This document provides an introduction to balancing water and electricity generation between a power plant and desalination plant. It begins with background sections on power plants and desalination plants, specifically multi-stage flash distillation. The objective is to find an optimum balance between the two systems for maximum performance. Chapter 2 will model the power plant, which uses a Rankine cycle, and the desalination plant. Results from modeling different design parameters will be discussed in Chapter 3 to determine the best operating conditions and balance between the plants.
HAQ: Centre for Child Rights has partnered with Open Learning Systems in Orissa to analyze the state budget for children in Orissa. The analysis aims to understand financial commitments to children, influence public debate around child rights, and advocate for greater priority and resources for children. Budget analysis is an important tool for monitoring government spending on children's needs and holding governments accountable for realizing children's rights. While national level analysis is important, state-level analysis allows for more in-depth understanding of local issues and opportunities to respond more effectively to children's needs.
[Virtual Workshop] Holiday Strategies for PPC, Social Media Advertising, and ...Point It, Inc
Join Bing and Point it to learn how you can make the most of this Holiday 2015 buying season. Ensure that you have the most up to date digital marketing strategies by tapping the digital marketing experts. We’ll dig deep into strategies for PPC, paid social advertising, and programmatic and traditional display as it effects your holiday campaign planning. Christi Olson, our Director of PPC will help tackle tough paid search challenges. Ian Mackie, Director of Social Advertising will dig into how paid social can help boost holiday campaigns. Evan Barocas, Senior Account Manager, Display will share how programmatic display can help add leads to the funnel. Allen Klein from Bing will make sure get the best from our experts to ensure you’ll walk away with the who/what/where/when/why of holiday 2015.
In our upcoming session, you’ll get an opportunity to learn about:
- When should you get started on your digital marketing campaigns
- Who you should be targeting and when you should be targeting them
- Why a fresh holiday approach should be your #1 priority
- What the top trends for PPC, Paid Social Advertising, and display will be
В условиях экономического кризиса сегодня на рекламном рынке особенно востребован performance-подход: работа на увеличение продаж с сайта в кратчайшие сроки.
This document discusses composting and provides instructions for doing it at home. It defines composting as the accelerated decomposition of organic matter that occurs naturally but can be sped up through various methods. It encourages composting to reduce food waste and recycle nutrients, saying it is easy to start and helps save money while improving soil. Instructions are provided on essential elements like carbon, nitrogen, water, and oxygen as well as different composting methods that can be done at home.
- Статистика по региональным проектам Wizard.sape, что изменилось за последние 6 мес.
- Популярные схемы работы или какие факторы рулят (ссылки, внутренние или поведенческие)
- SEO vs Контекст. Как на противостоянии сказался "минусинск"
- Практические рекомендации по SEO в условиях новых алгоритмов
HAQ: Centre for Child Rights has partnered with Open Learning Systems in Orissa to analyze the state budget for children in Orissa. The analysis aims to understand financial commitments to children, influence public debate around child rights, and advocate for greater priority and resources for children. Budget analysis is an important tool for monitoring government spending on children's needs and holding governments accountable for realizing children's rights. While national level analysis is important, state-level analysis allows for more in-depth understanding of local issues and opportunities to respond more effectively to children's needs.
[Virtual Workshop] Holiday Strategies for PPC, Social Media Advertising, and ...Point It, Inc
Join Bing and Point it to learn how you can make the most of this Holiday 2015 buying season. Ensure that you have the most up to date digital marketing strategies by tapping the digital marketing experts. We’ll dig deep into strategies for PPC, paid social advertising, and programmatic and traditional display as it effects your holiday campaign planning. Christi Olson, our Director of PPC will help tackle tough paid search challenges. Ian Mackie, Director of Social Advertising will dig into how paid social can help boost holiday campaigns. Evan Barocas, Senior Account Manager, Display will share how programmatic display can help add leads to the funnel. Allen Klein from Bing will make sure get the best from our experts to ensure you’ll walk away with the who/what/where/when/why of holiday 2015.
In our upcoming session, you’ll get an opportunity to learn about:
- When should you get started on your digital marketing campaigns
- Who you should be targeting and when you should be targeting them
- Why a fresh holiday approach should be your #1 priority
- What the top trends for PPC, Paid Social Advertising, and display will be
В условиях экономического кризиса сегодня на рекламном рынке особенно востребован performance-подход: работа на увеличение продаж с сайта в кратчайшие сроки.
This document discusses composting and provides instructions for doing it at home. It defines composting as the accelerated decomposition of organic matter that occurs naturally but can be sped up through various methods. It encourages composting to reduce food waste and recycle nutrients, saying it is easy to start and helps save money while improving soil. Instructions are provided on essential elements like carbon, nitrogen, water, and oxygen as well as different composting methods that can be done at home.
- Статистика по региональным проектам Wizard.sape, что изменилось за последние 6 мес.
- Популярные схемы работы или какие факторы рулят (ссылки, внутренние или поведенческие)
- SEO vs Контекст. Как на противостоянии сказался "минусинск"
- Практические рекомендации по SEO в условиях новых алгоритмов
Six Sigma DMADV Approach for Conceptual Design Synthesis of Car DashboardJayesh Sarode
This document summarizes a research paper that applied Six Sigma methodology to conceptual design synthesis of a car dashboard. The paper utilized Define, Measure, Analyze, Design and Verify (DMADV) phases of Six Sigma to develop conceptual designs for a dashboard. Customer requirements were gathered and Quality Function Deployment was used to determine important design factors. Five design concepts were generated and analyzed using tradeoff analysis to select the best concept. CAD modeling of the selected design was performed to verify it met requirements. Structural analysis and head impact analysis confirmed the design's structural safety. The paper concluded the design process successfully applied Six Sigma tools to optimize the dashboard design.
IRJET- Six Sigma DMADV Approach for Conceptual Design Synthesis of Car Da...IRJET Journal
This document summarizes a research paper that applied Six Sigma methodology to conceptual design synthesis of a car dashboard. The paper utilized Define, Measure, Analyze, Design and Verify (DMADV) phases of Six Sigma to develop conceptual designs for a dashboard. Customer requirements were gathered and Quality Function Deployment was used to relate customer needs to design requirements. Five design concepts were generated and analyzed using tradeoff analysis to select the best concept. CAD modeling of the selected design was performed and the design was verified through structural analysis, material selection analysis, and head impact safety analysis to ensure it met requirements. The paper concluded that Six Sigma DMADV approach helped optimize the dashboard design to increase value through cost and weight reduction while meeting safety and
The candidate is a senior professional with over 10 years of experience in design engineering, new product development, product life cycle processes, plastic product design, Pro-E design and manufacturing, CAM programming, process and quality management, and project management. They have expertise in facilitating new product initiatives with a focus on design, planning, requirements analysis, system design, and manufacturing engineering. The candidate also has knowledge of managing design engineering operations involving various stages from design to development and coordination with other departments.
D. Giri H. Rao has over 16 years of experience in engineering design for oil and gas projects. He currently works as a lead design engineer for Avineon (I) Pvt Ltd in Hyderabad, India. Prior to this, he held design and engineering roles at Technip, PL Engineering, BecRel Engineering, and other companies. He has expertise in pressure vessels, heat exchangers, columns, and other static equipment design.
This resume summarizes the qualifications and experience of Abdul Samad, seeking a position as a structural engineer. He has over 10 years of experience in structural inspection and engineering roles. His educational background includes a diploma in mechanical engineering. He has worked on projects in Saudi Arabia and India for companies in various industries, including oil and gas, power plants, and manufacturing. His roles have included structural inspection, design, fabrication supervision, and HVAC engineering. He is proficient in AutoCAD, SAP, and Microsoft Office software.
Designing slides without using smart art,
Designing slides keeping in mind colour schemes,
Best use of space provided in an each slide,
Presenting graphs with proper labelling and description,
While designing slide keeping in mind alignment of all the figures.
causes and implication of delay in constructionsudinak
This document summarizes the M.Sc. thesis presented by Sudina Kuikel to the Nepal Engineering College on local road bridge construction delays. The thesis aims to analyze the present situation of local road bridge construction in Nepal, identify the real causes of delays, study stakeholder perceptions of delays and their effects, and assess the implications of delays. The methodology involves a literature review, analysis of project documents and interviews with clients, contractors and consultants to develop a list of common delay causes and effects. Questionnaires are used to gather stakeholder input on cause and effect rankings, and site observations are conducted for selected case studies. Preliminary findings indicate delays are a major problem, with schedules often revised multiple times. The document outlines the structure
This document provides information about a Post Graduate Diploma in Process Engineering. The course covers concepts from conceptual design of processes to fabrication and construction plans. Key topics include heat and mass transfer calculations, equipment selection and design, piping design, and safety. Site visits to industrial plants are included. Graduates can find jobs in design, manufacturing, production, and testing fields. The course aims to enhance skills of chemical and process engineers.
IRJET- Concept Validation and Design Synthesis of Car Dashboard as Per Plasti...IRJET Journal
This document summarizes the conceptual design, validation, and CAD modeling of an automobile dashboard according to plastic trim standards. Five dashboard concepts were generated and evaluated using tools like benchmarking, brainstorming, and a Pugh matrix. Concept 3 was selected based on key criteria like manufacturability, strength, aesthetics, and ergonomics. CAD modeling of Concept 3 was performed in CATIA, applying guidelines for plastic injection molding like uniform wall thickness, draft angles, and radii. The model was modified to ensure clear draft and surface tangency. The final CAD model incorporated ribs for stiffness while meeting plastic trim industry standards.
“CONCEPT VALIDATION AND DESIGN SYNTHESIS OF CAR DASHBOARD AS PER PLASTIC TRIM...Jayesh Sarode
The evolution of dashboard has led to increased
vehicle occupant comfort and convenience as new systems
become available. The project work aims to develop the work
to apply theoretical and practical tools/techniques to solve
real life problems related to industry and current research in
this project automobile (Car) dashboard upper cover is
selected for a design. For doing so conceptual design tool is
used. I am created a five different concepts using different
benchmarking & brainstorming. Select a best option using
different tools in six sigma like trade off analysis. Threedimensional
CAD software (such as CATIA) is used to develop a
CAD model same as concept. The aim is to achieve the essential
function at the lowest overall cost while maintaining
customers’ optimum value assurance. In this project I try to
develop a such dashboard design which follow a design
guidelines, And analyze and discuss the results to Obtain valid
conclusions which follows a design Standards.
This document contains a summary of Karan Mandokar's professional experience and qualifications. He has over 10 years of experience in design engineering and new product development. Some of his responsibilities have included product design, prototyping, testing, project management, and interacting with various teams. He is proficient in Pro-E modeling software and has experience designing plastic, sheet metal, and die cast products.
In this session you will learn:
Feed documents overview
PFD and P&ID
Process flow diagram
Piping and instrumentation diagram
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
BIS Frucon Engineering Inc. is a project delivery engineering firm that specializes in engineering, procurement, and consulting services for manufacturing, industrial, and institutional clients. They serve small to medium sized retrofit and revamp projects ranging from $0.01 million to $40 million. They have offices in Jacksonville, Tallahassee, Houston, and St. Louis and work with clients throughout North America and beyond.
BIS Frucon Engineering Inc. is a project delivery engineering firm that specializes in engineering, procurement, and consulting services for manufacturing, industrial, and institutional clients. They serve small to medium sized retrofit and revamp projects ranging from $0.01 million to $40 million. They have offices in Jacksonville, Tallahassee, Houston, and St. Louis and work with clients throughout North America and beyond.
This document is a resume for L. William Nie seeking a position as a Mechanical Engineer. It summarizes his work experience including positions at Sumitomo Electric Wiring Systems as a Design Engineer, RB-Tech Inc. as a Design Engineer, Perfecular Inc. as a Mechanical Designer, and internships in China. It also lists his education as an MS in Mechanical Engineering from USC and BS from Guang Dong University of Technology, China. Relevant skills include CAD, FEA, injection molding design, and languages of English and Mandarin Chinese.
Kashish Goyal completed a diploma in product design from CADD Centre Training Services in Bathinda, India in July 2015. The certificate, numbered M150690044, was issued on August 4, 2015 and certified that Kashish Goyal received training in topics including Solidworks, GD&T, AutoCAD 2D, sketching, modeling, assemblies, simulations, drawings, dimensions, and independent project work with expert assistance.
Padmanabh M. Dhobale is a senior piping engineer with over 11 years of experience in piping design and 3D modeling. He has extensive experience leading the design of piping systems for major projects in countries like Kuwait, Oman, Egypt, India, and the UAE. Some of his responsibilities include developing overall piping layouts, finalizing nozzle orientations, designing pipe rack layouts, performing stress analysis, and ensuring design compliance. Currently he works as a senior piping engineer at DODSAL Engineering & Construction Pte. Ltd. in Dubai, where he is the lead engineer for the piping system on the EWTIP project in Kuwait.
This document is a curriculum vitae for Kolaparthi Venkata Sai Pavan Kumar. It outlines his work experience as a Product Design Engineer at Voith Turbo Private Ltd. since 2011, where he is responsible for designing hydraulic couplings and accessories. It also lists his education, including an M-Tech in Engineering Design from Amrita University and a B-Tech in Mechanical Engineering. His skills include expertise in CAD software like UG NX 8.5 and experience handling major projects for clients in industries like power generation.
This document lists common engineering deliverables across various engineering disciplines including: general, process, HSE, plant layout, civil, equipment, piping design, piping installation, piping stress analysis, piping material, material & corrosion, instrumentation & control, and electrical. It provides the engineering document title for each discipline across two pages including documents such as process flow diagrams, piping isometric drawings, piping material requisitions, instrumentation loop diagrams, and electrical one line diagrams.
Six Sigma DMADV Approach for Conceptual Design Synthesis of Car DashboardJayesh Sarode
This document summarizes a research paper that applied Six Sigma methodology to conceptual design synthesis of a car dashboard. The paper utilized Define, Measure, Analyze, Design and Verify (DMADV) phases of Six Sigma to develop conceptual designs for a dashboard. Customer requirements were gathered and Quality Function Deployment was used to determine important design factors. Five design concepts were generated and analyzed using tradeoff analysis to select the best concept. CAD modeling of the selected design was performed to verify it met requirements. Structural analysis and head impact analysis confirmed the design's structural safety. The paper concluded the design process successfully applied Six Sigma tools to optimize the dashboard design.
IRJET- Six Sigma DMADV Approach for Conceptual Design Synthesis of Car Da...IRJET Journal
This document summarizes a research paper that applied Six Sigma methodology to conceptual design synthesis of a car dashboard. The paper utilized Define, Measure, Analyze, Design and Verify (DMADV) phases of Six Sigma to develop conceptual designs for a dashboard. Customer requirements were gathered and Quality Function Deployment was used to relate customer needs to design requirements. Five design concepts were generated and analyzed using tradeoff analysis to select the best concept. CAD modeling of the selected design was performed and the design was verified through structural analysis, material selection analysis, and head impact safety analysis to ensure it met requirements. The paper concluded that Six Sigma DMADV approach helped optimize the dashboard design to increase value through cost and weight reduction while meeting safety and
The candidate is a senior professional with over 10 years of experience in design engineering, new product development, product life cycle processes, plastic product design, Pro-E design and manufacturing, CAM programming, process and quality management, and project management. They have expertise in facilitating new product initiatives with a focus on design, planning, requirements analysis, system design, and manufacturing engineering. The candidate also has knowledge of managing design engineering operations involving various stages from design to development and coordination with other departments.
D. Giri H. Rao has over 16 years of experience in engineering design for oil and gas projects. He currently works as a lead design engineer for Avineon (I) Pvt Ltd in Hyderabad, India. Prior to this, he held design and engineering roles at Technip, PL Engineering, BecRel Engineering, and other companies. He has expertise in pressure vessels, heat exchangers, columns, and other static equipment design.
This resume summarizes the qualifications and experience of Abdul Samad, seeking a position as a structural engineer. He has over 10 years of experience in structural inspection and engineering roles. His educational background includes a diploma in mechanical engineering. He has worked on projects in Saudi Arabia and India for companies in various industries, including oil and gas, power plants, and manufacturing. His roles have included structural inspection, design, fabrication supervision, and HVAC engineering. He is proficient in AutoCAD, SAP, and Microsoft Office software.
Designing slides without using smart art,
Designing slides keeping in mind colour schemes,
Best use of space provided in an each slide,
Presenting graphs with proper labelling and description,
While designing slide keeping in mind alignment of all the figures.
causes and implication of delay in constructionsudinak
This document summarizes the M.Sc. thesis presented by Sudina Kuikel to the Nepal Engineering College on local road bridge construction delays. The thesis aims to analyze the present situation of local road bridge construction in Nepal, identify the real causes of delays, study stakeholder perceptions of delays and their effects, and assess the implications of delays. The methodology involves a literature review, analysis of project documents and interviews with clients, contractors and consultants to develop a list of common delay causes and effects. Questionnaires are used to gather stakeholder input on cause and effect rankings, and site observations are conducted for selected case studies. Preliminary findings indicate delays are a major problem, with schedules often revised multiple times. The document outlines the structure
This document provides information about a Post Graduate Diploma in Process Engineering. The course covers concepts from conceptual design of processes to fabrication and construction plans. Key topics include heat and mass transfer calculations, equipment selection and design, piping design, and safety. Site visits to industrial plants are included. Graduates can find jobs in design, manufacturing, production, and testing fields. The course aims to enhance skills of chemical and process engineers.
IRJET- Concept Validation and Design Synthesis of Car Dashboard as Per Plasti...IRJET Journal
This document summarizes the conceptual design, validation, and CAD modeling of an automobile dashboard according to plastic trim standards. Five dashboard concepts were generated and evaluated using tools like benchmarking, brainstorming, and a Pugh matrix. Concept 3 was selected based on key criteria like manufacturability, strength, aesthetics, and ergonomics. CAD modeling of Concept 3 was performed in CATIA, applying guidelines for plastic injection molding like uniform wall thickness, draft angles, and radii. The model was modified to ensure clear draft and surface tangency. The final CAD model incorporated ribs for stiffness while meeting plastic trim industry standards.
“CONCEPT VALIDATION AND DESIGN SYNTHESIS OF CAR DASHBOARD AS PER PLASTIC TRIM...Jayesh Sarode
The evolution of dashboard has led to increased
vehicle occupant comfort and convenience as new systems
become available. The project work aims to develop the work
to apply theoretical and practical tools/techniques to solve
real life problems related to industry and current research in
this project automobile (Car) dashboard upper cover is
selected for a design. For doing so conceptual design tool is
used. I am created a five different concepts using different
benchmarking & brainstorming. Select a best option using
different tools in six sigma like trade off analysis. Threedimensional
CAD software (such as CATIA) is used to develop a
CAD model same as concept. The aim is to achieve the essential
function at the lowest overall cost while maintaining
customers’ optimum value assurance. In this project I try to
develop a such dashboard design which follow a design
guidelines, And analyze and discuss the results to Obtain valid
conclusions which follows a design Standards.
This document contains a summary of Karan Mandokar's professional experience and qualifications. He has over 10 years of experience in design engineering and new product development. Some of his responsibilities have included product design, prototyping, testing, project management, and interacting with various teams. He is proficient in Pro-E modeling software and has experience designing plastic, sheet metal, and die cast products.
In this session you will learn:
Feed documents overview
PFD and P&ID
Process flow diagram
Piping and instrumentation diagram
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
BIS Frucon Engineering Inc. is a project delivery engineering firm that specializes in engineering, procurement, and consulting services for manufacturing, industrial, and institutional clients. They serve small to medium sized retrofit and revamp projects ranging from $0.01 million to $40 million. They have offices in Jacksonville, Tallahassee, Houston, and St. Louis and work with clients throughout North America and beyond.
BIS Frucon Engineering Inc. is a project delivery engineering firm that specializes in engineering, procurement, and consulting services for manufacturing, industrial, and institutional clients. They serve small to medium sized retrofit and revamp projects ranging from $0.01 million to $40 million. They have offices in Jacksonville, Tallahassee, Houston, and St. Louis and work with clients throughout North America and beyond.
This document is a resume for L. William Nie seeking a position as a Mechanical Engineer. It summarizes his work experience including positions at Sumitomo Electric Wiring Systems as a Design Engineer, RB-Tech Inc. as a Design Engineer, Perfecular Inc. as a Mechanical Designer, and internships in China. It also lists his education as an MS in Mechanical Engineering from USC and BS from Guang Dong University of Technology, China. Relevant skills include CAD, FEA, injection molding design, and languages of English and Mandarin Chinese.
Kashish Goyal completed a diploma in product design from CADD Centre Training Services in Bathinda, India in July 2015. The certificate, numbered M150690044, was issued on August 4, 2015 and certified that Kashish Goyal received training in topics including Solidworks, GD&T, AutoCAD 2D, sketching, modeling, assemblies, simulations, drawings, dimensions, and independent project work with expert assistance.
Padmanabh M. Dhobale is a senior piping engineer with over 11 years of experience in piping design and 3D modeling. He has extensive experience leading the design of piping systems for major projects in countries like Kuwait, Oman, Egypt, India, and the UAE. Some of his responsibilities include developing overall piping layouts, finalizing nozzle orientations, designing pipe rack layouts, performing stress analysis, and ensuring design compliance. Currently he works as a senior piping engineer at DODSAL Engineering & Construction Pte. Ltd. in Dubai, where he is the lead engineer for the piping system on the EWTIP project in Kuwait.
This document is a curriculum vitae for Kolaparthi Venkata Sai Pavan Kumar. It outlines his work experience as a Product Design Engineer at Voith Turbo Private Ltd. since 2011, where he is responsible for designing hydraulic couplings and accessories. It also lists his education, including an M-Tech in Engineering Design from Amrita University and a B-Tech in Mechanical Engineering. His skills include expertise in CAD software like UG NX 8.5 and experience handling major projects for clients in industries like power generation.
This document lists common engineering deliverables across various engineering disciplines including: general, process, HSE, plant layout, civil, equipment, piping design, piping installation, piping stress analysis, piping material, material & corrosion, instrumentation & control, and electrical. It provides the engineering document title for each discipline across two pages including documents such as process flow diagrams, piping isometric drawings, piping material requisitions, instrumentation loop diagrams, and electrical one line diagrams.
1. KING FAHD UNIVERSITY OF
PETROLEUM & MINERALS
Mechanical Engineering
Department
(152)
Senior Design Project
Design Project
ME 412/416
Balancing of water VS Electricity generation (Rankine-MSF)
Name: Alkathiri, Ali Ahmed ID# 201158730
Name: Julaidan, Mohammed ID# 201168370
Name: Alhujaili, Amjad ID# 201050560
Name: ID#
Name: ID#
Advisor Name:
MOHAMMED A. ANTAR
Coordinator Name:
26/04/2016
2. ii
EVALUATION SHEET
Editorial Structural
Criteria Evaluation Criteria Evaluation Criteria Evaluation Criteria Evaluation
Cover page*
Introduction
Overview
Final Design
Overall description
Conclusion &
Recommendation
Conclusion*
Title*
Problem
definition*
Detailed design description Recommendation*
Abstract* Objectives* Analysis & results*
Appendices
Decision matrix
Table of
contents*
Project
management*
Material selection Gantt chart*
List of figures
Background
Existing product Cost analysis
Final drawing
List of table Market research Drawings
List of vendors, contact
information and pricing
Heading Technical data*
Product
Realization
Manufacturing processes
Specification for
supplied materials
Language Patent search
Prototype verses planned
design
Detailed supporting
analysis
Figure/table
Design &
Development
Conceptual
design
Manufacturing processes
Captions
List of
constraints* Future manufacturing
recommendation
Final Report Score
List of standards*
Figure/table
citation Concept selection
Design
verification
Test description
References
Preliminary
analysis*
Detailed results
Proof of concept Specification verification list
Items in (red) with asterisks (*) are mandatory.
3. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
1
Table of Contents
List of tables: ..................................................................................................................... 2
List of figures: .................................................................................................................... 2
Chapter 1: ......................................................................................................................... 3
Introduction: ..................................................................................................................... 3
1.1 Power Plant: .................................................................................................................... 3
Classification of power station: ................................................................................................... 3
1.1.1 By heat source: [1] .......................................................................................................... 3
1.1.2 By prime mover: ............................................................................................................. 3
1.1.3 By duty (scheduled): ....................................................................................................... 4
1.2 Desalination Plant: .......................................................................................................... 5
1.2.1 Types of distillation process: .......................................................................................... 5
1.2.2 How it works: .................................................................................................................. 6
1.2.3 Challenges: ..................................................................................................................... 7
1.3 Objective: ........................................................................................................................ 7
Chapter 2: ......................................................................................................................... 8
2.1 Power Plant: .................................................................................................................... 8
2.1.1 Boiler: ........................................................................................................................... 10
2.1.2 High Pressure Turbine: ................................................................................................. 10
2.1.3 Low Pressure Turbine: .................................................................................................. 11
2.1.4 Condenser: ................................................................................................................... 12
2.1.5 Open Feed Water Heater: ............................................................................................ 12
2.1.6 Closed Feed Water Heater 2: ....................................................................................... 13
2.1.7 Closed Feed Water Heater 1: ....................................................................................... 14
2.2 Desalination Plant: ........................................................................................................ 15
2.1.1 Mass balance modeling: ............................................................................................... 17
2.1.2 Temperature Drop Modeling: ...................................................................................... 18
2.1.3 The Temperature at each Stage: .................................................................................. 18
2.1.4 Heat transfer areas: ...................................................................................................... 18
2.1.5 Flashing Stage Dimensions Modeling: .......................................................................... 20
2.1.6 Performance Modeling: ................................................................................................ 20
Chapter 3: ....................................................................................................................... 21
Results and discussion: .................................................................................................... 21
3.1 Power plant ................................................................................................................... 21
3.1.1 The optimum pressure: ................................................................................................ 21
3.1.2 The effect of mass extraction from power plant to MSF plant .................................... 22
3.1.3 The effect of the condition of the extraction mass ...................................................... 24
3.1.4 Make up water for the power plant: ............................................................................ 25
3.1.5 Comparison between data: .......................................................................................... 26
3.2 MSF Plant ...................................................................................................................... 29
3.2.1 The effect of condition of the steam ............................................................................ 29
3.2.2 Extraction “b” from power plant to the MSF ............................................................... 32
6. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
4
electric power produced in the world is through use of steam
turbines.
o Gas turbine plants use the dynamic pressure from flowing gases
(air and combustion products) to directly operate the turbine.
Natural-gas fuelled (and oil fueled) combustion turbine plants
can start rapidly and so are used to supply "peak" energy during
periods of high demand, though at higher cost than base-loaded
plants. These may be comparatively small units, and sometimes
completely unmanned, being remotely operated. This type was
pioneered by the UK, Princetown being the world's first,
commissioned in 1959.
o Combined cycle plants have both a gas turbine fired by natural
gas, and a steam boiler and steam turbine which use the hot
exhaust gas from the gas turbine to produce electricity. This
greatly increases the overall efficiency of the plant, and many
new baseload power plants are combined cycle plants fired by
natural gas.
o Internal combustion reciprocating engines are used to provide
power for isolated communities and are frequently used for
small cogeneration plants. Hospitals, office buildings, industrial
plants, and other critical facilities also use them to provide
backup power in case of a power outage. These are usually
fuelled by diesel oil, heavy oil, natural gas, and landfill gas.
o Microturbines, Stirling engine and internal combustion
reciprocating engines are low-cost solutions for using
opportunity fuels, such as landfill gas, digester gas from water
treatment plants and waste gas from oil production.
1.1.3 By duty (scheduled):
o Base load power plants run nearly continually to provide that
component of system load that doesn't vary during a day or
week. Baseload plants can be highly optimized for low fuel
cost, but may not start or stop quickly during changes in system
load. Examples of base-load plants would include large modern
7. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
5
coal-fired and nuclear generating stations, or hydro plants with
a predictable supply of water.
o Peaking power plants meet the daily peak load, which may only
be for one or two hours each day. While their incremental
operating cost is always higher than base load plants, they are
required to ensure security of the system during load peaks.
Peaking plants include simple cycle gas turbines and sometimes
reciprocating internal combustion engines, which can be started
up rapidly when system peaks are predicted. Hydroelectric
plants may also be designed for peaking use.
o Load following power plants can economically follow the
variations in the daily and weekly load, at lower cost than
peaking plants and with more flexibility than baseload plants.
1.2 Desalination Plant:
Desalination is a process that removes minerals from saline water.
More generally, desalination may also refer to the removal of salts and
minerals, as in soil desalination, which also happens to be a major issue for
agricultural production.
1.2.1 Types of distillation process:
a. Multi-Stage flash distillation.
b. Multiple-effect distillation.
c. Vapor-Compression.
8. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
6
1.2.1 Multi-Stage flash distillation [1]:
Multi-stage flash distillation is a water distillation process that distills
seawater by flashing a portion of the water into steam in multiple stages of
what are essentially countercurrent heat exchangers.
1.2.2 How it works:
The plant has a series of spaces called stages, each containing a heat
exchanger and a condensate collector. The sequence has a cold end and a hot
end while intermediate stages have intermediate temperatures. The stages
have different pressures corresponding to the boiling points of water at the
stage temperatures. After the hot end there is a container called the brine
heater.
Figure 1: Schematic of a 'once-through' multi-stage flash desalinates:
A - Steam in
B - Seawater in
C - Potable water out
D - Waste out
E - Steam out
F - Heat exchange
G - Condensation collection
H - Brine heater
9. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
7
1. The cold water pumped from the sea through a heat exchanger and it warms it up.
(with each stage, the temperature of sea water increase).
2. Then, when it reaches the brine heater, it already had got nearly the maximum
temperature.
3. The water enters the brine heater and some heat is added.
4. After the heater, the water flows through valves back into the stages that have
ever lower pressure and temperature. The water now called brine.
5. The brine enters each stage at temperature higher than the boiling temperature.
As a result, small fraction of brine flashes to steam until its temperature reduce to
equilibrium. Then enters the next stage.
6. The steam cools and condense against the heat exchanger tube, and it heats up
the water coming from the sea.
7. At the final stage, the temperature in nearly same as the inlet temperature.
1.2.3 Challenges:
There is a maximum temperature of brine heater that the water can’t
be heated above 120, because this will result in corrode the heat exchanger
as well as scale formation, which is the salt from sea. It can be avoided by
adding Nano filters, so the water is out of Mg and Na.
Also, another challenge is about how to balance the heat used in brine
heater, that it gives the best performance with low energy lost.
1.3 Objective:
Our objective is to find a balance between the power plant and the desalination
plant so that we get the optimum performance for both systems.
13. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
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here is the mass flow rate fraction balance:
1 = 6 + 8 + / (3)
where:
§ h[12] represents the enthalpy of X-fraction of steam from the
high pressure turbine at P[12] and T[12].
§ h[13] represents the enthalpy of Y-fraction of steam from the
high pressure turbine at P[13] and T[13].
§ h[14] represents the enthalpy of Z-fraction of steam from the
high pressure turbine at P[14] and T[14].
2.1.3 Low Pressure Turbine:
In the low pressure turbine, we have three fractions being
extracted at different pressure and the first one is going to the open
feed water heater, and the other one is going to the MSF-OT plant,
and the third one is going to the condenser. The low pressure turbine
has isentropic efficiency of 85% and here is the energy balance
equation for the low pressure turbine:
2:45 = &'() /×ℎ 15 − &×ℎ 16 − =×ℎ 17 − ?× ℎ@A"#B[18]
(4)
here is the mass flow rate fraction balance:
/ = & + = + ? (5)
where:
§ h[15] represents the enthalpy of Z-fraction of steam from the
reheat at P[15] and T[15] to the low pressure turbine.
§ h[16] represent the enthalpy of m-fraction of steam from the
low pressure turbine at P[16] and T[16] and going to the open
feed water heater.
§ h[17] represents the enthalpy of n-fraction of steam from the
low pressure turbine at Pcondenser and T[17] going to the
condenser.
§ hbrine[18] represents the enthalpy of b-fraction of steam from
the reheat at Pbrine[18] and Tbrine[18] and going to the MSF.
14. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
12
§ m represents the fraction of steam that is going to the open feed
water heater.
§ n represents the fraction of steam that is going to the condenser.
§ b represents the fraction of steam that is going to the MSF brine
heater.
2.1.4 Condenser:
In the condenser, the water enters the condenser at Pcondenser
and the water gets out with zero quality. Here is the energy balance
equation for the condenser:
!(F) = &'() = ℎ 17 − ℎ 1 (7)
where:
§ h[1] represents the enthalpy of n-fraction of water from the
condenser at Pcondenser and T[1] to pump 1.
2.1.5 Open Feed Water Heater:
In the open feed water heater, n-fraction after pump 1, m-
fraction from low pressure turbine, b-fraction from MSF. Here is the
energy balance equation for the open feed water heater:
ℎ 2 × = + ℎ 16 × & + ℎ 6 × 6 + 8 + ?×ℎ 20 = ℎ 3 (8)
here is the mass flow rate fraction balance equation:
? + 6 + 8 + & = 1 (9)
where:
§ h[2] represents the enthalpy of n-fraction of water from the
condenser at P[2] and T[2] and going to the open feed water
heater.
§ h[6] represents the enthalpy of (x&y)-fraction of steam from
trap 2 at P[6] and T[6] and going to the open feed water
heater.
§ h[20] represents the enthalpy of b-fraction of steam from MSF
at Pbrine and Tbrine[18] and going to the open feed water heater.
17. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
15
2.2 Desalination Plant:
We chose to work on a once through multi-stage flash distillation
system consists of two basic sections, a heat addition section and a heat
recovery section as can be seen in the Fig.4, in the next page. The heat
recovery section consists of a condenser, the distillate collection trays
and the flashing chamber. On the other hand, the heat addition section
consists mainly of a brine heater. (See Appendix B for the EES code)
23. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
21
Chapter 3:
Results and discussion:
3.1 Power plant
3.1.1 The optimum pressure:
The optimum pressure in the power plant in boiler, reheat and extraction
line from turbines to feed water. All following pressure calculate at extraction
equal 0.
3.1.1.1 Boiler pressure
The pressure of the boiler is the maximum pressure in the power plant. The
pressure set it to be 12MPa and all following pressure will depends on this
pressure. We use 12 MPa. As we increase the pressure of the boiler, the
efficiency of the cycle will increase but this increase should have some
constrain.
3.1.1.2 Reheat pressure
The best condition of the reheat pressure is to be 20-25 % of the boiler
pressure, which is equal to 2400 KPa. We can see that from the following
graph and table.
Table 1: Reheat pressure
Wnet
(KW)
Efficiency
(%)
Pressure
(KPa)
71119236.197000
71739036.296500
72376936.386000
73034636.475500
73714536.555000
74419236.634500
75151436.74000
75914636.763500
76712236.83000
77547736.822500
77719536.822400
78422236.82000
Figure 5: preheat/Boiler Vs eff
24. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
22
3.1.1.3 Extraction pressure p [12]
The extraction pressure from the HPT to CFWH1. We can see P =
2500 KPa is the best condition. This pressure cannot be lower than the
pressure of reheat.
3.1.2 The effect of mass extraction from power plant to MSF plant
Here in this part, we want to see the effect of the amount of mass that
extract form the power plant to the MSF plant. As we can see from the figure
and the table, the effect of the extraction is not going to effect the power plant.
If we say that we need to extract 10 % of the mass flow rate of the power plant,
we will loss 0.43% from the efficiency and if we compare this loss to how much
are we going to produce water, that loss will be nothing. The value of extraction
represent how much do we take from the power plant to the MSF. The following
table shows extraction value starts from 0% to 34% of the mass flow rate of the
power plant. The following data are at 700 KPa and we will discuss latter why
we choose this pressure for the extraction.
Wnet
(KW)
Efficiency
(%)
Pressure
(KPa)
59678031.715000
63562733.044500
67334834.214000
71041835.253500
74734736.173000
78474936.972500
Table 2: optimum pressure for P [12]
25. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
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Table 3: Mass extraction and effect on efficiency
Wnet
(KW)
Efficiency
(%)
Extraction
fraction
77719536.820
77629836.780.01
77540036.730.02
77450236.690.03
77360536.650.04
77270736.610.05
77181036.560.06
77091236.520.07
77001436.480.08
76911736.440.09
76821936.390.1
76373136.180.15
75924335.970.2
75475535.760.25
75026735.540.3
74667635.370.34
Figure 6: Effincy Vs Extraction fraction
26. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
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3.1.3 The effect of the condition of the extraction mass
Here we are going to see, how the condition of the extraction can effect
on the power plant system. The following result is when we extract 10 % of
the mass flow rate of the power plant. As we can see, decreasing the pressure
will lead to decrease the value of hg . However, hfg will increase because as we
decrease the pressure hfg increase from the
T-S diagram. From here we can calculate the
amount of heat that will be delivered to the
MSF plant which equal to = m*hfg.. From
here, we can control the condition of the
pressure of extraction based on the needed
heat in the MSF plant.
Table4 : effect of the condition of the extraction
Q Desalination
(KJ)
Wnet
(KW)
Efficiency
(%)
hg
(KJ/Kg)
Pressure
(KPa)
15883476857736.4127841200
15997176847536.4127811100
16116976838536.427781000
16243976831036.42774900
16379676825336.42769800
16526176821936.392763700
16686376821636.392757600
16864376825536.42749500
17067476835536.42739400
17307976855136.412725300
17612476891936.432707200
18059876969836.462675100
Figure 8: Efficiency Vs Pbrine
Figure 7: : T-S diagram shows how hfg increase
as pressure decreases
27. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
25
3.1.4 Make up water for the power plant:
The losses in the mass flow rate from the power plant during the extraction
to the MSF plant should be compensating. Usually the makeup water that for the
power plant is water at 25 Co
. in this project we tried to see how we could use the
same water that used in the MSF plant. Water that come back again will have less
energy than that delivered to MSF because the energy used to heat up the see
water temperature.
1- Using the water from the MSF:
Steam that used in MSF plant will have hg at the extraction pressure. The
outlet of the brine heater will have hf at the extraction pressure. In this case, we
can use this water and pump it back to the open feed eater at the same pressure of
the OFWH. This water will be the makeup water for the power plant. We can see
from the table 5, how the efficiency will be if we return the water from MSF plant.
Table 5: Efficiency with returning water from MSF
Wnet
(KW)
Efficiency
(%)
b
77719536.820
77629836.780.01
77540036.730.02
77450236.690.03
77360536.650.04
77270736.610.05
77181036.560.06
77091236.520.07
77001436.480.08
76911736.440.09
28. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
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2- Using water at 25Co
As we extract steam from power plant we should have make up water to
complete the cycle. In this case we will have makeup water in 25 C. we will pump
this water to same pressure of OFWH. The flowing data are at 700 KPa for the
make water and its efficiency.
Table 6: Efficiency with returning water 25 C
Wnet
(KW)
Efficiency
(%)
b
77719536.820
77557136.740.01
77394636.650.02
77232136.570.03
77069736.490.04
76907236.410.05
76744736.320.06
76582336.240.07
76419836.160.08
76257336.070.09
3.1.5 Comparison between data:
This table will show how much is the difference in efficiency and Wnet between make
up water from MSF or at 25 C
Defiance
(KW)
Wnet
(KW)
Wnet
(KW)
Defiance
(%)
Efficiency
(%)
Efficiency
(%)
b
0777195777195036.8236.820
7277755717762980.0436.7436.780.01
14547739467754000.0836.6536.730.02
21817723217745020.1236.5736.690.03
29087706977736050.1636.4936.650.04
36357690727727070.236.4136.610.05
43637674477718100.2436.3236.560.06
50897658237709120.2836.2436.520.07
58167641987700140.3236.1636.480.08
65447625737691170.3736.0736.440.09
31. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
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3.2 MSF Plant
In MSF plant, we are going to see how much steam do we need from the
power plant to produce 378.8 Kg/s of desalt water. Also, how can the temperature of
the steam can affect the performance ration of the MSF.
3.2.1 The effect of condition of the steam
As we see previously how the extraction condition can effect on the power plant.
It is also effect on MSF. As we increase the pressure of extraction, the temperature of
the steam will increase. Therefore, the temperature of top brine will increase also. We
can see how temperature will be difference with pressure in the following table.
Table 7: pressure and its temperatures
TsatPextraction
1881200
184.11100
179.91000
175.4900
170.4800
165700
158.9600
151.9500
143.6400
133.6300
120.2200
99.63100
Increasing the pressure of extraction will led to increase in performance ratio and
increasing in number of stages. In addition, it will led to decrease the amount of steam
that need in MSF plant. The following table shows how temperature will effect in the
energy needed (Q) and number of stages.
Table 8: steam temperature and its effect on Q and number of stages
MsteamQ (KJ)PR∆T (Co
)nTn (Co
)To (Co
)
95.70492118373.9582.752440106
90.839332000254.172.82540110
80.819291756904.6872.7586212940120
73.099191567515.1822.81253240130
66.996821416115.6542.7777783640140
62.067841292236.1032.754040150
58.026961188926.5282.7906984340160
54.692461101556.9262.7659574740170
32. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
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Tn: Brine Temperature in last stage
To: Top brine temperature
n: Total number of stages
∆T: The temperature drop per stage
PR: performance ratio
Q: heat need in brine heater
In figure 11, we can see the relation between the top brine temperature, which is (Tsat – 10),
and the number of stages. As we increases number of stages the performance ratio will
increases.
Figure 11: number of stage Vs Top brine temperature
y = 0.3613x - 14.543
20
25
30
35
40
45
50
100 110 120 130 140 150 160 170
34. ME 412 | Balancing of water VS Electricity generation (Rankine-MSF)
32
3.2.2 Extraction “b” from power plant to the MSF
b = ms / mp
ms: mass of steam required in MSF
mp: mass flow rate of water in power plant, which is 800 Kg/s
The following table show how much “b” we need to satisfy the MSF at different top
brine temperature.
Table 9: table show how much “b” we need to satisfy the MSF at different top brine temperature
bmsteam
(Kg/s)
Q
(KJ)
To
(Co
)
0.11963195.7049211837106
0.11354990.83933200025110
0.10102480.81929175690120
0.09137473.09919156751130
0.08374666.99682141611140
0.07758562.06784129223150
0.07253458.02696118892160
0.06836654.69246110155170
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Knowen information
Pboiler = 12000
P12 = 2600
P13 = 2500
Preheat = 2400
P16 = 900
Pcond = 7.5
T11 = 565
T15 = 550
HPT = 0.8
LPT = 0.85
b = 0.07254
Pbrine,18 = 700
rat =
Preheat
Pboiler
Qdesel = b · M · hbrinefg,18
M = 800
CFWH1 = CFWH2
CFWH2 = 0.4
massdes = M · b
Heat Transfer Coefficient of steam at Steam Temperature
hbrinefg,18 = Enthalpyvaporization SteamIAPWS , P = Pbrine,18
HPT
h11 = h Steam , T = T11 , P = Pboiler
s11 = s Steam , T = T11 , P = Pboiler
Isontropic turbine
s11 = s12
s11 = s13
s11 = s14
hiso,12 = h Steam , s = s12 , P = P12
hiso,13 = h Steam , s = s13 , P = P13
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hiso,14 = h Steam , s = s14 , P = Preheat
HPT =
h11 – h12
h11 – hiso,12
HPT =
h11 – h13
h11 – hiso,13
HPT =
h11 – h14
h11 – hiso,14
T12 = T Steam , s = s12 , P = P12
T13 = T Steam , s = s13 , P = P13
T14 = T Steam , s = s14 , P = Preheat
1
Mass balance
1 = z + x + y
WHPT = M · x · h11 – h12 + y · h11 – h13 + z · h11 – h14
LPT
h15 = h Steam , T = T15 , P = Preheat
s15 = s Steam , T = T15 , P = Preheat
Isontropic turbine
s15 = s16
s15 = s17
hiso,16 = h Steam , s = s16 , P = P16
hiso,17 = h Steam , s = s17 , P = Pcond
LPT =
h15 – h16
h15 – hiso,16
LPT =
h15 – h17
h15 – hiso,17
T16 = T Steam , s = s16 , P = P16
T17 = T Steam , s = s17 , P = Pcond
2
Mass Balance
z = m + n + b
hbrine,18 = h Steam , x = 1 , P = Pbrine,18
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EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
WLPT = M · z · h15 – m · h16 – n · h17 – b · hbrine,18
Condencer
h1 = h water , P = Pcond , x = 0
To calculate the work in pump 1
v1 = v water , P = Pcond , x = 0
Qout = M · n · h17 – h1
Pump 1
P2 = P16
P1 = Pcond
Wp1 = M · n · v1 · P2 – P1
M · n · h2 – h1 = Wp1
OFWH
3
h2 · n + h16 · m + h6 · x + y + b · h20 = h3
Hrecovery = M · n · h2 + h16 · m + h6 · x + y + b · h20
hnew,3 =
Hrecovery
M
h21 = h water , T = 25 , P = 101
h3 = h water , P = P16 , x = 0
To calculate the work in pump 2
v3 = v water , P = P16 , x = 0
T3 = T water , P = P16 , h = hnew,3
T2 = T water , P = P16 , h = h2
T6 = T water , P = P16 , h = h6
Pump 2
P4 = Pboiler
P3 = P16
Wp2 = M · v3 · P4 – P3
M · h4 – hnew,3 = Wp2
T4 = T3
pump 3
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EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
P16 = P20
v19 = v water , P = Pbrine,18 , x = 0
WP3 = M · b · P20 – Pbrine,18 · v19
WP3 = M · b · h20 – h19
h19 = h water , P = Pbrine,18 , x = 0
CFWH 2
4
y · Cp13 · T13 – Tsat,13 + y · hfg,13 + h4 + h9 · x = x + y · h5 + h7
Tsat,13 = Tsat water , P = P13
Tavg,13 =
T13 + Tsat,13
2
Cp13 = Cp water , T = Tavg,13 , P = P13
hfg,13 = Enthalpyvaporization Steam , P = P13
T7 – T4
T13 – T4
= CFWH2
T5 = T water , P = P13 , x = 0
h7 = h water , T = T7 , P = P4
Trap 2
h5 = h6
h5 = h water , P = P13 , x = 0
CFWH 1
5
x · Cp12 · T12 – Tsat,12 + x · hfg,12 + h7 = h8 · x + h10
Tsat,12 = Tsat water , P = P12
Tavg,12 =
T12 + Tsat,12
2
Cp12 = Cp water , T = Tavg,12 , P = P12
hfg,12 = Enthalpyvaporization Steam , P = P12
h8 = h water , P = P12 , x = 0
T10 – T7
T12 – T7
= CFWH1
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EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
T8 = T water , P = P12 , x = 0
h10 = h water , T = T10 , P = P4
Trap 1
h8 = h9
Boiler
Qin = M · h11 – h10 + z · h15 – h14
The System effecince
Wtotal,pump = Wp1 + Wp2 + WP3
Wnet = WLPT + WHPT – Wp1 – Wp2 – WP3
sys,new =
Wnet
Qin
· 100
HC,2 = M · n · h2
HC,4 = M · h4
HC,5 = M · x + y · h5
HC,6 = M · x + y · h6
HC,7 = M · h7
HC,8 = M · x · h8
HC,9 = M · x · h9
HC,10 = M · h10
HC,11 = M · h11
HC,12 = M · x · h12
HC,13 = M · y · h13
HC,14 = M · z · h14
HC,15 = M · z · h15
HC,16 = M · m · h16
HC,17 = M · n · h17
HC,18 = M · b · hbrine,18
SOLUTION
Unit Settings: SI C kPa kJ mass deg
b = 0.07254 CFWH1 = 0.4
CFWH2 = 0.4 HPT = 0.8
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Arrays Table: Main
Ti Tavg,i Tsat,i vi hbrinefg,i
16 389.9
17 40.3
18 2066
19 0.001108
20
21
There are a total of 114 equations in the Main program.
Block Rel. Res. Abs. Res. Units Calls Time(ms) Equations
0 0.000E+00 0.000E+00 OK 1 0 P_boiler=12000
0 0.000E+00 0.000E+00 OK 1 0 P[12]=2600
0 0.000E+00 0.000E+00 OK 1 0 P[13]=2500
0 0.000E+00 0.000E+00 OK 1 0 P_reheat=2400
0 0.000E+00 0.000E+00 OK 1 0 P[16]=900
0 0.000E+00 0.000E+00 OK 1 0 P_cond=7.5
0 0.000E+00 0.000E+00 OK 1 0 T[11]=565
0 0.000E+00 0.000E+00 OK 1 0 T[15]=550
0 0.000E+00 0.000E+00 OK 1 0 Eta_HPT=0.8
0 0.000E+00 0.000E+00 OK 1 0 Eta_LPT=0.85
0 0.000E+00 0.000E+00 OK 1 0 b=.07254
0 0.000E+00 0.000E+00 OK 1 0 P_brine[18]=700
0 0.000E+00 0.000E+00 OK 1 0 M_dot=800
0 0.000E+00 0.000E+00 OK 1 0 epsilon_CFWH2=0.4
0 0.000E+00 0.000E+00 OK 4 0 rat=P_reheat/P_boiler
0 0.000E+00 0.000E+00 OK 4 0 mass_des=M_dot*b
0 0.000E+00 0.000E+00 ? 4 0 h_brinefg[18]=Enthalpy_vaporization(Steam_IAPWS,P=P_brine[18])
0 0.000E+00 0.000E+00 ? 4 0 h[11]=Enthalpy(Steam,T=T[11],P=P_boiler)
0 0.000E+00 0.000E+00 ? 4 0 s[11]=Entropy(Steam,T=T[11],P=P_boiler)
0 0.000E+00 0.000E+00 OK 4 0 s[11]=s[12]
0 0.000E+00 0.000E+00 OK 4 0 s[11]=s[13]
0 0.000E+00 0.000E+00 OK 4 0 s[11]=s[14]
0 0.000E+00 0.000E+00 ? 4 0 h_iso[12]=Enthalpy(Steam,S=S[12],P=P[12])
0 0.000E+00 0.000E+00 ? 4 0 h_iso[13]=Enthalpy(Steam,S=S[13],P=P[13])
0 0.000E+00 0.000E+00 ? 4 0 h_iso[14]=Enthalpy(Steam,S=S[14],P=P_reheat)
0 1.355E-19 -5.073E-17 OK 4 0 Eta_HPT=(h[11]-h[12])/(h[11]-h_iso[12])
0 1.355E-19 5.182E-17 OK 4 0 Eta_HPT=(h[11]-h[13])/(h[11]-h_iso[13])
0 2.033E-19 -7.942E-17 OK 4 0 Eta_HPT=(h[11]-h[14])/(h[11]-h_iso[14])
0 0.000E+00 0.000E+00 ? 4 0 T[12]=Temperature(Steam,S=S[12],P=P[12])
0 0.000E+00 0.000E+00 ? 4 0 T[13]=Temperature(Steam,S=S[13],P=P[13])
0 0.000E+00 0.000E+00 ? 4 0 T[14]=Temperature(Steam,S=S[14],P=P_reheat)
0 0.000E+00 0.000E+00 ? 4 0 h[15]=Enthalpy(Steam,T=T[15],P=P_reheat)
0 0.000E+00 0.000E+00 ? 4 0 s[15]=Entropy(Steam,T=T[15],P=P_reheat)
0 0.000E+00 0.000E+00 OK 4 0 s[15]=s[16]
0 0.000E+00 0.000E+00 OK 4 0 s[15]=s[17]
0 0.000E+00 0.000E+00 ? 4 0 h_iso[16]=Enthalpy(Steam,S=s[16],P=P[16])
0 0.000E+00 0.000E+00 ? 4 0 h_iso[17]=Enthalpy(Steam,S=s[17],P=P_cond)
0 1.276E-19 3.587E-17 OK 4 0 Eta_LPT=(h[15]-h[16])/(h[15]-h_iso[16])
0 1.276E-19 1.346E-16 OK 4 0 Eta_LPT=(h[15]-h[17])/(h[15]-h_iso[17])
0 0.000E+00 0.000E+00 ? 4 0 T[16]=Temperature(Steam,S=s[16],P=P[16])
0 0.000E+00 0.000E+00 ? 4 0 T[17]=Temperature(Steam,S=s[17],P=P_cond)
0 0.000E+00 0.000E+00 ? 4 0 h_brine[18]=Enthalpy(Steam,x=1,P=P_brine[18])
0 0.000E+00 0.000E+00 ? 4 0 h[1]=Enthalpy(Water,P=P_cond,x=0)
0 0.000E+00 0.000E+00 ? 4 0 v[1]=Volume(Water,P=P_cond,x=0)
0 0.000E+00 0.000E+00 OK 4 0 P[2]=P[16]
0 0.000E+00 0.000E+00 OK 4 0 P[1]=P_cond
0 0.000E+00 0.000E+00 ? 4 0 h[21]=Enthalpy(Water,T=25,P=101)
46. File:powerplant6 with optimum presuure.EES 4/25/2016 9:34:26 PM Page 8
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
0 0.000E+00 0.000E+00 ? 4 0 h[3]=Enthalpy(Water,P=P[16],x=0)
0 0.000E+00 0.000E+00 ? 4 0 v[3]=Volume(Water,P=P[16],x=0)
0 0.000E+00 0.000E+00 OK 4 0 P[4]=P_boiler
0 0.000E+00 0.000E+00 OK 4 0 P[3]=P[16]
0 0.000E+00 0.000E+00 OK 4 0 W_p2=m_dot*v[3]*(P[4]-P[3])
0 0.000E+00 0.000E+00 OK 4 0 P[16]=P[20]
0 0.000E+00 0.000E+00 ? 4 0 v[19]=Volume(Water,P=P_brine[18],x=0)
0 0.000E+00 0.000E+00 OK 4 0 W_P3=m_dot*b*(P[20]-P_brine[18])*v[19]
0 0.000E+00 0.000E+00 ? 4 0 h[19]=Enthalpy(Water,P=P_brine[18],x=0)
0 0.000E+00 0.000E+00 ? 4 0 T_sat[13]=T_sat(Water,P=P[13])
0 0.000E+00 0.000E+00 OK 4 0 T_avg[13]=((T[13]+T_sat[13])/2)
0 0.000E+00 0.000E+00 ? 4 0 Cp[13]=Cp(Water,T=T_avg[13],P=P[13])
0 0.000E+00 0.000E+00 ? 4 15 h_fg[13]=Enthalpy_vaporization(Steam,P=P[13])
0 0.000E+00 0.000E+00 ? 4 0 T[5]=Temperature(Water,P=P[13],x=0)
0 0.000E+00 0.000E+00 ? 4 0 h[5]=Enthalpy(Water,P=P[13],x=0)
0 0.000E+00 0.000E+00 ? 4 0 T_sat[12]=T_sat(Water,P=P[12])
0 0.000E+00 0.000E+00 OK 4 0 T_avg[12]=((T[12]+T_sat[12])/2)
0 0.000E+00 0.000E+00 ? 4 0 Cp[12]=Cp(Water,T=T_avg[12],P=P[12])
0 0.000E+00 0.000E+00 ? 4 16 h_fg[12]=Enthalpy_vaporization(Steam,P=P[12])
0 0.000E+00 0.000E+00 ? 4 0 h[8]=Enthalpy(Water,P=P[12],x=0)
0 0.000E+00 0.000E+00 ? 4 0 T[8]=Temperature(Water,P=P[12],x=0)
0 0.000E+00 0.000E+00 OK 4 0 h[8]=h[9]
0 0.000E+00 0.000E+00 OK 4 0 H_C[11]=m_dot*h[11]
0 0.000E+00 0.000E+00 OK 4 0 H_C[18]=m_dot*(b)*h_brine[18]
0 0.000E+00 0.000E+00 OK 4 0 Q_desel=b*M_dot*h_brinefg[18]
0 0.000E+00 0.000E+00 OK 4 0 epsilon_CFWH1=epsilon_CFWH2
0 1.087E-16 -1.398E-15 OK 4 0 W_P3=m_dot*b*(h[20]-h[19])
0 0.000E+00 0.000E+00 OK 4 0 h[5]=h[6]
0 0.000E+00 0.000E+00 ? 4 0 T[6]=Temperature(Water,P=P[16],h=h[6])
1 0.000E+00 0.000E+00 OK 48 0 1=(z+x+y)
1 0.000E+00 0.000E+00 OK 48 0 z=(m+n+b)
1 0.000E+00 0.000E+00 OK 40 0 W_p1=m_dot*(n*v[1]*(P[2]-P[1]))
1 1.919E-08 -6.663E-06 OK 48 0 m_dot*(n)*(h[2]-h[1])=W_p1
1 1.121E-11 -8.325E-09 OK 64 0 h[2]*(n)+h[16]*m+h[6]*(x+y)+b*h[20]=h[3]
1 1.121E-11 6.662E-06 OK 72 0 H_recovery=m_dot*(n*h[2]+h[16]*m+h[6]*(x+y)+b*h[20])
1 0.000E+00 0.000E+00 OK 40 0 h_new[3]=H_recovery/m_dot
1 2.002E-14 3.512E-12 ? 82 109 T[3]=Temperature(Water,P=P[16],h=h_new[3])
1 6.245E-19 -6.217E-15 OK 40 0 m_dot*(h[4]-h_new[3])=W_p2
1 0.000E+00 0.000E+00 OK 40 0 T[4]=T[3]
1 0.000E+00 0.000E+00 OK 56 0 y*Cp[13]*(T[13]-T_sat[13])+y*h_fg[13]+h[4]+h[9]*x=(x+y)*h[5]+h[7]
1 1.152E-18 4.608E-19 OK 40 0 (T[7]-T[4])/(T[13]-T[4])=epsilon_CFWH2
1 1.104E-16 -1.099E-13 ? 40 0 h[7]=Enthalpy(Water,T=T[7],P=P[4])
1 8.468E-20 1.110E-16 OK 48 0 x*Cp[12]*(T[12]-T_sat[12])+x*h_fg[12]+h[7]=h[8]*x+h[10]
1 1.897E-18 7.589E-19 OK 40 0 (T[10]-T[7])/(T[12]-T[7])=epsilon_CFWH1
1 2.148E-17 -2.498E-14 ? 40 0 h[10]=Enthalpy(Water,T=T[10],P=P[4])
2 3.174E-10 -9.812E-05 OK 3 0 W_HPT=m_dot*(x*(h[11]-h[12])+y*(h[11]-h[13])+z*(h[11]-h[14]))
3 3.174E-10 -1.498E-04 OK 3 0 W_LPT=m_dot*(z*h[15]-m*h[16]-n*h[17]-b*h_brine[18])
4 2.525E-09 2.291E-03 OK 3 0 Q_out=m_dot*(n*(h[17]-h[1]))
5 4.229E-14 1.705E-12 ? 3 16 T[2]=Temperature(Water,P=P[16],h=h[2])
6 2.525E-09 5.329E-03 OK 3 0 Q_in=m_dot*(h[11]-h[10]+z*(h[15]-h[14]))
7 3.786E-11 3.905E-07 OK 3 0 W_total_pump=W_p1+W_p2+W_p3
8 2.525E-09 1.946E-03 OK 3 0 W_net=W_LPT+W_HPT-W_p1-W_p2-W_p3
9 4.218E-14 1.540E-12 OK 3 0 Eta_sys_new=(W_net/q_in)*100
10 3.786E-11 2.479E-06 OK 3 0 H_C[2]=m_dot*n*h[2]
11 2.525E-09 1.526E-03 OK 3 0 H_C[4]=m_dot*h[4]
12 3.174E-10 -8.958E-05 OK 3 0 H_C[5]=m_dot*(x+y)*h[5]
13 3.174E-10 -8.958E-05 OK 3 0 H_C[6]=m_dot*(x+y)*h[6]
14 2.525E-09 2.010E-03 OK 3 0 H_C[7]=m_dot*h[7]
15 3.174E-10 -3.758E-05 OK 3 0 H_C[8]=m_dot*(x)*h[8]
47. File:powerplant6 with optimum presuure.EES 4/25/2016 9:34:26 PM Page 9
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
16 3.174E-10 -3.758E-05 OK 3 0 H_C[9]=m_dot*(x)*h[9]
17 2.525E-09 2.349E-03 OK 3 0 H_C[10]=m_dot*h[10]
18 3.174E-10 -1.216E-04 OK 3 0 H_C[12]=m_dot*x*h[12]
19 2.525E-09 1.358E-03 OK 3 0 H_C[13]=m_dot*(y)*h[13]
20 2.525E-09 4.001E-03 OK 3 0 H_C[14]=m_dot*(z)*h[14]
21 2.525E-09 4.572E-03 OK 3 0 H_C[15]=m_dot*(z)*h[15]
22 3.174E-10 -6.544E-05 OK 3 0 H_C[16]=m_dot*(m)*h[16]
23 2.525E-09 2.456E-03 OK 3 0 H_C[17]=m_dot*(n)*h[17]
Parametric Table: overall viwe
b T10 sys,new Wnet T3 hnew,3 x y z n
Run 1 0 266 36.82 777195 175.4 742.9 0.1523 0.2144 0.6333 0.5428
Run 2 0.01 266 36.78 776298 175.4 742.9 0.1523 0.2144 0.6333 0.5345
Run 3 0.02 266 36.73 775400 175.4 742.9 0.1523 0.2144 0.6333 0.5262
Run 4 0.03 266 36.69 774502 175.4 742.9 0.1523 0.2144 0.6333 0.5179
Run 5 0.04 266 36.65 773605 175.4 742.9 0.1523 0.2144 0.6333 0.5095
Run 6 0.05 266 36.61 772707 175.4 742.9 0.1523 0.2144 0.6333 0.5012
Run 7 0.06 266 36.56 771810 175.4 742.9 0.1523 0.2144 0.6333 0.4929
Run 8 0.07 266 36.52 770912 175.4 742.9 0.1523 0.2144 0.6333 0.4846
Run 9 0.08 266 36.48 770014 175.4 742.9 0.1523 0.2144 0.6333 0.4763
Run 10 0.09 266 36.44 769117 175.4 742.9 0.1523 0.2144 0.6333 0.468
Run 11 0.1 266 36.39 768219 175.4 742.9 0.1523 0.2144 0.6333 0.4597
Run 12 0.11 266 36.35 767321 175.4 742.9 0.1523 0.2144 0.6333 0.4514
Run 13 0.12 266 36.31 766424 175.4 742.9 0.1523 0.2144 0.6333 0.4431
Run 14 0.13 266 36.27 765526 175.4 742.9 0.1523 0.2144 0.6333 0.4347
Run 15 0.14 266 36.22 764629 175.4 742.9 0.1523 0.2144 0.6333 0.4264
Run 16 0.15 266 36.18 763731 175.4 742.9 0.1523 0.2144 0.6333 0.4181
Run 17 0.16 266 36.14 762833 175.4 742.9 0.1523 0.2144 0.6333 0.4098
Run 18 0.17 266 36.1 761936 175.4 742.9 0.1523 0.2144 0.6333 0.4015
Run 19 0.18 266 36.05 761038 175.4 742.9 0.1523 0.2144 0.6333 0.3932
Run 20 0.19 266 36.01 760140 175.4 742.9 0.1523 0.2144 0.6333 0.3849
Run 21 0.2 266 35.97 759243 175.4 742.9 0.1523 0.2144 0.6333 0.3766
Run 22 0.21 266 35.93 758345 175.4 742.9 0.1523 0.2144 0.6333 0.3683
Run 23 0.22 266 35.88 757448 175.4 742.9 0.1523 0.2144 0.6333 0.36
Run 24 0.23 266 35.84 756550 175.4 742.9 0.1523 0.2144 0.6333 0.3516
Run 25 0.24 266 35.8 755652 175.4 742.9 0.1523 0.2144 0.6333 0.3433
Run 26 0.25 266 35.76 754755 175.4 742.9 0.1523 0.2144 0.6333 0.335
Run 27 0.26 266 35.71 753857 175.4 742.9 0.1523 0.2144 0.6333 0.3267
Run 28 0.27 266 35.67 752959 175.4 742.9 0.1523 0.2144 0.6333 0.3184
Run 29 0.28 266 35.63 752062 175.4 742.9 0.1523 0.2144 0.6333 0.3101
Run 30 0.29 266 35.59 751164 175.4 742.9 0.1523 0.2144 0.6333 0.3018
Run 31 0.3 266 35.54 750267 175.4 742.9 0.1523 0.2144 0.6333 0.2935
Run 32 0.31 266 35.5 749369 175.4 742.9 0.1523 0.2144 0.6333 0.2852
Run 33 0.32 266 35.46 748471 175.4 742.9 0.1523 0.2144 0.6333 0.2769
Run 34 0.33 266 35.42 747574 175.4 742.9 0.1523 0.2144 0.6333 0.2685
Run 35 0.34 266 35.37 746676 175.4 742.9 0.1523 0.2144 0.6333 0.2602
Parametric Table: overall viwe
m
Run 1 0.0905
Run 2 0.08881
Run 3 0.08712
Run 4 0.08543
Run 5 0.08374
Run 6 0.08205
48. File:powerplant6 with optimum presuure.EES 4/25/2016 9:34:26 PM Page 10
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
Parametric Table: overall viwe
m
Run 7 0.08036
Run 8 0.07867
Run 9 0.07698
Run 10 0.07529
Run 11 0.0736
Run 12 0.07191
Run 13 0.07022
Run 14 0.06853
Run 15 0.06684
Run 16 0.06515
Run 17 0.06346
Run 18 0.06177
Run 19 0.06008
Run 20 0.05839
Run 21 0.0567
Run 22 0.05501
Run 23 0.05332
Run 24 0.05163
Run 25 0.04994
Run 26 0.04825
Run 27 0.04656
Run 28 0.04487
Run 29 0.04318
Run 30 0.04149
Run 31 0.0398
Run 32 0.03811
Run 33 0.03642
Run 34 0.03473
Run 35 0.03304
Parametric Table: O.P for P_12
P12 sys,new Wnet x z y m n
Run 1 5000 31.71 596780 0.4112 0.4441 0.1447 0.04252 0.4016
Run 2 4500 33.04 635627 0.3552 0.4801 0.1647 0.05165 0.4285
Run 3 4000 34.21 673348 0.301 0.5171 0.1819 0.06104 0.4561
Run 4 3500 35.25 710418 0.2479 0.5559 0.1962 0.07088 0.485
Run 5 3000 36.17 747347 0.195 0.5973 0.2076 0.08139 0.516
Run 6 2500 36.97 784749 0.1415 0.6428 0.2158 0.09291 0.5499
Parametric Table: O.P for P_13
P13 sys,new Wnet x z y m n
Run 1 2000 36.88 796997 0.2084 0.6563 0.1353 0.09147 0.4923
Run 2 1900 37.01 803773 0.2123 0.6664 0.1213 0.09493 0.4989
Run 3 1800 37.14 810485 0.2163 0.6766 0.1071 0.09839 0.5057
Run 4 1700 37.25 817126 0.2205 0.687 0.09246 0.1019 0.5126
Run 5 1600 37.35 823694 0.2249 0.6976 0.07744 0.1054 0.5198
Run 6 1500 37.44 830179 0.2295 0.7085 0.06195 0.1089 0.5271
Run 7 1400 37.51 836574 0.2343 0.7197 0.04595 0.1124 0.5348
Run 8 1300 37.57 842866 0.2394 0.7312 0.02936 0.116 0.5427
49. File:powerplant6 with optimum presuure.EES 4/25/2016 9:34:26 PM Page 11
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
Parametric Table: O.P for P_reheat
Preheat rat sys,new Wnet x z y m n
Run 1 7000 0.5833 36.19 711192 0.1523 0.6333 0.2144 0.09917 0.5341
Run 2 6500 0.5417 36.29 717390 0.1523 0.6333 0.2144 0.09855 0.5347
Run 3 6000 0.5 36.38 723769 0.1523 0.6333 0.2144 0.09789 0.5354
Run 4 5500 0.4583 36.47 730346 0.1523 0.6333 0.2144 0.09717 0.5361
Run 5 5000 0.4167 36.55 737145 0.1523 0.6333 0.2144 0.09639 0.5369
Run 6 4500 0.375 36.63 744192 0.1523 0.6333 0.2144 0.09554 0.5377
Run 7 4000 0.3333 36.7 751514 0.1523 0.6333 0.2144 0.09459 0.5387
Run 8 3500 0.2917 36.76 759146 0.1523 0.6333 0.2144 0.09351 0.5398
Run 9 3000 0.25 36.8 767122 0.1523 0.6333 0.2144 0.09228 0.541
Run 10 2500 0.2083 36.82 775477 0.1523 0.6333 0.2144 0.09083 0.5425
Run 11 2400 0.2 36.82 777195 0.1523 0.6333 0.2144 0.0905 0.5428
Run 12 2000 0.1667 36.8 784222 0.1523 0.6333 0.2144 0.08905 0.5442
Parametric Table: P_brine
Pbrine,18 hbrine,18 b Wnet sys,new T3 Qdesel T10
Run 1 1200 2784 0.1 768577 36.41 175.4 158834 266
Run 2 1100 2781 0.1 768475 36.41 175.4 159971 266
Run 3 1000 2778 0.1 768385 36.4 175.4 161169 266
Run 4 900 2774 0.1 768310 36.4 175.4 162439 266
Run 5 800 2769 0.1 768253 36.4 175.4 163796 266
Run 6 700 2763 0.1 768219 36.39 175.4 165261 266
Run 7 600 2757 0.1 768216 36.39 175.4 166863 266
Run 8 500 2749 0.1 768255 36.4 175.4 168643 266
Run 9 400 2739 0.1 768355 36.4 175.4 170674 266
Run 10 300 2725 0.1 768551 36.41 175.4 173079 266
Run 11 200 2707 0.1 768919 36.43 175.4 176124 266
Run 12 100 2675 0.1 769698 36.46 175.4 180598 266
Parametric Table: 1234
b T10 sys,new Wnet T3 hnew,3 x y z n
Run 1 0 263.4 37.14 822747 179.9 762.9 0.2505 0.04243 0.7071 0.586
Run 2 0.01 263.4 37.13 822602 179.9 762.9 0.2505 0.04243 0.7071 0.5776
Run 3 0.02 263.4 37.12 822456 179.9 762.9 0.2505 0.04243 0.7071 0.5692
Run 4 0.03 263.4 37.12 822311 179.9 762.9 0.2505 0.04243 0.7071 0.5608
Run 5 0.04 263.4 37.11 822165 179.9 762.9 0.2505 0.04243 0.7071 0.5524
Run 6 0.05 263.4 37.1 822019 179.9 762.9 0.2505 0.04243 0.7071 0.5439
Run 7 0.06 263.4 37.1 821874 179.9 762.9 0.2505 0.04243 0.7071 0.5355
Run 8 0.07 263.4 37.09 821728 179.9 762.9 0.2505 0.04243 0.7071 0.5271
Run 9 0.08 263.4 37.08 821583 179.9 762.9 0.2505 0.04243 0.7071 0.5187
Run 10 0.09 263.4 37.08 821437 179.9 762.9 0.2505 0.04243 0.7071 0.5103
Run 11 0.1 263.4 37.07 821291 179.9 762.9 0.2505 0.04243 0.7071 0.5018
Run 12 0.11 263.4 37.06 821146 179.9 762.9 0.2505 0.04243 0.7071 0.4934
Run 13 0.12 263.4 37.06 821000 179.9 762.9 0.2505 0.04243 0.7071 0.485
Run 14 0.13 263.4 37.05 820854 179.9 762.9 0.2505 0.04243 0.7071 0.4766
Run 15 0.14 263.4 37.04 820709 179.9 762.9 0.2505 0.04243 0.7071 0.4682
Run 16 0.15 263.4 37.04 820563 179.9 762.9 0.2505 0.04243 0.7071 0.4598
Run 17 0.16 263.4 37.03 820418 179.9 762.9 0.2505 0.04243 0.7071 0.4513
Run 18 0.17 263.4 37.02 820272 179.9 762.9 0.2505 0.04243 0.7071 0.4429
Run 19 0.18 263.4 37.02 820126 179.9 762.9 0.2505 0.04243 0.7071 0.4345
Run 20 0.19 263.4 37.01 819981 179.9 762.9 0.2505 0.04243 0.7071 0.4261
50. File:powerplant6 with optimum presuure.EES 4/25/2016 9:34:26 PM Page 12
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
Parametric Table: 1234
m
Run 1 0.1211
Run 2 0.1195
Run 3 0.1179
Run 4 0.1163
Run 5 0.1147
Run 6 0.1132
Run 7 0.1116
Run 8 0.11
Run 9 0.1084
Run 10 0.1068
Run 11 0.1053
Run 12 0.1037
Run 13 0.1021
Run 14 0.1005
Run 15 0.09893
Run 16 0.09735
Run 17 0.09576
Run 18 0.09418
Run 19 0.0926
Run 20 0.09102
0.1 0.2 0.3 0.4 0.5 0.6
36
36.5
37
Preheat/P,boiler
sys,new
51. File:powerplant6 with optimum presuure.EES 4/25/2016 9:34:26 PM Page 13
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
35
35.5
36
36.5
37
37.5
38
b
sys,new
0 200 400 600 800 1000 1200
36.3
36.4
36.5
Pbrine[18]
sys,new
53. File:C:UsersAli AlkathiriDesktopSDPFinalMSF_32 (1).EES 4/25/2016 9:26:31 PM Page 1
EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
Inputs
Md = 378.8 Total Distillate Flow Rate
n = 42 Total Number of Stages
Tf = 25 [C] Feed Seawater Temperature
To = 160 [C] Top Brine Temperature
Tsteam = 170 [C] Steam Temperature
Tn = 40 [C] Brine Temperature In Last Stage
Xf = 42000 [ppm] Salinity of Feed Seawater
Cp = Cp water , T = Tf , x = 0 Heat Capacity of Liquid Streams
Cd = 0.5 Weir Friction Cofficient
Vvn = 6 [m/s] Vapor Velocity in the last Stage
Vb = 180 [Kg/ms] Brine Mass Flow Rate Per Stage Width
Temprutres Calcution
Tavg =
To + Tn
2
Avrege Temperature of Brine Seawater
hfg,av = Enthalpyvaporization SteamIAPWS , T = Tavg Heat Transfer Coefficient of Braine at Avrege Temperature
hfg,steam = Enthalpyvaporization SteamIAPWS , T = Tsteam Heat Transfer Coefficient of steam at Steam Temperature
T =
To – Tn
n
The Temperature Drop Per Stage
Ti = To – T Temperature at i Stage
Tii,DELTA,T = To – T · i for i = 1 to n
T1 = Tf + n · T Seawater Temperature leavs the first stage of the condenser
T2 = T1 – T Seawater Temperature leavs the second stage of the condenser
i = Tf + n – i – 1 · T for i = 1 to n xi=Ti
flow rate
for first stage
y = Cp ·
T
hfg,av
specific ratio of Sensible Heat
Md = Mf · 1 – 1 – y
n
Mb = Mf – Md Rejected Brine Mass Flow Rate
Xf · Mf = Xb · Mb X is Salt Concentration
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EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
Msteam = Mf · Cp ·
To – T1
hfg,steam
Steam Flow Rate
Heat transfer Area
Ab = Msteam ·
hfg,steam
Ub · Tlmtd,b
Area of Brine Preheater
Ub = 1.7194 + 3.2063 · 10
– 3
· Tsteam + 1.5971 · 10
– 5
· Tsteam
2
– 1.9918 · 10
– 7
· Tsteam
3
Tlmtd,b =
Tsteam – To – Tsteam – T1
ln
Tsteam – To
Tsteam – T1
Logarithmic Mean Temperature
Ac =
Mf · Cp · T1 – T2
Uc · Tlmtd,c
Area of Condenser
Uc = 1.7194 + 3.2063 · 10
– 3
· Tv,1 + 1.5971 · 10
– 5
· Tv,1
2
– 1.9918 · 10
– 7
· Tv,1
3
Tv,1 = Ti – BPE1 – NEA1 – T1 Vapor Temperature
BPE1 = X1 · B + X1 · C · 10
– 3
Boiling Point Elevation
X1 =
Mf · Xf
B1
B1 = Mf – D1
D1 = y · Mf Amount of Flashing Vapor Formed in First Stage
B = 6.71 + 6.34 · 10
– 2
· Ti + 9.74 · 10
– 5
· Ti
2
· 10
– 3
C = 22.238 + 9.59 · 10
– 3
· Ti + 9.42 · 10
– 5
· Ti
2
· 10
– 8
NEA1 = 0.9784
To
· 15.7378
H1
· 1.3777
Vb
· 10
– 6
Non-Equilibrium Allawnce
T1 = 0
Tlmtd,c =
Tv,1 – T1 – Tv,1 – T2
ln
Tv,1 – T1
Tv,1 – T2
A = n · Ac + Ab
Dii = Mf · 1 – 1 – y
i
for i = 1 to n Md in each stage + all stages before
Mb,i = Mf – Mf · 1 – 1 – y
i
for i = 1 to n R = Mb
Xi =
Mf · Xf
Bi
for i = 1 to n
Bi = Mf – Dii for i = 1 to n
stage Diamentions
GH1 =
Mf · 2 · bi · Pi
– 0.5
Cd · W
Gate Height
bi = 1002
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EES Ver. 9.901: #1696: Department of Mechanical Engineering King Fahd University Petroleum and Minerals
Pi = 10490 Presure in each Stage
GH : gate height
dP : stage pressure drop
db : brine density
Cd : weir friction coeff.
W : stage width
H1 = 0.2 + GH1
W =
Mf
Vb
Width of the Stage
PR =
Md
Msteam
Performance
Q = Msteam · hfg,steam
Msteam = 800 · bex Bex : the extraxtion fraction of mass flow rate
SOLUTION
Unit Settings: SI C kPa kJ mass deg
A = 46290 [m2
] Ab = 4156 [m2
]
Ac = 1003 [m2
] B = 0.01908
BPE1 = 1.27 [C] B1 = 1885 [Kg/s]
bex = 0.07253 C = 2.607E-07
Cd = 0.5 Cp = 4.183 [Kj/Kg-C]
bi = 1002 Pi = 10490
T = 2.857 [C] T1 = 0 [C]
D1 = 10.04 [Kg/s] GH1 = 0.07852 [m]
H1 = 0.2785 [m] hfg,av = 2256 [Kj/Kg]
hfg,steam = 2049 [Kj/Kg] Mb = 1516 [Kg/s]
Md = 378.8 [Kg/s] Mf = 1895 [Kg/s]
Msteam = 58.03 [Kg/s] n = 42
NEA1 = 0.06547 [C] PR = 6.528
Q = 118885 T1 = 145 [C]
T2 = 142.1 [C] Tavg = 100 [C]
Tf = 25 [C] Ti = 157.1 [C]
Tlmtd,b = 16.37 [C] Tlmtd,c = 12.18 [C]
Tn = 40 [C] To = 160 [C]
Tsteam = 170 [C] Tv,1 = 155.8 [C]
Ub = 1.747 [Kw/m2
-C] Uc = 1.853 [Kw/m2
-C]
Vb = 180 [Kg/ms] Vvn = 6 [m/s]
W = 10.53 [m] X1 = 42224
Xb = 52496 [ppm] Xf = 42000 [ppm]
y = 0.005297
264 potential unit problems were detected.
Arrays Table: Main
i Dii Bi Xi Mb,i Tii,3
1 145 10.04 1885 42224 1885 157.1
2 142.1 20.02 1875 42449 1875 154.3
3 139.3 29.95 1865 42675 1865 151.4