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Integration of energy and wastewater treatment alternatives with process facilities to manage industrial flares during normal and abnormal operations - A multi-objective extendible optimization framework Monzure-Khoda Kazi, Fadwa Eljack, Nesreen Elsayed, and Mahmoud M El-Halwagi Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b03938 • Publication Date (Web): 27 Jan 2016 Downloaded from http://pubs.acs.org on February 2, 2016

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Industrial & Engineering Chemistry Research

Integration of energy and wastewater treatment alternatives with process facilities to manage industrial flares during normal and abnormal operations - A multi-objective extendible optimization framework Monzure-Khoda Kazia, Fadwa Eljacka*, Nesreen A. Elsayedb, Mahmoud M. El-Halwagi b a

Qatar University, Department of Chemical Engineering, College of Engineering, Doha, Qatar, P.O. Box-2713 Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3116, United States

b

Abstract. This work reports an extendible multi-objective optimization framework to find the optimal configuration of energy utilization and waste water treatment facility of the process. It incorporates two sustainable energy integration alternative tools e.g., cogeneration (COGEN) unit and thermal membrane distillation (TMD) as available add-ons to the process during normal operation and for abnormal situation management. The objective of the framework is to reduce the environmental footprint of abnormal flares by enumerating and assessing possible process configurations in order to manage flares from uncertain sources and to utilize unused energy resources for waste water treatment. The core of this optimization framework is developed using genetic algorithm and its objective function is aimed at minimizing the total annualized cost which accounts the fixed and operating costs of the system, the value of produced co-products (i.e., power, waste water treatment savings, income from permeate), and taxes/credits associated with GHGs. An ethylene process plant is used to demonstrate the applicability of the developed framework. The results of different alternative configurations demonstrate the economic, energetic and/or environmental trade-offs of integrating TMD and COGEN unit with the process plant both for flare mitigation and during normal operation. It was seen that total annualized cost (TAC) dropped around 35% and the payback period reduced from 7.01 to 4.61 years when integrated process plant (ethylene plant), utility unit (COGEN) and waste water treatment facility (TMD) was considered instead of separate divisions. Moreover, utility savings were achieved up to 8% and annual incomes from co-products were increased around 20% for the integrated ethylene plant, COGEN and TMD unit. Besides, prolific recycling opportunities of unused flare streams and treated wastewater were identified to make some valuable products from waste streams. Address all correspondence to: Fadwa Eljack, Qatar University, Department of Chemical Engineering, College of Engineering, Doha, Qatar, P.O. Box-2713; Tel: +974 404 4141; Fax: +974 485 2491; E-mail: [email protected]

1. Introduction 1.1. Abnormal situation

Abnormal situation is a disturbance or range of disruptions in a process that cause plant operations to diverge from its typical operation state to an abnormal and unfamiliar state. In such situations, plant personnel must intervene to correct the unknown disturbances with which the automated control systems cannot cope. Abnormal situations can be caused by process failure, equipment failure, human/working context errors, or some combination of the three. In most cases, it appears as a result of the interaction 1

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among multiple sources, which varies in their complexity and effect on the process. The ultimate consequence of abnormal situation is not always explosions and fires. Nonetheless, they can result in low productions, poor product quality, reduced job satisfaction, schedule delays, equipment damage, violation of environmental releases and other significant costs; in more serious cases it may endanger human life. The business impact of abnormal situations is not negligible, unexpected events can cost 3-8% of total plant capacity. Based on data from insurance reports, the cost of lost production due to abnormal situations is at least $10 billion annually in the U.S. petrochemical industry. The estimated costs of equipment repair, replacement, environmental fines, compensation for human casualties, investigation, litigation, etc., due to accidents to be another $ 10 billion 1.

1.2. Flare management

Flaring is a very common practice across all industrial plants during abnormal situation management. Industries usually flare to reduce the risk during process upsets, to dispose associated gasses, to maintain the product quality or to operate safely during process start up and shut down. Recently, industrial flaring and its negative impacts on environment, ecosystem and society have gained the attention of researchers, environmentalists and decision makers. It does not only waste potentially valuable source of energy, it also adds significant carbon emissions and other toxic materials to the atmosphere that have been linked to cause diseases in human health and contribute to global warming or several natural disasters

2-10

.

Therefore, numerous protocol, international agreement and steps such as Kyoto protocol, the United Nations Environment Programme (UNEP), World Bank Global Gas Flaring Reduction (GGFR) Programme have initiated to mitigate the impact of industrial flaring. Flare reduction can be possible by implementing legislative acts so that the industries become more concern regarding this issue. It can also be done by flare recovery and by the effective utilizing of those flaring streams 3, 11-14. Lastly, government can encourage industrial bodies by giving several incentives like credit for carbon-reduction

8, 15

.

Although, significant investments and initiatives have been made for flare reduction, the global gas flaring amount has remained largely stable over the past fifteen years in the range of 140-170 billion cubic meters (BCM) which is equivalent of 5% of global gas production and 300 million tons CO2 emissions 8. The prime challenges for this inevitable global gas flaring amount are: the increasing demand of industrial production with the increasing world population, the shortage of cost effective alternatives/integration for flare management and the lack of co-operation with neighbouring and competing industries. Moreover in some countries, companies have access to relatively inexpensive energy sources and often feel that managing associated gases is too much of a hassle. However, industry

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has become more conscious regarding the better management of their natural resources and has started to investigate means of flare management and recovering/storing these vented and associated gases.

1.3. Water management

Besides energy, water is another major resource for process and chemical industries. Water is usually not only the carrier of contaminants but also the media of energies. Excessive utilization of freshwater resources in industrial sectors affects negatively on water stressed regions

16-19

. Desalination is another

important source for industrial water in areas that have scarce fresh water resources. Sustainable costeffective strategies for water treatment and recycle/reuse can also play a key role in managing water resources. Moreover, increasing price of fresh water and strict environmental regulations on wastewater discharge, due to its adverse impacts on natural ecosystems, have been the major driving forces for developing systematic methods to minimize freshwater consumption as well as waste water treatment. As water and energy are closely related within process, the synthesis of combined water and energy systems grows lot of attention for recent investigations

17, 20-22

. Many design techniques have been developed to

achieve such goals. Among them, mathematical programming techniques and conceptual approaches are the two major methodologies 20, 23, 24.

1.3.1.

Effective flare and water management

Effective management framework and appropriate energy alternative tools are necessary for combining abnormal situation scenarios with water management in chemical industries. Despite the development of new methods and tools, no single approach will be appropriate for all projects to deal with industrial flaring and water treatment. Industry needs to be able to choose from among a variety of creative and commonly use approaches to address flaring and water concerns in specific operations. Therefore, an optimization framework is necessary, which will encourage and allow companies to select from among very different approaches in order to achieve the best practicable outcome in particular circumstances 1, 8. The decision makers of industries are often concerned to implement new energy alternative solution to their plant due to the initial capital investment and the operating/maintenance costs. Therefore, initial economic analysis of the new technologies/alternatives is very important for comparing with the standing process. It is also necessary to identify the optimal operating size and condition before exploring the feasibility of new technology. 1.4. Energy integration alternatives

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Big picture first, details later- this concept is very helpful for solving process integration problem. The global insights of the process and the root causes of performance limitations will help to generate effective solution alternatives to achieve sustainable design and operating strategies. It was shown that COGEN unit can be used as a flare mitigation tool and TMD unit can be used as water management tool 8, 11, 25, 26

. In this work, some heuristic procedures for the simultaneous synthesis of water and energy using

cogeneration (COGEN) unit and thermal membrane distillation (TMD) as energy alternative tools has been explored in terms of techno-economic and environmental aspects during normal plant operation and for abnormal situation management. Afterwards, the possibility of integrating COGEN and TMD with process plant has been investigated to maximize the utilization of available resources and to make the solutions more impactful.

1.4.1.

Cogeneration unit (COGEN)

Cogeneration (COGEN) unit has become a mainstream practice in different industries due to its high economic, environmental and energy-savings potentials

8, 27-32

. It is an integrated system where multiple

forms of useful energy (usually mechanical and thermal) can be generated concurrently using a single fuel. COGEN unit mainly comprise of a boiler and a turbine where dual industrial process requirement of

Figure 1. Cogeneration unit.

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power and heat can be satisfied simultaneously (see Fig. 3). Boiler produces required steam to generate heat according to the process demand and the steam is also used to operate a turbine to produce shaft work for power generation. Recently, researchers have explored several possibilities of using COGEN unit as potential flare mitigation tool by reusing waste flare streams that mostly contain combustible hydrocarbon with significant heating values during process upsets

8, 11

. COGEN unit can be used in flare

management by recycling some portion of flare streams in its boilers as the supplement of fresh fuel for steam generation and thereby reduce the amount of GHG emissions.

1.4.2.

Thermal membrane distillation (TMD)

Thermal membrane distillation (TMD) is an emerging technology which has significant potential for industrial water recycle/ reuse and desalination

25, 26

. There are several technologies for industrial

wastewater treatment and seawater desalination, which can be broadly classified as thermal processes, membrane systems and other technologies. TMD lies between thermal and membrane technologies. Although, TMD was first developed in 1960s 33, initially it was not so successful due to relatively high cost and low performance of the membranes. Recent advancement of membrane technology enhances the performance of the membranes in terms of mass and heat transfer rates, which has improved the prospective of cost-effective commercialization of TMD

25, 34-37

. TMD is a non-isothermal separation

process through a hydrophobic, microporous membrane where the driving force is the difference in chemical potential across the membrane which is strongly dependent on the vapour pressure difference between the feed and the permeate side. The vapour pressure difference can be created by heating up the feed solution to be distilled to some moderate temperature and allowing vapour liquid equilibrium to take place

25, 33, 38, 39

. Therefore, there is a unique opportunity to integrate TMD with process industries for

using the surplus heat/excess low-level heat from the process industries to drive the TMD. It will reduce the heating utility needed to run TMD and also can reduce equivalent amount of cooling utility demand for the process. Furthermore, TMD can act as a waste water treatment or recycling tool to provide fresh water for the industrial facility after desalination. Figure 2 shows a system schematic of TMD unit.

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Figure 2. TMD system schematic.

TMD has many features which distinguishes this process from more conventional water recycle/reuse and desalination process such as reverse osmosis and thermal evaporation. It can operate at lower operating pressure and lower temperatures than the boiling temperature of feed solutions 25, 35, 39. It can treat highly concentrated feeds and can produce high-purity permeate products. It is also compact in size compared to other technologies and its modular nature gives the extra advantage to add more modules depending on process demand. Modular nature also helps to do the maintenance without completely shutting down the plant. Further details regarding TMD and its advantages can be found in the literature 25, 26, 33, 35, 38. TMD can ultimately play a vital role for flare management during abnormal situation. If TMD and COGEN unit are combined together with the process, there will be scope to produce more heat from COGEN unit to supply those to TMD. This will increase the utilization of wasted flare steam during process upsets. Excess water produced from TMD can be supplied to the neighbouring facility or community which will also generate revenue for the plant from surplus resources.

In this paper, COGEN and TMD are presented as sustainable energy integration tools to manage flares during abnormal situation as well as waste water treatment facility for a running plant. A conceptual framework along with the design and optimization tools for integrating COGEN and TMD with industrial facilities is presented. Special emphasis is given to the integration of COGEN and TMD with the process because of the opportunities for potential savings in energy and cost for the industrial processes. 6


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Several possible configurations of using COGEN and TMD have been explored to find the optimal configuration for the maximum benefits. The investigation is accomplished using the proposed systematic approach and optimization framework. Finally, economic analysis is completed, which accounts the fixed and operating costs of the system, the value of produced co-products (i.e., power, waste water treatment savings, income from permeate), and taxes/credits associated with GHGs.

2. Systematic integration of flare and waste water management As the target of this work is to utilize the surplus energy from industrial flaring to manage waste water treatment facility, a systematic approach is necessary to integrate flare and water management tools with process industries. It is not always certain that every tool will be feasible in terms of economic or environmental considerations

40, 41

. During the exploration of alternative tools a systematic approach can

assist to evaluate the performance of proposed tools on an equal basis. Before comparing several alternatives it is important to find their optimal performance based on techno-economic and environmental point of view. Suitable process integration framework for flare and waste water management should ensure the maximum usage of excess resources while minimizing the installation/operating costs and environmental impacts. In Fig. 3, a systematic approach is presented for simultaneous abnormal situation management and water management.

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Figure 3. Systematic integration of flare and water management.

Figure 1 suggests the following steps:

2.1. Task and targeting

Task identification and targeting goals are the preliminary steps towards sustainable process integration. Proper task identification and targeting will allow the decision makers to determine how far they can push the process performance and sheds useful insights on the exact potentials and realizable opportunities for the process. In case of flare and water management, it is necessary to identify the excess resources available in the running process i.e., unused flare streams during abnormal situations, unused low-level heat, waste water, etc. It will suggest the possible types of techniques (e.g., COGEN and TMD) that can be used to reach the targeted goal of effective management by proper utilization of surplus resources.

2.2. Input data

After that, process data mining and analysis is necessary to find out the appropriate input data for the process integration. In case of flare management, historical flare data is very important to identify the 8


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flare properties, components, quantities, durations or frequency of the events. This historical database helps to find out the excess energy sources or waste heat streams which are wasting through industrial flaring and also helps to identify the key flaring sources. Therefore, development and utilization of historical database is very important during abnormal situation management.

Additionally, the operating and design data are also required to supply as input data to flare management tools. In case of water management, identification of possible waste water sources, fresh water demand and information regarding waste water treatment facility is necessary. As utility requirement of the process is directly related with the different forms of energy, so the total demand and distribution of process utility will help the investigation team to search for effective integration tools.

2.3. Energy integration tools

After, task identification, targeting and initial data collection the next step will be to generate alternative solutions for sustainable design of flare and water management. In this work, the feasibility of the COGEN and TMD unit as energy alternative tools has been explored in terms of techno-economic and environmental aspects.

2.4. Possible design configurations

Once the tools are available for abnormal situation (i.e., COGEN) and water management (i.e., TMD), every possible configuration should be investigated to find out the optimum process configuration for the specific process industry. In this work, three possible configurations with COGEN and TMD will be presented along with the base case process (e.g., ethylene) plant.

2.4.1.

Scenario 1: Separate process plant + separate utility section + separate waste water treatment facility

In base case process (e.g., ethylene) plant, it is required supplying necessary process utility and fresh water to produce targeted product. In outlet streams there are some excess process heat streams and waste water streams. Furthermore, there are waste flare streams during abnormal situation. In Fig. 4, a basic configuration of base case ethylene process plant is shown. This configuration is studied as scenario 1 to get basis of the techno-economic comparison while considering mitigation alternatives.

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Figure 4. Schematic representation of ethylene plant without mitigation tool.

2.4.2.

Scenario 2: Integrated process plant and utility section (COGEN) + separate waste water treatment facility

In scenario 2, a COGEN unit is installed to work as a flare management tool for the process plant. Some part of the waste flare streams is recycled to the COGEN unit as supplement fuel feed to the boiler (see Fig. 5). COGEN unit produce require power and heat simultaneously for the process. Produced utility is reused in the process plant to reduce the utility demand from outside. Moreover, excess power can be sold outside if the process plant authority has enough collaboration with the neighboring facility. This type of COGEN unit can work as a supplier of necessary process utility requirement throughout the year and additionally it can work as a flare mitigation tool during abnormal situations. The anticipation here is offsetting part of the fresh fuel consumed will result in reduced overall CO2 emissions. In earlier works, it was showed that substantial amount of CO2 reduction could be achieved if it is feasible to utilize heat otherwise wasted with flared streams

11

. In reality, there is a need for significant modifications and

investment due to challenges such as over-pressure of streams in boiler/turbine, over-heating, severe

10


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turbulence, deficiency of strength in boiler/turbine materials, and the uncertainty associated with frequency, duration and amount of available streams 8.

The use of COGEN is attractive, however requires both capital investment and operational costs. These costs associated with COGEN are directly dependent on the size of the unit. In the specific application of utilizing non-continuous upset flare streams as feed to COGEN, there is a need to find the optimum COGEN capacity (i.e. amount of heating and power generated) that should be designed 8.

Figure 5. Schematic representation of integrating COGEN unit with processing facility.

2.4.3.

Scenario 3:Integrated process plant and waste water treatment facility (TMD) + separate utility section

Another possible configuration is to integrate TMD with process plant for saving energy and cost for the process (see Fig. 6). TMD is primarily driven by heat, so there is a unique opportunity for synergism with industrial facilities where there is typically surplus heat. Two benefits can accrue as a result of proper thermal coupling of TMD and process facilities: reduction in heating utility needed to run the TMD and an equivalent reduction in the cooling utility for the processing facility. Additionally, water can be 11



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integrated between the process and the TMD by treating and recycling waste water and/or providing fresh water to the industrial facility after desalination.

Figure 6. Schematic representation of integrating TMD network with processing facility.

2.4.4.

Scenario 4: Integrated process plant, utility section (COGEN) and waste water treatment facility (TMD)

In scenario 4, both COGEN and TMD units are integrated with the process plant. This configuration will provide the opportunity to use a flare mitigation tool and waste water treatment facility toghether. 12


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Moreover, it will facilitate to generate more heat to provide the driving force to TMD for producing more treated water; ultimately this will lead to more usage of flare streams and revenue from excess power and treated water. Possible configuration of integration COGEN and TMD with process facility is shown Fig. 7.

Figure 7. Schematic representation of integrating TMD and COGEN unit with processing facility.

2.5. Performance evaluation through multi-objective optimization

Lastly, all possible configurations should be compared on the basis of their optimal operating performance with the support of a robust optimization framework. This optimization framework will deliver optimum size of the tools, optimum operating conditions, optimum flare utilization and optimum waste water treatment facility. The added advantage of the multi-objective optimization framework will be to provide the economic, energetic and/or environmental trade-offs which able the end user to make an informed choice of suitable process integration configuration.

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3. Problem statement Given a process with known stream flow rates, compositions and operating conditions and the historical data of flaring events during abnormal operations (including flare cause, source and duration); also given is a waste water management technique (e.g., TMD) and a flare mitigation tool (e.g., COGEN); it is desired to maximize the use of available energy within flare streams to yield valuable products/services and to determine the best process configuration (optimum amount of wastewater treatment, environmental and economic impacts) of waste water management for sustainable process design. Thus, it is important to develop a multi-objective optimization framework to compare different strategies of energy integration based on techno-economic and environmental impacts. The possible alternatives need to be analyzed and screened to figure out the optimum process configuration for extracting the maximum benefit from the energy integration. The choice of the decisive possible configuration is not straightforward as their performance depends on various desirable/conflicting objectives.

Here, the problem includes the following givens: 1. Steady state mass and energy balances of each stream of a plant 2. Historical flaring data: frequency, flaring duration, flaring locations, flare stream amount, compositions 3.

Time profiles of the allowable ranges for the flows, temperatures, pressures, compositions, and other specifications [e.g., LHV, Wobbe index (WI)] of fuel feed to proposed alternatives

4.

Operating parameters, capital expenditures and operating expenditures for proposed solutions

5.

Amount of CO2 emissions from each flare stream during abnormal situation

6.

Regulatory limit on pollutant without penalty during flaring and amount of CO2 tax

7. Amount of waste water form the process that need to be treated

The multi-objective problem formulation looks to address the following questions:

1. What are the alternative tools for flare mitigation and waste water management? 2. What is the optimum process configuration for the given problem? 3. What is the impact of installing new techniques for mitigation approach based on economic analysis? 4. What is the optimum size of the proposed wastewater treatment facility? 5. What is the optimum amount of heat recovered from flared streams? 6. What are the optimum quantities and levels of utility requirements? 14


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7. How much and in which proportion of each flare steams can be recycled as fuel feed during flare management? 8. What are the realized CO2 reductions as a result of recycling flared streams? 9. Considering the implications of CO2 tax and credit, what is the potential amount of CO2 tax savings? 10. What is the savings from wastewater management? 11. What is the income from co-products?

The following are the key assumptions in this formulation: 1. The minimum energy demand for process is known from the mass and energy balance. 2. The flaring scenarios are completely discrete and random in nature. 3. A COGEN unit is adopted to mitigate the flaring impacts and also provide the required heat and power to the process. 4.

Boiler type in COGEN is fixed (which can handle all flare streams as fuel feed).

5. All flare streams due to upsets are available for use (No constraints from other side i.e., H2 content). 6.

CO2 emissions from upset streams are estimated using an in-house GHG calculator linked to the framework.

7.

The Wobbe index needed to determine energy values for flared streams are known.

8.

The LHVs of fuel components do not change with temperature.

9. The cost of COGEN unit is mainly contributed by the turbine and boiler cost. The variation in pump and other accessories costs for different operation variables are assumed as insignificant. 10. A TMD unit is the considered waste water management system in this formulation. It should be noted that the framework can handle any alternative technique the user desires as long as necessary inputs are provided. 11. The amount of available Low-level heat from COGEN and process waste streams is used as the driving force in this multi-objective optimization problem.

4. Extendible optimization framework for sustainable process integration

4.1. Multi-objective optimization methodology

The objective of this optimization framework is to provide decision makers sufficient degrees of freedom when they are considering several configurations of COGEN and TMD as flare mitigation and waste 15



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water treatment approaches for an existing process or for fresh process. The optimization framework aims to find the optimal size, optimal operating conditions, optimal flare utilization and optimal wastewater treatment facility of several COGEN and TMD configurations considering the technical, economic and environmental constraints respectively. The objective function is formulated to minimize the total annualized cost (TAC) which accounts for the fixed and operating costs of the system, the value of produced co-products (i.e., waste water treatment savings, income from permeate), the value of produced utility (i.e., heat, power) and taxes/credits associated with GHGs. Finally, this optimization framework generates Pareto fronts and economic comparisons of different alternative configurations; that demonstrate the economic, energetic and/or environmental trade-offs of integrating TMD and COGEN unit with the process plant both for flare mitigation and during normal operation. This optimization framework is not restricted to any solution technique or for any specific process. It is extendible in nature and can be used for potential energy utilization alternatives, where the required process and abnormal situation data are available to the decision makers.

Figure 8. Optimization framework to find the maximum benefits from possible process configuration

The proposed framework is shown in Figure 8. First, the given process is modeled in steady-state condition using computer-aided simulation tool (i.e., Aspen HYSYS). Different published data, historical 16


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and real time industrial plant data can be used to develop the database for modeling parameters and flaring scenarios. Abnormal situations are by nature discrete and not predictable; therefore, reliable flare data is necessary to represent these flaring incidences. For this study, flaring historical database was created in-house with input from industry. The project has an industrial advisory board that consults on such activities. In the developed database, each flaring event is characterized by frequency, duration, flared amounts, and composition of the flared stream. The process utility requirement is determined using thermal pinch analysis and process integration methods

11, 41, 42

. The process utility requirement, the

external utility requirement and the flaring events are needed to specify the problem constraints. In the optimization framework, equipment sizing, amount of flare utilization, amount of wastewater treatment and operational variables are considered as decision variables.

The overall optimization model is nonlinear and non-convex due to multiple optimization variables and constrains. There are several feasible points for the given constrains but the relevant solution must be the global optimum point. Hence, there is a need to use optimization techniques that are capable of handling such complex systems. NLPs have huge body of very complex mathematical theory. For this reason, there are a variety of different ways to solve the NLPs, with some methods being better than others in certain circumstances. However, there is no one method that works the best in all situations.

The proposed optimization approach is based on a combination of stochastic method and linear programming (LP). Global optimization toolbox in MATLAB is used to converge the optimization formulation, which employs one of the most powerful and robust multi-objective optimization algorithms, namely NSGA-II (non-dominated sorting genetic algorithm)

43, 44

. In this algorithm, solutions are

categorized based on sorting non-dominated solutions into layers spearheaded by Pareto set. The convergence is measured by change in relative distance or spread of Pareto set members

43, 44

. After

convergence of the optimization algorithm, the set of optimal solutions will be received as Pareto fronts. The developed problem formulations can also be solved using global optimization solver in LINGO 14.0 or any kind of commercial optimization software as different platforms have their individual advantages.

4.2. Problem formulation

The optimization problem presented aims to determine the optimum process configuration for simultaneous flare and waste water management based on techno-economic and environmental analysis. The objective function will be expressed in terms of the total annualized cost. The total annual cost (TAC) can be calculated by the following equation: 17



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= + − −

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(1)

Where, annual operating cost includes total raw materials cost (i.e., feed cost and catalyst cost) and total utility cost (i.e., fuel cost, high pressure steam cost, cooling water cost, electricity cost and other utility cost). Annual fixed cost reflects the capital cost of the proposed mitigation tools. Annual income considers the revenue from co-products, heat utility savings, power generation, waste water treatment savings and income from permeate. Lastly, the environmental cost in terms of CO2 tax savings is considered for total emissions.

This optimization framework has been formulated as a multi-objective optimization problem to determine the produced amount of heat and power, while fulfilling process constrains. The solution domain is defined by decision variables, namely operating parameters to be changed in order to optimize the process. They are fresh fuel need, amount of recycle flare streams from each sources and the size of the alternatives. The penalty function was evaluated for implicit process constraints: Wobbe index of each flare streams, flow rate constrains, temperature and pressure constrains.

The total annual cost objective function described by the formulation in equation (1) can be expressed as following: Objective function: TAC = 1.3 × .. F&./. × C'()* ×.0 H, + 1411 + 431 − 7 + 16131 + 9 × :; -... .... -............./.............0 -...................../.....................0 12345 ?@A BCCD=E 2FGH=IJCK 1LMI

+k × Y + 1115 × :?@A -......./.......0 -......../........0 O × CPQR*)S + k O × CT(SURV) + 58.5 -................./.................0 12345 ?@A BCCD=E ZJ[G\ 1LMI l i

f −P H0, − ^ ×../. :_GHYG=IG -. ...0 − bc c ee& − eeRg& fRg& m × Cnop >. -).× /. -........./.........0 ?@A -......./.......0 Rjk gjk 12345 1=HqLC ?=[ r=sJCKM

BCCD=E `CaLYG

18


(2)

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Process constrains: :YJ[,Y=[ ≥ :YJ[ ≥ :YJ[,YJC

(3)

vYJ[,Y=[ ≥ vJ ≥ 0

(4)

YJ[,Y=[ ≥ J ≥ YJ[,YJC

(5)

xYJ[,Y=[ ≥ xJ ≥ xYJ[,YJC

(6)

The detail explanation for each part of the objective function is provided in Table.1.

Table 1. Details of the objective function formulation. Equations Comments In cogeneration unit, fuel cost is the substantial of all costs of yzz{|} ~|~z ~ operations and materials during production. Over 90% of total = . × × {} × generation cost is associated with fuel. The maintenance cost of = . × × c | × / × × the cogeneration unit is about 30% of the fuel cost 41, 42, 45-47. Where,

= c × × ¡

yzz{|} ~|~z ~ ¢£¤ = ¥ + ¥ − ¦ + + § × ¨©ª yzz{|} «¬ ~ = × ®~} + × ¢{z

+ × ¥¯° = × × × ¢ × .¯¯ × ±.¥°

Here, ¦ is the water recovery ration in kg permeate/kg raw feed, § is the activity coefficient of water in the feed to the TMD and :; is the total flow rate of the raw water to be treated in kg/s. This annual operation cost of TMD is calculated considering the key operating costs such as pretreatment cost, labor cost, brine disposal cost, pumping cost etc. 25. The cost of the boiler (CBoiler) is related with the amount of heat (Qb) transferred to the steam (Btu/h). Two factors named NP and NT are used to account the cost variation due to operational pressure and superheat temperature in the boiler respectively. The capital cost of the turbine is proportional to the amount of turbine shaft power output (Btu/h) 41, 42, 45.

Where, ²³´µ = × ¶· × ¶¸ × .¯¯ ¹ ¸µ¹´º = ¥¯° × ».¥° ¼

The estimated cost of the membranes is $90/m2 and the Lang yzz{|} «¬ ~ ¢£¤ factor of 5 is used to get the fixed cost of the membrane = ´½¾ ¿³À¼ ³ ÁÁ¹µº Á³¾À 25, 48 . Assuming that the membrane I equivalent to + º³ºÁÁ¹µº ´½¾ ¿·´¼ ´ºÂÀ¼Áº¼modules depreciating the $90/m2 portion using a 4-year linear = °Ã. ° × ÄÁ + ° × ¨¸ÅÆ depreciation scheme with no salvage value. The rest of the fixed cost of the membrane modules is depreciating using 10-year linear depreciation scheme with no salvage value. Here, Am is the area of the membrane in m2 and WTMD is the feed flow rate in TMD in kg/s.

19



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Annual income (COGEN) × »¼ × Ç È = × . ¥ Annual income (TMD) = Éª × ¨»µÁ¼

|~z |« |ÊzË = Ï

Î

bc c ÍÌ − Ì m × |« j j

Page 20 of 36

The annual income is calculated from the electric power generation ($/kWh). ^> is the unit selling price of the permeate, and :_GHYG=IG is the annual flow rate of the permeate 25. Here, ejikp as the amount of pollutant j that sink k would emit from source i during mode p and eUjp is the regulatory limit of that emission during mode p.

The following modeling equations are used for the thermal polarization coefficient in TMD (with Tb,f in K): Ð = 1.104 − 0.00086q,; And for the permeability: k.ÔÔÕ Ñ> = ÑÒÓ × Y Where × ÑÒÓ = 7.5 × 10kk Ø . . x. Ù k.ÔÔÕ And Tm is the average membrane temperature in K. The overall optimization model is nonlinear and non-convex due to multiple optimization variables and constrains. There are several feasible points for the given constrains but the relevant solution must be the global optimum point. Hence, there is a need to use optimization techniques that are capable of handling such complex systems. NLPs have huge body of very complex mathematical theory. For this reason, there are a variety of different ways to solve the NLPs, with some methods being better than others in certain circumstances. However, there is no one method that works the best in all situations.

The proposed optimization approach is based on a combination of stochastic method and linear programming (LP). Global optimization toolbox in MATLAB is used to converge the optimization formulation, which employs one of the most powerful and robust multi-objective optimization algorithms, namely NSGA-II (non-dominated sorting genetic algorithm)

43, 44

. In this algorithm, solutions are

categorized based on sorting non-dominated solutions into layers spearheaded by Pareto set. The convergence is measured by change in relative distance or spread of Pareto set members

43, 44

. After

convergence of the optimization algorithm, the set of optimal solutions will be received as Pareto fronts. The developed problem formulations can also be solved using global optimization solver in LINGO 14.0. Both these platforms have their individual advantages.

In previous publication, this optimization framework considered a single energy utilization alternative, that of a COGEN unit as flare mitigation tool 8. The optimal COGEN size, its optimal operating 20


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conditions and trade-offs between the economic, environmental, and energetic aspects were presented through Pareto fronts. In this work, the usage of this optimization framework has been extended both for COGEN and TMD, which demonstrates the extendible nature of the developed multi-objective optimization framework. As, detail specific equations for COGEN and TMD units are available in different publications

8, 25, 42, 45

, this optimization framework was used to combine those necessary

equations to identify the best possible configuration for simultaneous flare and wastewater management/desalination unit using COGEN and TMD. This work follows the same algorithmic flow chart as mentioned in COGEN sizing 8 , here the framework was expanded to include a set of equations to model a TMD system. The formulation here looks to solve different configurations using both COGEN and TMD. This offers extra degree of freedom to the decision makers to choose the energy utilization tool(s) and fit it in the optimization framework while still being able to receive as output a set of Pareto fronts that determine their optimum configuration, optimum operating conditions, in addition to the economic, environmental and performance tradeoffs.

5. Base case study – ethylene process The step by step procedure of integrating abnormal situation and water management tools with the aid of an optimization framework are explained here for a base case process plant. An ethylene plant is chosen as base case study because ethylene is a key intermediate that feeds many industries, including plastics, textiles, toiletries, detergents, paints, antifreeze, light weight car components, medical supplies, polymers, pharmaceuticals and specialty chemicals. According to Global Data the market for ethylene could reach US $ 177.83 billion by 2017, climbing from US $ 131.88 billion in 2012 at a Compound Annual Growth Rate (CAGR) of 6.2%. Moreover, less expensive ethane derived from wet shale gas makes ethylene production highly attractive and drives the attention of large firms such as The Dow Chemical Company, Chevron Phillips, ExxonMobil and Royal Dutch Shell to construct large scale ethane crackers to produce cost-advantaged ethylene 49. Notwithstanding its importance, the operation of ethylene plants like other industries includes flaring during abnormal situations and waste water streams. 5.1. System configuration Fig. 9 illustrates the process flow diagram and main equipment locations for a typical ethylene process. The bold- dashed arrows define major potential flaring sources in the ethylene process. A detail process flow diagram is necessary to identify potential energy streams and to target the performance goals for the optimum process performance. A flare source is a stream that is sent to flare during process upsets. The process starts with acid gas removal unit, where sour gases are separated to produce sweet gas as feed to 21



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the cracking furnace. Steam to gas ration in the furnace is 1 to 3. The cracked gas then passes through the quench tower. Here, the light gases (C4+) mixture is separated and sent to a three stage compressor section. After that, the process stream is further treated in the CO2 removal unit and later it is dried. The developed ethylene base case process uses front-end based technology, where the acetylene hydrogenation unit located at the deethanizer’s overhead product and before the demethanizer. The ethane and lighter gas mixture from deethanizer unit are recovered and sent to the 4th stage compressor and the heavier mixture is sent for further separation. The light gases enter the acetylene hydrogenation unit, where acetylene is totally converted to ethylene. In the demethanizer unit, the methane and light components are recovered from top and from bottom the ethane and ethylene are directed to C2 splitter unit. There the ethane is recycle back to the front of the process and combined with the feed. Flare F 25

P-54 4 Feed (Sour Gas) 298K 23psia

1

Ethane Recycle Reducing Valve

Steam 412 K 50 psia

Flare A

7

6

2

351K 1200K 23psia 23psia Gas Cooling Quench Tower Mixing Valve Cracking Furnace 9 H2O 263K 23psia

Desulfur 3

1st Stage comp.

8.4

8.2

8.1

P-78

318 K 23 psia

5

8.5

8.3

318K 56psia 2nd Stage comp. cooling

318 K 137psia

3rd Stage comp.

Cooling

Cooling 318 K 335 psia

H2S 10 Flare B 11

CO2

Flare G 22

CH4

13

H2O

191.8 K 223.7K

16 334.7psia 318 K 335 psia

318 K 335 psia 12

CO2 Removal

14

Dryer

Compressor

298.7K 334.7psia 15

Cooler

380.2 K 336.9 psia 17

Flare D

Flare C

249.5 K 464 psia

325K 464psia 19

18

Heater

DeEthanizer

Flare E 244.6 K 270 psia

P = 460 psia

20

21

355.7K 464psia

194.3K 464psia

Cooler

Acetylene Hydrogenation

23 265.3K

DeMethanizer

Ethylene 24

Ethylene Splitter

Input stream Output stream Possible flare location C3+ Sepration

Figure 9. Ethylene process base case

All necessary material and energy flows are calculated and used to determine the process heating and cooling utility requirements.

From the detailed process flow diagram of the base case ethylene plant, it can be identified that there are flaring streams which are realizing significant amount of energy and these streams are also resulting negative impact on ecosystem in terms of GHG emissions (see Table 2). The range of Wobbe index (WI)

22


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for different flare streams in Table 2 suggests that they can be used as the supplement of natural gas or other types of fuel gases based on the combustion energy output.

5.2. Historical database

Historical flaring events data was created for the base case and validated by industrial collaborators working with the research team. This historical data includes events that lead to flare (e.g. compressor failure, off-spec product, deviation from operating conditions, etc.), the duration and frequency of each flaring event. The amount of flare and composition of the flared gas are obtained from the base case material balances.

5.2.1.

Key flaring sources and waste water streams

In this base case study seven potential flaring sources (Flare A – G) are identified in the process, see Figure 9. These streams can be flared as a result of various abnormal situations or processes upsets. It should be noted; one or multiple streams that can be flared as a result of a single event, for example during compressor failure all streams are flared. In the presented case study, it is assumed that various process upsets will result in an average 12 (non-continuous) hours per annum. Hence, it is assumed that each stream (A-G) will be flared an average of 12 hours per year. The flare streams’ flows and operating conditions used in the case are presented in Table 2. This data is extracted from developed mass and energy flows and from the historical data.

Table 2. Ethylene flared gas streams specifications from mass balance

Flare

Location

Mass

Name

Temperature

Pressure

flow (ton/h)

(K)

(psia)

Wobbe

CO2

index

emissions (ton/yr)

Flare A

Top of quench tower

190.59

318

23

47.14

6301.93

Flare B

Deethanizer overhead

171.12

223.7

334.7

49.37

5970.53

Flare C

Acetylene

171.12

355.7

464

49.47

5970.63

124.61

265.3

461

60.65

4636.40

120.74

244.6

270

60.21

3868.10

hydrogenation outlet Flare D

Demethanizer bottom product

Flare E

Off-spec Ethylene

23



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Flare F

Ethylene splitter

Page 24 of 36

21.87

244.6

270

62.87

768.30

46.50

191.8

460

40.82

1334.14

overhead/ Ethane Recycle Flare G

Demethanizer overhead

The base case ethylene plant also has some process streams which are realizing waste waters from different sources i.e., quench tower (stream 9 in Fig. 9.) and dryer (stream 13 in Fig. 9).

5.2.2.

Operational and design data

Along with historical database of flaring, operational and design data of the plant are necessary as input data to explore the scope of possible energy alternative process synthesis. Furthermore, these data can be used as the decision variables to the optimization framework to find out the optimal operating conditions for the given process. For the ethylene base case plant, some operation and design data are obtained with the help of materials and energy balances using simulations. Later, the values are verified with the industrial collaborators. Figure 9 contains the temperature and pressure values of the corresponding streams. Heat integration of the given ethylene process was accomplished to identify the minimum utility requirements. The grand composite curve in Fig. 10 shows that the minimum cooling utility for this base case ethylene plant will be 184.49 MW or 629.50 MMbtu/hr

40, 41

. Therefore, significant investment is

required for the cooling utility. This large amount of low level heat can be used in TMD, which will ultimately reduce the cooling utility cost.

24


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Figure 10. The grand composite curve from heat integration

5.2.3.

System parameters

The following are the key assumptions used in developing the base case: 1. The capacity of the ethylene plant is 900,000 tons per annum. 2. COGEN capacity is capped at 400 MMBtu/hr at 100 psi. The value covers ethylene process heating and power needs and excess capacity. This target can be modified and is dependent on the user spec. 3. The feed stream contains 96 wt.% of ethane, 3 wt.% H2S, and 1 wt.% of CO2 49. 4. The conversion rate of this furnace is assumed as 87.6% and yield as 67.1%. This furnace is operated at 1200K and 23 psia 49. 5. The average hourly flaring rate per flare stream per annum for this process is 12 hour. 6. The boiler efficiency is 40% and it operates at a maximum pressure of 280 psi. 7. The turbine used in the COGEN facility can operate at a maximum inlet temperature of 450oF and maximum flow rate of 25,000 lb/hr. 8. The minimum superheat temperature at the exit of the turbine is 5oF. 9. The power price is 0.05$/kWh and the fuel price is 1 $/MMlb, the generator efficiency is 40%, the pressure factor in the generation cost is assumed as unity. 10. The heating utility cost (heating fluid at 590 K) is $6/109 J, the cost of low pressure steam at 420 K is $2.5/109 J and the cooling water cost at 293 K is $4/109 J. 11. The TMD to be used in wastewater treatment facility uses the polypropylene hollow-fiber membrane MD020CP2N (manufactured by Microdyn). The fibers have an inside diameter of 1.5 mm, an outside diameter of 2.8 mm, and a length of 0.45 m. Details of these modules are given in previous publications 25, 48. The maximum allowable temperature for heating feed is 363 K. 25



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6. Results and Discussion The optimal performances of the possible design configurations of energy utilization tool were explored using the proposed optimization framework of Section 4. Recall that COGEN and TMD (mentioned in Section 2.4) are the energy utilization tools presented in this study. The proposed multi-objective optimization methodology provides the set of Pareto solutions for power utility versus heating utility requirements for each design configuration (see Fig. 11), depending on the plant input data (i.e., operational data, design data, historical flare data). Note that for each possible design configuration, corresponding Pareto fronts are generated. The Pareto Curve here shows the trade-off between these conflicting objectives and provides a wide range of optimal operating points for the decision maker. In figure 11, the left most point on the Pareto curve implies that maximum power utility can be obtained when heating utility is minimal. Whereas, the right most point refers the amount of maximum heating utility correspond to the lowest power utility. The solutions mid of two extremist points are showing the tradeoffs between two objectives. Each point of this Pareto front is an optimum solution that contains a set of valuable information regarding the techno-economic and environmental variables such as CAPEX, OPEX, CO2 tax savings, annual income, amount of flare utilized, etc. 8. As mentioned previously, the aim of the work presented here is to compare the different possible process integration configurations (COGEN and TMD) using the extendible optimization framework; that is robust regardless of the number of considered alternatives and configurations. The framework key requirement is an equation set that describes the alternative and relevant input data. In this section, analysis for the four alternative configurations (shown in Figure 4-7) is conducted. The analysis selects a single optimal point on the power-energy Pareto curve. From this point, the optimization algorithm is able to provide various process, energy, economic, and environmental data related to each configuration. The decision maker can make informed choice depending on their directive. Some directives are maximize profitability, which translates to the objective of maximizing total annual income; reduce CO2 tax, minimize utility costs, lower GHG emissions, maximize flare recovery, maximize wastewater treatment, target increased freshwater production, etc. The variety in directives comes from the fact that this tool is useful to various departments within an industrial facility such as environmental engineers, technical department, utility department, and management including upper administration.

26


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When producing power is important to decision maker

When producing heat is important to decision maker

Figure 11. Power –Heat Pareto Curve.

If the decision makers choose 40 MMBtu/hr as the target heating utility demand for the process (second left point of Fig. 11), they can get the detail economic comparison for all four energy utilization configurations for different directives.

Directive: invest in energy integration alternative with shortest payback period

If the directive is to consider energy utilization technique that will recover the capital investment quickly, the decision makers needs to look at the comparison of the total annualized cost (TAC) and the payback period (PBP) for different configurations (see Fig. 12). It is always desire to keep the plant’s TAC and payback period as low as possible. It is evident that scenario 4 gives the lowest TAC 573.16 $MM/yr and the shortest PBP of 4.61 year, and hence it is the best option for this directive. This lower payback period is expected because we are recovering waste heat (from flares during abnormal situation) and converting to value added product which treated wastewater (Fig. 12b). These interesting states will also change the typical view of the decision makers towards adopting a new design modification or system integration. The additional investment costs for the COGEN and TMD units were minimized by the advantages of COGEN and TMD unit. It was obvious that the initial investment for installing COGEN and TMD will be higher; therefore the annual fixed cost for integrating ethylene plant, COGEN and TMD was 37% higher than the base case ethylene plant (Fig. 13a). The reason for the overall reduced TAC is that the additional investment of COGEN/TMD installation was traded off by lower operation costs (e.g. energy, wastewater treatment, fresh-water production and CO2 savings). It should be noted that the results provide the user a lot of other valuable information. Consider the TAC of scenario 1 (separate ethylene plant + separate utility unit + separate wastewater treatment facility) and 27



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scenario 2 (integrated ethylene plant and utility unit (COGEN) + separate wastewater treatment facility): the TAC for both scenarios is around 880 $ MM/yr and they both have similar PBP of around 7 years (see Figure 12b). However, in scenario 2, the integrated COGEN unit with the process offers the added environmental benefit of reducing carbon footprint at no additional cost when compared with scenario 1. The justification for this observation is the CO2 tax savings offset the COGEN capital investment. This is how the tool is able to show the tradeoffs. The tool also lets the user know that for this scenario 2 1.32 × 10Û kg of flare was utilized/reduced, which translates to 0.21 $MM/yr CO2 tax savings.

For scenario 3 (integrated ethylene plant and wastewater treatment facility (TMD) + separate utility unit), the TAC dropped around 27% from the base case; this reduction was possible because of the extra income of 320 $MM/yr from permeate (freshwater) and savings from the utility cost (Fig. 12a).

(a)

(b)

Figure 12. Comparison of (a) total annualized cost and (b) payback period for all possible design configurations with COGEN and TMD.

Directive: lower operational and utility costs

Consider a directive that aims to reduce operational and utility cost. Figure 13b provides the comparison of annual operating costs for all scenarios: scenario 4 (integrated ethylene plant + COGEN + TMD) shows the lowest annual operating cost of 469 $MM/yr. Whereas, Fig. 13c shows annual utility costs for all scenarios and scenario 4 is the optimum process configuration in terms of utility savings (8% from the base case Scenario 1). Thus, scenario 4 provides the lowest TAC, the shortest payback period and the lowest operational/utility costs. These improvements was possible because of the maximum flare utilization as the supplement fuel feed and also for the utilization of low level heat of the process and COGEN unit into the TMD unit.

28


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(a)

(b)

(c)

(d)

Figure 13. Comparison of (a) annual fixed cost, (b) annual operating cost, (c) annual utility cost and (d) annual income for all possible scenarios.

Directive: Increase Income

For a directive of increase income the algorithm provides data that considers income from CO2 tax savings, heat utility savings, power generation, waste water treatment savings and income from permeate (see equation 2). The configuration that gives maximum annual income is scenario 4; it is around 20% higher than that of the base case ethylene plant (see Figure 13d). The income breakdown is: heat utility savings is 23.29 $MM/yr, income from power generation is 0.56 $MM/yr, wastewater treatment savings is 1.73 $MM/yr, income from permeate (freshwater) is 319.68 $MM/yr, income from other co-products is 1260 $MM/yr and income from CO2 tax savings is 0.21 $MM/yr. Environmental Directive The rising concerns with environmental impacts have given rise to directives to reduce carbon footprint; global warming issues have brought about directives to reduce flaring and there is the continuous concern with wastewater treatment and the production of clean water. The integrated ethylene with COGEN and

29



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Page 30 of 36

TMD (scenario 4) gives the lowest carbon footprint 2.76 × 10Õton CO2/yr to the environment during

abnormal situations, and provides the maximum water production of 1.47 × 10Ô ton freshwater per year.

Figure 14. Amount of recycled flare streams as supplement fuel feed in COGEN unit from all flaring sources.

The proposed optimization framework is also useful to identify the optimum amount of each recycled flare streams. The summary of the optimum flare utilization for specific heating demand (40 MMBtu/hr) is shown in Figure 14. This optimization approach allows finding the ratio of the flare mixture which will be more efficient in terms of Wobbe index and heating value.

The abovementioned details techno-environmental and economic analysis shows that optimum benefit can be achieved from having an integrated ethylene plant with COGEN and TMD units (scenario 4). Moreover, it also reflects the necessity of process integration with a suitable optimization framework.

7. Conclusion This work has proposed an extendible multi-objective optimization framework, which can be used as an analysis tool that assesses the impacts of various energy alternatives for the utilization of flare gas streams and for the waste water treatment of the process. The work of Kazi et al. 8 is extended by integrating the TMD with the process along with COGEN. The main outputs of the optimization framework are a set of 30


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optimal solutions in the form of Pareto Fronts, that show the tradeoff amongst the competing objectives (e.g. economic, technical, and environmental). The scope of COGEN unit as flare mitigation and energy utilization tool, and the possibility of TMD unit as waste water treatment facility have been investigated during normal operation and for abnormal situation management. The proposed framework incorporates those energy integration alternatives (COGEN and TMD) as available add-ons to the process by considering the techno-economic and environmental factors.

Four process configurations were considered as the possible solution for optimum energy utilization. Finally, the detail economic analysis of those possible configurations was studied with the help of a base case ethylene plant to manage flares from uncertain sources and to utilize unused energy resources.

The results showed that the integration of COGEN and TMD unit simultaneously with the ethylene process was the optimum configuration in terms of payback period, total utility cost, total operating cost and total income from co-products. It has shown that 8% total utility cost savings can be achieved by this optimum configuration. Moreover, this configuration can generate extra 20% revenue from its coproducts (i.e., carbon tax savings, heat utility savings, power generation, waste water treatment savings and income from permeate) if the COGEN and TMD units are harnessed properly with the base case ethylene plant. Although, the initial capital investment will be higher than the base case plant, the annualized total cost will be lower because of the cost savings and extra income. The payback period of the base case ethylene plant can be shortened from 7 to 4.61 years by applying these energy integration alternatives.

This multi-objective optimization framework is generic and extendible in nature because it can be applied to a wide range of industrial processes or systems, and the user has the flexibility to include a range of process, environmental and economic constraints with the only limitation being the availability of data, such as information related to the process and flare upset. The final Pareto fronts resulting from this optimization framework will help the end user to make an informed choice of operating point depending on economic, energetic, and/or environmental trade-offs. The optimization framework is also able to provide detail information about the integrated process (i.e., the size of the alternative tools, the amount of the recycled streams, the amount of GHG gas emission etc.) to the decision makers.

Nomenclature ae

Electrical power price ($/kWh)

af

Unit fuel cost for the fuel f ($/MMBtu) 31



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Am

Area of the membrane (m2)

As

Correlation parameter

BwB

Temperature-independent base value for the permeability (kg/m2.s.Pa.K1.334)

Bs

Correlation parameter

CBoiler

Capital cost of boiler ($)

CFuel

Fuel cost ($)

Ctax

Tax for CO2 emissions ($/ton)

CTurbine ejp eijkp GHG

Capital cost of turbine ($) Regulatory limit on pollutant j flared without penalty at flare in mode p Amount of pollutant j that sink k would emit in mode p for 1 MMscf of fuel gas flared Green house gas

FP

Flexibility factor for the increase in pressure 47

h

Enthalpy (Btu/lb)

hf

Latent heat (Btu/lb)

Hγ

Annual operation time (h/year)

kf

Factor used to annualized the capital costs (years-1)

LHV

Lower heating value (Btu/lb)

NP

Factor to account for the operational pressure

NT

Factor for the superheat temperature

Pe

Profit obtained for the electric power produced ($/kWh)

Pt

Turbine shaft power output (Btu/h)

Qb

Heat required in the boiler (Btu/hr) Amount of heat transferred from the combustion of fuel f to the steam at the boiler (Btu/h) Total annualized cost

Qf TAC q,;

Temperature of the feed in the bulk (oK)

Tm

Average membrane temperature (oK)

Tsat

Saturation temperature (oF)

s

^> :;

:_GHYG=IG WTMD

Entropy (Btu/lboF) Unit selling price of the permeate Total flow rate of the raw water to be treated (kg/s) Annual flow rate of the permeate (kg/s) Feed flow rate in TMD (kg/s)

Greek letters §

Activity coefficient of water in the feed to the TMD 32


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ηf

Efficiency in the boiler for the fuel f

ηg

Efficiency for the generator

¦

Water recovery ration (kg permeate/kg raw feed)

Acknowledgment This paper was made possible by NPRP grant No 5-351-2-136 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s]. The author thanks Ahmed Mhd Nabil AlNouss and Fahd Mohammed for their contribution through managing historical database and for providing access to the GHG calculator. References

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Table of Contents Graphics

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Integration of Energy and Wastewater Treatment ... - ACS Publications

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