be implemented purely on an economic basis); • considering long-term liability costs, including off-site disposal and site clean-up; • varying waste flow quantities to determine the sensitivity of the system and the effect of variabilities in operation; • accounting for increases in waste flow generation in relationship to production rates; • varying the costs of some options within the estimated ranges; • increasing off-site disposal costs; • adding more waste streams and reduction options to the formulation for a specific application. The research reported in this note has shown that the developed optimization modelling approach is potentially useful for waste reduction planning for the short-to-intermediate term where significant capital resources may not be available for waste management activities at a given facility.
REFERENCES 1. Drabkin, M., Fromm, C., and Freeman, H. M. Development of options for minimizing hazardous waste generation. Environmental Progress 3:167-174 (1988). 2. Roberge, H. D., and Baetz, B. W. Optimization modelling for industrial waste reduction planning. Waste Management 14(1): 35-48 (1994). 3. Bromley Engineering Ltd. Waste minimization in the upstream oil and gas industry: vol. 1. Prepared for the Canadian Petroleum Association, Calgary, Alberta, Canada (1992). 4. Beak Consultants Ltd. Research study: Application of a waste reduction model to a sour gas plant. Prepared for the Canadian Petroleum Association, Calgary, Alberta, Canada. (1992).
Open for discussion until 28 February 1995.
Pergamon 0956-053X(94)OOO59-X
TECHNICAL
NOTES
USE OF OPTIMIZATION MODELLING TO EVALUATE INDUSTRIAL WASTE REDUCTION OPTIONS: APPLICATION TO A SOUR GAS PLANT HdlOne D. Roberge BEAK Consultants Ltd., 3285 Cavendish Blvd., Suite 610 , MontrOal, Qudbec, Canada, H4B 2L9
Rod P. Sikora Gulf Canada Resources Ltd., 401-9th Avenue S.W., P.O. Box 130, Calgary, Alberta, Canada, T2P 2H7
Brian W. Baetz* Department o f Civil Engineering, McMaster University, Hamilton, Ontario, Canada, L8S 4L7
A B S T R A C T . This note reports on a study of waste reduction options for the upstream oil and gas industry and involves
the application of a waste reduction optimization model to a generic sour gas plant. The waste reduction optimization model is meant as an aid for decision-making relating to the implementation of waste reduction options. The generic facility was developed from process knowledge provided by industry members of a project steering committee, as well as waste management information from industry manuals and represents a facility of average capacity and typical configuration. Several waste minimization options were modelled for selected waste streams. The selected streams were chosen based upon waste flows and disposal costs and their potential for waste reduction. The results of the modelling for the generic sour gas plant have shown that a set of cost-effective waste reduction options exist, there is significant potential for reducing the total quantity of waste to be managed and disposed of, and that implementation of the options would lead to considerable cost savings. The value and usefulness of the modelling approach lie not only in the generated results, but also in the fact that to construct the model, relevant waste flows and every possible manner that these waste flows can be minimized or processed are systematically identified. Once modelled, the parameters can be readily manipulated to determine various possible waste management strategies. To effectively use the modelling approach, the waste reduction team should have knowledge of the plant processes, existing waste management practices and costs, information on potential waste reduction options and technologies, as well as experience in mathematical modelling and analysis.
The oil and gas production industry, along with a range of other production industries, is interested in dealing with waste management problems in a proactive manner. The decommissioning of production facilities can cost millions of dollars for site cleanup and monitoring activities, which may have been avoided or significantly reduced if efforts had been previously put into waste reduction and effective waste management. The decommissioning experiences gained illustrate the problems resulting from the generation of large quantities of waste that must be dealt with at the end of a plant's operating life and the need for long-range waste m a n a g e m e n t planning to reduce generated waste quantities. A waste reduction model has been previously developed to assist in the long term planning of waste reduction and waste m a n a g e m e n t strategies (2). Because of the longer time horizon used in the pre-
INTRODUCTION Waste reduction involves in-plant practices that reduce, avoid or eliminate the generation of waste, so as to reduce risks to the environment and health (1). Potential waste reduction options may be changes to input materials, process modifications and recovery/reuse of on-site waste streams.
TECHNICAL NOTES is a section for concise, peer-reviewed papers containing useful, ableit sometimes narrowly focused, technical information. RECEIVED 29 MARCH 1994; ACCEPTED 13 SEPTEMBER 1994. *To whom correspondence may be addressed. Acknowledgments--The authors would like to acknowledge Environment Canada, the Federal Panel for Energy Research and Development, The Canadian Petroleum Association (CPA) and the Alberta Environmental Research Trust, for their financial support of this research project, and to the members of the project steering committee for their input.
649
650 vious work, a greater emphasis was placed upon development and expansion of on-site waste management facilities and the interaction between these t r e a t m e n t / d i s p o s a l facilities and the p r o d u c t i o n units with waste reduction potential. It is desired, however, to illustrate that this modelling approach could also be useful o v e r the short-to-intermediate term for the investigation of waste reduction options with no or low capital cost requirements, to reflect the realities of industrial waste management in difficult e c o n o m i c conditions. This note describes the application of the waste reduction optimization model to a generic sour gas plant for this type of waste management planning and is part of an overall study of waste reduction options for the upstream oil and gas industry (3,4). The optimization modelling approach is based on minimizing the overall cost of waste reduction and waste management for an industrial facility over a defined time period w h e r e a c o m b i n a t i o n o f m a n y potential options can be considered. The model is meant to be used as an aid in an overall decision-making framework. The structure of the note is as follows. In section two, the modelling approach is briefly summarized. The case study facility is discussed in section three, and the obtained modelling results for this generic facility are reported and discussed within section four. Concluding remarks are offered in section five. 2. B A C K G R O U N D ON M O D E L L I N G APPROACH The waste reduction model uses an optimization technique to determine an overall strategy for minimizing the cost of waste reduction, treatment and disposal options. The different options and their associated costs, as well as other economic and technical information, are required as input data. The problem is formulated as a mixed integer linear programming (MILP) problem. The program was run on a microcomputer using L I N D O , a commercial software package. In this application, the objective function represents the capital and operating costs of the existing and proposed waste reduction and waste management options, and the constraints represent the limitations present within the modelled facility. These aspects are briefly summarized below and are presented in greater detail in the previous work (2).
Objective Function The objective function in the model formulation represents the overall Net Present Value (NPV) cost of waste reduction and disposal and includes cost savings due to implementation of waste reduc-
H.D. ROBERGE, R. P. SIKORA, AND B. W. BAETZ tion options. The cost savings will be a function of the current waste disposal practices, as well as the proposed waste reduction options and the waste flow quantities. An overall planning horizon of 5 years was considered, with five increments of 1 year each. All costs were escalated at a rate of 5% and were discounted back to present dollar terms with a discount factor of 15%. The escalation rate value and the discount factor were consistent with those used by member companies of the Canadian Petroleum Association (CPA).
Constraints There are many different types of constraints that can be used to define a mathematical programming problem, and those considered for this industrial waste reduction case were the following (2): • mass balances on process units, waste reduction options, and flow junctions for all waste streams; • initial waste flow quantities; • waste management facility capacity constraints; • waste classifications (hazardous or nonhazardous). 3. FACILITY DESCRIPTION Figure 1 shows a simplified generic sour gas production facility with the process units and the waste streams that were c o n s i d e r e d for the modelling work carried out for this research. The generic facility has an average inlet flow and composition and results in hydrocarbon gas and liquid sales streams and includes sulphur recovery. The raw gas flowrate is 830,000 m3/day, with 4% HzS content. 45 tonnes/day of sulphur are generated in the sulphur plant (without tail gas clean-up), and 670,000 m3/day of sales grade gas are produced. Figure 1 also shows the waste streams that result from the various stages in the process. Table 1 gives the current waste disposal methods and costs, provided by the industry members of the project steering c o m m i t t e e , for the waste stream categories modelled for the generic facility. These costs vary, depending upon whether the streams are classified as hazardous or nonhazardous. In the generic facility, the sludges are classified as hazardous or n o n h a z a r d o u s , depending upon their source and nature, and the filters and solvent wash fluids are classified as hazardous. F o r the modelling base case, the sludges were classified as nonhazardous. The waste streams included in the base case were the following: process run-off • open drain wastewaters • boiler blowdown •
O~
I
I cIosed Drain ) Wastewaters 180m3/year
I 1700 m3/year /
I Process Runoff Nj 17000m3/yearf
LPGand ) Condensate 230m3/day
I
FIGURE 1. Generic sour gas plant schematic used for modelling.
Blowdown Water Qty. 300 m3/yr
I
Solvent Wash Fluids Qty. 3 m3/yr Disposal Cost $4.5K
Solvent Wash Fluids ] Qty. 9 m3/yr Disposal Cost $13.5KJ
.~,utmm~/Yr/l(and otheruses)
Solvent Wash Fluids] Qty. 2 m3/yr | Disposal Cost $3.0K j
I Laboratoryand Maintenance I i
Fractionation, Treatment andStorage
SweeteningLiquld
60 m3/day
110 m3/day
•
Solvent Wash Fluids ] Qty. 3 m3/yr Disposal Cost $4.5K I
I
Sales Gas N 670000 m3/day j v produced ~ "
DeepCut
Process Pond Sludge I Qty. 110 m3/yr Disposal Cost $20.0K
l Process Wastewater I Qty. 19200 m3/yr Disposal Cost $57.5K I
I Wastewater System I (DeepWell)
Hydrocarbon Lube Oil I Qty. 10 m3/yr I DisposalCost $3.5K I
Gas Drying Sludge Qty. 1 m3/yr Disposal Cost $2.5K
MEA Sludge Qty. 3 m3/yr Disposal Cost $7.5K
Hydrocarbon Lube Oil Qty. 25 m3/yr Disposal Cost $8.8K
I LIQUID (60 hal/day) Solvent Wash Fluids I I '
83oo00m3/,~y¢/I Separation
RawGasInlet~
GAS
Filters - Glycol Qty. 8/yr Disposal Cost $0.2K
Filters - Glycol I Qty. 37/yr I DisposalCost $0.9K I
Filters - MEA Qty. 246/yr Disposal Cost $5.9K
RefrigerationI---
Filters- Glycol Qty. 19/yr DisposalCost $0.5K
eL_
I Dehydration -~'[Glycol lujeetionJ
Gas Sweetenin
CompressiHon3000 =~~[ (Amine-MEA)
Sulphur
652
H . D . R O B E R G E , R . P . S I K O R A , A N D B. W. B A E T Z TABLE1 BreakdownofCurrentWasteDisposalCosts~rtheGenedcPlant Disposal Cost ($ per unit)
Categories of Waste
Unit
Classification
Disposal Location
In-Plant
Transport
Disposal
Total
Sludges
m3
Nonhazardous
#
Nonhazardous
98 56 2,450 1 12
Wastewater Solvent-wash fluids
m3 m3
Lube oil
m3
22 20 50 10 12 3 3 15 51
40 40
Filters
Reclaimer Landfill Chem Security Rocky Landfill Chem Security Deep Well Deep Well Chem Security Reclaimer
160 116 2,500 12 24 3 3 1,500 350
Nonhazardous Hazardous
1
1,485 248
51
1Waste costs per unit depend on how much is handled or disposed of at any one time. 2Disposal costs to Chem. Security (an off-site treatment/disposal facility) includes the transportation cost component.
• • • • • • •
sludge - - gas sweetening sludge - - gas drying closed drain w a s t e w a t e r s lubrication oils filters - - M E A filters - - glycol solvents
size and configuration of the generic facility. Operating cost estimates were made, based on available information, as well as estimates for any additional operating costs for new equipment and any applicable savings in energy c o n s u m p t i o n and raw materials. These choices were made by the project steering committee, based on collective experience and technical judgement.
Table 2 gives a s u m m a r y of the information on the waste reduction options considered. The options were primarily taken f r o m (3) where they are described in detail. Ranges were given for the waste reduction potential, as well as for the capital and o p e r a t i n g c o s t s for the various options. Values within the ranges were chosen, consistent with the
4. MODELLING RESULTS Table 3 gives the general results obtained after solving the mathematical p r o g r a m m i n g model for the base run, as well as for the second case where the
TABLE 2 Summary of Waste Reduction Options
Option (Description) Flow diversion/housekeeping Equipment change Process change (MEA to MDEA) Inlet gas separation Filter optimization (oberg) Equipment optimization Filter optimization (cuno) Process optimization Recycling/reuse Material substitution Mechanical dewatering (centrifuge) Mechanical dewatering (plate & frame) Freeze/thaw dewatering
Capital Cost
Operating Cost I
$85,000 $35,000
$20,000 ($200)
$1,000,000
($800,000)
Affected Stream(s)
Waste Minimization Potential 2
Wastewater pond sludge Glycol filters Gas drying sludge MEA filters
30% 20% 20% 25%
Gas sweetening sludge Glycol filters MEA filters Glycol filters MEA filters Lube oil Solvent Solvent All sludge streams
50% 40% 70% 50% 70% 15% 75% 100% 80%
$45,000 $75,000 $0 $100,000 $0 $85,000 $0 $0
$2,600 $35,000 $600 $15,000 ($7,600) ($4,800) $900 $50/m3 + $5,000
$160,000
$45,000
All sludge streams
90%
$14,000
$25,000
All sludge streams
70%
1( ) indicates a cost savings. The operating costs do not include savings due to decrease in disposal costs. 2Based on waste flow units (see Fig. 1). 31ndicates a waste stream becoming nonhazardous.
Type of Reduction Source Source Source Source and hazard 3 Source Source Source Source Source Source Post-generation Hazard Post-generation (volume) Post-generation (volume) Post-generation (volume)
OPTIMIZATION MODELLING
653
TABLE 3 Summary of Optimal Results From Modelling Sludge Streams (nonhazardous)
Sludge Streams (hazardous)
$2,268,400 $1,000,000 $864,000 1.15 years $68,000
$3,075,700 $1,000,000 $1,075,000 0.9 years $280,000
Net cost savings over 5 years l Capital costs paid out Annual cost savings (year 1) Payback period 2 Annual disposal cost 3 savings
~Difference between savings and costs incurred for optimal strategy (net present value dollars). ZPayback period = [capital costs paid out/annual cost savings (year 1)]. 3Disposal costs include in-plant handling, transportation, and disposal.
sludge streams were considered hazardous. These strategies represent the best combination of waste reduction options to minimize Net Present Value (NPV) costs.
savings from the reduced waste flow and the change in treatment/disposal requirements, is 1.15 years. Table 4 illustrates the chosen options for the optimal solution of the base run. The results of the modelling were validated by industry members of the project steering committee. The following is a summary of the waste reduction achieved: • Process change from MEA to MDEA, resulting in a 25% reduction of filters (and with the remaining filters being nonhazardous), as well as a 50% reduction of gas sweetening sludge; • Equipment optimization for glycol filters, resulting in a 50% reduction in glycol filters; • Process optimization for lubrication oil, resulting in a 15% reduction of waste oil; • Material substitution for the solvent stream, resuiting in a nonhazardous waste stream; • Mechanical dewatering (by centrifuge) of the process pond sludges, resulting in an 80% reduction in the volume of sludge.
Non-hazardous Sludges As shown in Table 3, the net cost savings from implementing the optimal strategy is approximately $2.25 million over the 5-year time period. This total net cost savings is the difference between the savings (incurred from the reduced disposal and operating costs), and the costs (capital and operating costs paid out) for the optimal strategy. This value was determined from performing two computer runs: (a) where all the waste reduction options were considered, and (b) where no options were available. The optimal result from run (a) minus the optimal result from run (b) yielded the total net cost savings. The capital cost paid out is $1,000,000, for the process change options to convert from an MEA to an MDEA amine system. A large portion of the cost savings is due to the high energy savings resulting from implementing this option. The overall payback period for the capital investment, including cost
Hazardous Sludges For a second case, the sludge streams were considered to be hazardous wastes. The results of this case indicate how the overall cost could be affected by regulatory changes, which could result in a more stringent classification of waste streams; or if the plant was located in a different jurisdiction, where sludges of this nature are considered as hazardous wastes. The results show that the same waste reduction options as for the base case yield the optimal strategy; however, the net cost savings would be approximately $3 million. This is to be expected because the disposal cost for hazardous sludges is approximately 20 times higher than that for nonhazardous sludges. For this case, the overall payback period for the capital investment, including cost savings from the reduced waste flow and the change in treatment/disposal requirements, is less than 1 year. The results for this case were
TABLE 4 Selected Waste Reduction Options
Option Process change (MEA to MDEA)
Equipment optimization Process optimization Material substitution Mechanical dewatering (centrifuge)
Capital Cost
Operating Cost ~
$1,000,000
($800,000)
$0 $0 $0 $0
$600 ($7,600) $900 $50/m 3 + $5,000
Affected Stream(s) MEA filters Gas sweetening sludge Glycol filters Lube oil Solvent All sludge streams
Waste Minimization Potential 2 25% 50% 50% 15% 100% 80%
1( ) indicates a cost savings. The operating costs do not include savings due to decrease in disposal costs. 2Based on waste flow units (see Fig. 1). 3Indicates a waste stream becoming nonhazardous.
Type of Reduction Source and Hazard 3 Source Source Source Hazard Post-generation (volume)
654
H. D. R O B E R G E , R. P. S I K O R A , A N D B. W. B A E T Z
also validated by an industry member of the project steering committee.
5. CONCLUDING REMARKS The waste reduction optimization modelling approach used in this note is potentially useful for the evaluation of waste reduction alternatives and for implementing a structured approach to waste management planning. For the application to the generic sour gas plant, the obtained results illustrate that a set of waste reduction options exist that are costeffective and that their implementation would lead to considerable cost savings. The generic plant results should not be used for the evaluation of actual plants, but the developed approach could be utilized with specific plant inputs to generate a set of waste reduction options for consideration. The results from this type of modelling are not meant to provide definitive answers on which options to implement and exactly when they should be implemented. Rather, these results should be considered as part of an overall evaluation that may require more rigorous analysis based on other criteria to determine a course of action. For example, the MEA to MDEA process change would require more detailed process engineering work to determine site-specific applicability. The process of developing the required information and applying a systematic approach allows for the questioning of current waste management practices and methods, as well as identification of areas where information and knowledge is lacking. The modelling approach is most useful when the information used in developing the flowchart and the mathematical formulation is as reliable and accurate as possible and when the problem being studied has a significant degree of complexity. In order to effectively use the model, the team doing the waste reduction study should have reasonable knowledge of the industrial processes, as well as the waste streams generated and the current waste management practices and costs. Information on potential waste reduction options and their associated costs, as well as experience in mathemat-
ical modelling and using the appropriate software, are also required. The potential of the modelling approach could be further exploited by studying the effect of alternative scenarios and conditions, such as: • incorporating capital availability as a budgetary constraint; • fixing a total waste reduction objective of a certain percentage to be achieved by a particular year (this could reflect company policy and may include some waste reduction options that would not be implemented purely on an economic basis); • considering long-term liability costs, including off-site disposal and site clean-up; • varying waste flow quantities to determine the sensitivity of the system and the effect of variabilities in operation; • accounting for increases in waste flow generation in relationship to production rates; • varying the costs of some options within the estimated ranges; • increasing off-site disposal costs; • adding more waste streams and reduction options to the formulation for a specific application. The research reported in this note has shown that the developed optimization modelling approach is potentially useful for waste reduction planning for the short-to-intermediate term where significant capital resources may not be available for waste management activities at a given facility.
REFERENCES 1. Drabkin, M., Fromm, C., and Freeman, H. M. Development of options for minimizing hazardous waste generation. Environmental Progress 3:167-174 (1988). 2. Roberge, H. D., and Baetz, B. W. Optimization modelling for industrial waste reduction planning. Waste Management 14(1): 35-48 (1994). 3. Bromley Engineering Ltd. Waste minimization in the upstream oil and gas industry: vol. 1. Prepared for the Canadian Petroleum Association, Calgary, Alberta, Canada (1992). 4. Beak Consultants Ltd. Research study: Application of a waste reduction model to a sour gas plant. Prepared for the Canadian Petroleum Association, Calgary, Alberta, Canada. (1992).
Open for discussion until 28 February 1995.