Software Analyzed Systems and their Flow Diagrams

8.1 Contents of the Compact Disc

The compact disc (8.5 MB) consists of six executable programs, their essential data files, and dynamic loading library files. Two related documents are also included. Five programs deal with analysis and one program deals with tutorial material (book-solved tutorial examples and source code examples). They are grouped as follows:

8.1.1 The six executable programs

SystemTL: System analysis tool (user describes the system) DesalTL: System analysis tool (user selects the system) NovelsysTL: Systems involving new concepts and/or new devices VarloadTL: Variable load penalty

DeviceTL: Design analysis tool of energy conversion devices TutorTL: The solved problems and examples of the source code

8.1.2 Document (read by Microsoft Word) Handbook: Programs' descriptions and their flow diagrams

8.1.3 Document (read by Powerpoint) Book.sld: Slides that introduce the subject matter of the book in simple terms

8.1.4 Hardware requirements

Hardware requirements may call for a recent PC. The programs have been developed on a PC having Windows ME and Intel 3 processor. The source code is in BASIC. The version used is True Basic. The language supports many platforms including W95, W98, OS/2, and Macintosh.

(55 program units) (25 program units) (38 program units) (36 program units) (62 program units) (10 program units)

8.2 Brief Description of the Six Executable Tools

The following is a brief description of the six executable tools, the systems analyzed by the first five analysis tools and their flow diagrams. More details including flow diagrams are retrievable from the document "Handbook."

8.2.1 Energy analysis tool

(SystemTL.exe) (Last Revision, June 2000)

Purpose:

The tool helps you create your design concept of an energy system from elementary processes, then lets you analyze the system and optimize it for maximum efficiency or for a minimum cost. It is the testing ground for your energy-saving ideas for systems that use or produce useful energy.

Description:

The tool is a set of three executable programs. By itself, it is complete with a usermanual and reprints of recent publications on the methodology of analysis. The programs, supported by internal files, are available on compact discs or 3.5 diskettes running on IBM PC or compatible. The tool gives the user answers to the following inquiries:

• A working fluid energy state.

• Energy changes in a process.

• The energy resource utilization in a system of processes.

• The cost of the system's major processing components.

• The influence of system's decision parameters on fueling resource and cost.

• Automated optimization of decision parameters for minimum fuel or cost.

The systems to be analyzed are limited by the working fluids and the elementary processes of the database. To show the utilization of an energy resource in a system, the Second Law of Thermodynamics is invoked beside the routine mass and energy balances. This allows the computation of the work potential of an energy state and the destruction of this potential in a process. The distribution of potentials and their destructions (exergy and exergy destruction) display the energy resource utilization in the system. To estimate the cost of a processing device, a specific state-of-art design model is invoked and the cost is based on the materials and geometry as derived from the design model. Exergy computations and design-based costing of system devices are two distinguishing features of the methodology of analysis.

The first two inquiries are answered by one simple subroutine "Thermodynamic calculator." The rest of the inquiries are answered by two more elaborate subroutines: one receives the description of the system and the other does the analysis and the optimization. Systems of one or two products (co-generation) can be described. Description is entered in tables from a prepared flowsheet of numbered processes (1,2,... ,np.) and numbered states (1,2,... ,n\.). The three subroutines are available as separate programs as well.

The use of the thermodynamic calculator is simple and straightforward. The program, aided by simple hand computations, can even be used to answer the first three inquiries by moving from one process to another without the need for formal system description. In this respect, it is a crude system synthesizer/analyzer. It is also useful for checking the validity of certain computations or audited measurements in energy audit of existing plant.

The use of the system describer and the system analyzer is simple but not straightforward since the description by the first must be fully understood by the second. The describer receives the system description inputs in table form and saves them in an internal file for use by the analyzer. A simple graphic subroutine saves and retrieves flow-sheet files. Systems having up to 70 stations, 40 components, and 150 decision variables can be handled.

The analyzer computes the energy picture of a described system for the values assigned to the system decision variables and displays the results as summary or detailed. The detailed results are displayed in a way that helps the user to explore options of improvement. The program allows changes in the values of all the decision variables and permits the optimization of a meaningful subset of them. A subroutine surveys all heat exchange profiles for temperature crossings and pinch points. An optimization routine automates the optimization of the selected system decision variables for either minimum fuel or minimum cost.

Data Base

The programs are supported by a database of fluid properties, elementary process models, and costing equations. Properties' database contains the equations essential to compute the thermodynamic and transport properties of H20, NH3, R12, NH3/H20 mixtures, 7 ideal gases (02, N2, H20, C02, S02, CO and H2), their mixtures which can cover air, gas mixtures, combustion gases dry or wet, and 7 liquids (lubricating oil, ethylene glycol, glycerin, kerosene, sodium, bismuth, mercury and sea water/brines). Refrigerants R142a and R153b are now included. They accessed when called for. Processes' database contains 22 elementary processes that are the building blocks of systems and their computation. The use of elementary processes allows a large number of systems to be described. The main elementary processes handle expansion, compression, heat exchange, mixing, combustion, and throttling. Few processes are simple combinations of the elementary processes such as a multistage process. Few are purely computational such as splitting, merging, and tearing. Costing database has 20 design-based costing equations covering one or more types of devices. The characterizing dimension of a device (often a surface area) is based on design models encoding the design practices of their respective devices. The costing equations have the form k n x/" and their variables {xj are already set. The coefficient k and the exponents {«,} of an equation are computed by the respective design model. Their change should be based on a specified design model. The unit cost associated with the characterizing dimension can be changed to suit different economic environments. Obviously, the capability of the software is limited by the provided database.

Library

System-descriptions are saved in internal files and a library keeps track of them. The library is divided into a software-library of files for user analysis without access to change and a user-library of the files that are worked out and saved by the user. These are accessible to change, modification, or derivation of a new description. Description printouts are obtainable for all files. Software-library has 10 files listed as follows:

FS1 Simple combined cycle

FS2 2-Pressure-boiler combined cycle.

FS3 2-Pressure-boiler combined cycle, high temp gas turbine with cooled blades.

FS4 3-Pressure combined cycle with a reheater.

FS5 Sub-critical steam-power cycle with 5 feed heaters.

FS6 Super-critical steam-power cycle with 5 feed heaters.

FS7 Gas turbine power/heating co-generation system.

FS8 Gas turbine power/cooling co-generation system.

FS9 Gas turbine power/MSF seawater distillation co-generation system.

FS10 Back-pressure steam turbine power/ME distillation co-generation system.

An additional file is left as user file FU1 to serve as a vehicle to familiarize the user with the nature of the software. This file has the shortest description. It describes a simple gas turbine cycle (5 states and 3 devices). Next in complexity is the simple combined cycle FS1. It familiarizes with the heat exchange processes and heat transfer computations.

Systems of Units

The software accommodates both the British system of units IP and the international system of units SI for both its inputs and outputs. The default is IP. All software library description files happened to be in IP units but analysis outputs can be displayed in either system of units.

8.2.2 Power, distillation, and power ¡distillation systems (DesalTL.exe) (Last Revision November 2000)

Purpose

This software deals with the analysis and the optimization of 27 configurations of power distillation and power/distillation systems. Second-law analysis plays an important part in the optimization technique particularly when the objective is cost minimization through design improvement. The software is simple to use but unlike the energy analysis tool "SystemTL," this software "DesalTL" does not allow the user to synthesize his/her own system. The software consists of a number of IBM-compatible executable programs. The systems are divided into three groups:

• Steam power-multi-stage flash "msf" and multiple-effect "me" distillation systems.

• Gas turbine power multi-stage flash "msf" and multiple-effect "me" distillation systems.

• Vapor compression "vc" distillation systems.

The programs contain sufficient guidelines and instructions for convenient use. An overview of the guidelines follows.

Description

Twenty-seven systems along with their flowsheets are modeled for analysis and optimization. The systems are:

1. The simple steam boiler and msf distillation system importing its power requirement.

2. Same as (1) but using a two-stage recovery ejector.

3. Same as (1) but using a thermo-compression recovery effect.

4. Same as (1) but producing its own power requirements.

5. Same as (1) but using a thermo-compression multiple effect distillation system.

6. Back-pressure steam power with 3 feed heaters and msf cogeneration system.

7. Same as (6) but with one reheater instead of the feed heaters.

8. Same as (6) but using a multiple effect distillation system.

9. Same as (8) but using an extraction steam turbine instead of a backpressure turbine.

10. Condensing steam turbine with 3 feed heaters power-only system.

11. Backpressure turbine with 3 feed heaters, vapor compression, msf water-only system.

12. Gas turbine power with heat recovery steam generator, msf distillation system.

13. Same as (12) but with a multiple effect distillation system.

14. Same as (12) but with a backpressure steam turbine.

15. Same as (13) but with a backpressure turbine.

16. Simple gas turbine power-only system.

17. Simple combined cycle power-only system.

18. Gas turbine, heat recovery steam generator, vc, msf water-only system.

19. Same as (18) with a backpressure steam turbine.

20. Stand-alone vc operating sub-atmospheric.

21. Stand-alone vc operating at atmospheric pressure.

22. Stand-alone atmospheric pressure flash.vc water-only system.

23. Stand-alone atmospheric pressure msf.flash.vc water-only system.

24. Backpressure steam turbine with 3 feed heaters, flash.vc, msf water-only system.

25. Backpressure steam turbine with 3 feed heaters, msf.flash.vc, msf water-only system.

26. Gas turbine with heat recovery steam generator, flash.vc, msf water-only system.

27. Gas turbine with heat recovery steam generator, msf.flash.vc, msf water-only system.

For each system the following procedures are available:

1. Run the system at its reference design point (default).

2. Perform sensitivity analysis by changing one or up to 20 decision variables.

3. Optimize for minimum fuel consumption.

4. Optimize for minimum production cost for a given production rate.

5. Change the coefficient and exponents of a costing equation (design-model-based change).

6. Review results on the screen (tabulated and graphical).

7. Print a short or a long form of results.

8.2.3 New concepts and devices analysis tool

NovelsysTL.EXE (Last Revision September 2002)

Purpose

The tool is a testing ground for new concepts of systems and devices of emerging technologies. The target is raising efficiency and/or lowering production cost. The tool is the home of programs in which each deals with a concept and its alternative solutions.

Extent of Coverage

Two analyses are performed so far. The first deals with coal-fired power plants and the second with fuel cells.

1. Coal-Fired Plants

The question posed is: can the efficiency (power/fuel) be raised cost-effectively? The motives of addressing this question are:

Coal resources are relatively large.

Treatments of coal and products of combustion are developed. Higher efficiency means less pollution for same output product.

A conventional 50 MW plant burning pulverized bituminous coal and treating exhaust by precipitators and acid scrubbing is modeled for cost and performance. Its design point serves as a reference. Two directions are sought:

(a) Raise the superheat temperature to 1200-1400°F instead of the current 700-1000°F.

(b) Lower the firing temperature to 2600° F by water-walls-boiler. Introduce a high temperature heat exchanger (may be ceramic) to heat air as the working fluid of a blade-cooled gas turbine cycle with air leaving turbine close to ambient. Keep the rest of plant conventional.

One alternative solution is analyzed for the first direction and four alternatives are analyzed for the second. Cost effectiveness is discussed for each alternative.

Systems performance and cost models are expressed in terms of thermodynamic efficiency and loading parameters. Adiabatic efficiency, heat exchange effectiveness, and pressure loss ratio are examples of efficiency parameters. Mass rate, heat rate, power and pressure, and temperature levels are examples of loading parameters. Systems having up to 70 states, 40 processes and 120 decision variables can be accommodated. Default fuel is bituminous coal of given composition rated at 0.003 $/kWh higher heating value. The analysis allows the change of the default composition and fuel cost beside the change of any decision variable. Up to 20 decisions can be changed. Infeasible solutions are flagged. A default cost multiplier of 2 is applied to all devices exposed to temperatures higher than conventional. The flow diagrams are given in Section 8.3. Process numbers are in bold to distinguish them from state numbers. Results are available in British and International units (IP and SI). Results are given in six tables (summary, states, composition, processes, exergy destructions, and costs). T-Q diagrams of all heat exchange devices are available for inspection for pinch or temperature crossing along with tabulated values.

2. Fuel Cells

Direct conversion of fuel energy to work with no moving parts is a tremendous advantage in the field of energy conversions. At the moment, all attempts have to accommodate the technology of a hydrogen/oxygen fuel cell. Natural gas is the easiest fuel to preprocess fuel cell reaction, yet the processing involves too many energy conversions that limit the conversion efficiency. Processing includes S02 removal to protect fuel cell catalyst, reformer to generate the fuel cell hydrogen and a shift converter to convert any CO produced in the reformer process to C02.

A 200 kW low-temperature phosphoric acid fuel cell is used as a vehicle to gain insight in the fuel cell deficiencies, advantages and the limitations of current fuel cell technology. The fuel cell serves also as a reference case for future studies. The considered fuel cell tries to tap part of generated heat to co-generate hot water beside power. The analysis is limited to efficiency because of insufficient data on costs. The fuel cell has 59 states, 35 processes, and 82 decision variables. The decisions are mainly efficiency parameters, essential temperature and pressure levels, and the 200 kW as sizing parameter. The fluids involved are H20, 10-species ideal gas mixture covering the natural gas, air, combustion gases, and reformer gases. Ethylene glycol is introduced to help recover much of the reformer H20. The flow diagram is given in Section 8.3. Process numbers are shown in boxes. The analysis allows the change of all decision values. Up to 20 variables can be changed per run. Infeasible solutions are flagged. The overall system efficiency and the co-generated heat are presented as function 5 efficiency parameters (one parameter at a time). The parameters are the fuel cell extent of reaction, the fuel cell efficiency, the reformer extent of reaction, the reformer excess steam ratio, and the temperature subcooling by the control heat exchanger. Results are available in British units (IP) and partly in International units (SI). Results are given in six tables (summary, states, composition, processes, exergy destructions, and costs). T-Q diagrams of all heat exchange devices are available for inspection for pinch or temperature crossing along with tabulated values.

8.2.4 Variable-load design analysis tool

VarloadTL.EXE (Last Revision February 2001)

Purpose

The tool is meant to provide insight into the impact of variable demands on system efficiency and hence fuel consumption penalty.

Complexity

The handling of variable-load system-design is more complex than that of handling base-load system-design. Load variability raises several questions:

• Which load should be the design load?

• How will the system efficiency respond to the load variation?

• How much can a control strategy reduce inefficiency?

• How much is the mismatch between products and demands for multi-product cases?

• What system configuration is suited for the nature of load variation?

The answers to these questions call for an increased dose of computation. The dose of computation can be one order of magnitude higher than that of base-load design. The tool addresses three main variable-load problems:

1. Screening alternative designs for one or two alternatives that are most competitive. A reliable screening procedure is needed for handling the large number of alternatives that are generated by the complexity of load variation.

2. Predicting the part-load performance of a design concept of a system.

3. The optimal operating mix of a group of existing plants.

All three situations attempt to reduce the in-efficiency of fuel utilization resulting from off-design performance.

1. Screening for the most competitive system design concepts

The approach to variable-load system design assumes base-load design followed by running the design through the repeatable pattern of the variable-load profiles and computing the fuel penalty due to the lower efficiency resulting from off-design operation. The keyword is: "Design constant load, operate variable load."

The overall system efficiency as function of load is approximated by a quadratic equation. A system efficiency measure = a + ¿> * X+c* X2 guided by the performance of similar operating plants within the permissible X range of the system which is usually 0.4 to 1.1. A screening process based on this approximation becomes convenient to handle a large number of alternative systems.

Two main configurations for the supply of power, cooling and heating to meet a variable demand of each are analyzed. They both use gas turbine power units to meet given variable demands, stand-alone facilities and as grid-connected. The variable demand pattern used assumes a summer condition repeatable every 24-h of an hourly-base change. The complexity of the demand pattern poses no limitation on the method of analysis.

The first configuration uses heat-driven cooling and heating. A low-pressure heat-recovery steam generator supplies heat to a single-effect lithium bromide absorption refrigeration unit and to a steam heating coil.

The second configuration uses power-driven cooling and heating. The gas turbine becomes a combined cycle for power and supplies external power, power to a mechanical vapor compression system for cooling, and power to a heat pump for heating. The working fluid in both machines is R142b.

For each configuration, four efficiency levels are considered: low, medium, high, and minimum-cost optimal. The high level efficiency assumes high gas turbine firing temperature. For each level, an expected and a low value of the constant "a" of the quadratic equation is used.

For the first, any mismatch in the needed power and process steam is handled simply by venting and supplementary firing. Two heat recovery procedures are investigated to reduce the fuel loss by these two convenient but inefficient processes. One recovery uses the vented hot gas to a gas turbine regenerator with heat storage to raise the air temperature before firing. The other uses a vapor-compression cooling unit with storage to move the supplementary firing to main gas-turbine firing-point. Thus the first configuration generated three other configurations.

For the second configuration, the recovery problem is absent. Any mismatch is by fuel at the gas-turbine firing-point. The results of all the above cases are summarized on a cost-efficiency diagram. The cost is the total cost per unit exergy delivered by power heating and cooling. The efficiency is the delivered exergy/fuel exergy. The diagram simply indicates that the name of the game is higher efficiency devices less sensitive to load variation at same or lower cost.

2. Predicting part-load performance of a system design-concept

A screening program simplifies the part-load performance by considering an overall system efficiency quadratic in load fraction without considering the off-design performance of the system devices. Although this is most convenient for the purpose of screening, not much insight is gained in what is happening within the system.

Predicting system efficiency in terms of the off-design efficiencies of its devices gives the insight needed.

The system considered is a simple combined cycle optimally designed for minimum cost along a specified control strategy. The load ratio is changed in steps from 1 (full load) to 0.5. With a given system and given control strategy, there is no more free design decision to optimize. The number of variables equals the number of equations. When the load changes, certain control variables are changed to meet the load and the system proceeds to a different steady state.

The predicting program has a default control strategy that may be changed by the user by a part-load descriptor by filling in three tables according to given instructions.

3. Optimal operation of a group of existing plants

The program of the optimal operating mix of plants considers single purpose power plants. It is assumed that all the participating plants are available for running. The program is also applicable to two product plants if the demand for one of the products is constant.

The program assumes that the overall performance equation of a plant can be satisfactorily expressed by a quadratic equation between full load ratio 1 and a minimum allowable load ratio (e.g. 0.3).

The overall performance equation can be presented as fuel rate consumption given demand, as fuel consumption per unit product, or as product per unit fuel (efficiency). The first two forms are to be minimized. The last one is to be maximized. It is important to make sure that the right form is extremized the right way (for a minimum or for a maximum). A wrong form will increase fuel not reduce it. A concave shape usually gives a maximum. A convex shape usually gives a minimum.

The quadratic equation a + b* X+c* X2 can be concave or convex depending on the values and the signs of a, b, and c. Usually a negative c gives a maximum and a positive c gives a minimum.

The program uses a simple mathematical method that is applicable to any form. The program considers a facility of four power plants as an illustrative example. The overall system performance of each plant is considered once as fuel rate, once as fuel per unit power, and once as efficiency. The method works whatever the values of a, b, and c are, so long as the equation represents a fuel saving parameter. The three quadratic forms that may be used to correlate off-design performance are:

Specific fuel consumption = q = Qf/(X * P) = i/3 - 63 * X + C3 * X2

X is load fraction, P is the design power of a plant, and Qf is a fuel consumption rate. The constants {a, b, c} are adjusted to fit a particular performance.

The program also allows a user to enter up to 10 power plants and their overall performance equations by any of the three forms of the quadratic equation. A file stores the last tried facility. The current stored case has six plants having first form performance equation.

The optimization procedure is that of the Lagrange multiplier and uses only one multiplier. The objective function (the minimum of sum of the fuel consumptions Q's by all the plants by whatever form used) is augmented by the constraint that the sum of partial loads equals the demand for a time period over constant demand along with an undetermined multiplier multiplied to the constraint to treat the load fractions as decision variables. This ties the constants b and c of a load to the Lagrange multiplier.

The problem reduces of finding the value of the multiplier that satisfies all plants for values of load fractions between zero and one. The process of finding this multiplier is fast because it is bounded by a lower and higher value corresponding to zero load and full load. A Newton-Raphson search converges quickly. The trick is computing two values for each update and not to rush to try single values.

8.2.5 Device design analysis tool

DeviceTL.EXE (Last Revision February 2001)

Purpose

The tool is meant to provide insight into the interaction between design parameters of a device (mainly dimensions and shapes) and its thermodynamic parameters (mainly efficiencies and loading rates).

Advantages

The insight offers the following four advantages:

• Revealing opportunities of higher device efficiency and/or lower material content by modifying design parameters.

• Providing a rational basis for predicting the material content of a device in terms of its performance parameters.

• Establishing the concept of costing equations. These are costs of devices in terms of their performance parameters. The material content translates to cost by estimated unit material costs.

• Enhancing the optimization of a system of devices designed for a minimum product cost by the concept of costing equations.

The approach to design analysis

The approach consists of two steps:

• Quantifying the resources of a device.

• Expressing the resources by common variables.

• Quantifying device resources:

- Any energy conversion device requires two types of resources; resources to make it and resources to operate it. The value of the first is related to the material content of the device and their shapes. The value of the second is related to the device impact on fueling resources when the device performs its energy conversion process. An ideal device has zero impact.

- The material value of a device is described by many dimensions and each dimension is associated with a unit cost depending on the material type and the manufacturing difficulty of its shaping. Quite frequently, one dimension dominates and hence the value of the device can be fairly expressed by one dimension and one unit cost. In this software, a device cost is expressed by one dimension that is a surface area A of heat exchange, mass exchange, or momentum exchange.

- The device fueling resource is simply its exergy destruction D (Lost Work) at a price depending on that of the fueling resource and the way the device is connected with other devices to the fueling resource.

• Expressing the two resources by common variables

- The surface area A and the exergy destruction D can be expressed by a set of variables {V} to establish a sought relationship of interest between them. The set {V} may be all design variables, thermodynamic variables, or a combination of both. A thermodynamic-design correlating-matrix TDCM is generated to contain several values of A and their corresponding values of D over a {V\ range of interest. A curve-fitting procedure then gives A ({K}) and D ({K}).

- This software, being one of three tools of a common purpose, expresses the sought relationship in terms of thermodynamic variables only. The design model of a device repeats the design process over the {V} range of interest to generate the correlating matrix TDCM. The generation of the matrix is either manual or automated.

- The input parameters to the design model depend on its computational algorithm. A loading parameter such as mass rate, power, or heat rate is usually an input. Efficiency parameters such as adiabatic efficiency, effectiveness, or pressure loss ratio may be inputs or outputs. Being inputs implies a tailored design to meet a required performance. Being outputs implies the best efficiency the design model can offer.

Extent of software coverage

A reasonable number of design models have been considered for about 20 energy conversion devices of interest to power generation, co-generation, and refrigeration.

The design models target the characterizing surfaces of the devices. The description of the design models and their references are included.

The design models are listed under four broad device categories:

• Heat and mass exchange devices.

• Power driven or driving devices.

• Separation devices.

• Miscellaneous devices.

The software contains the following devices so far:

• Convective heat transfer devices:

- A number of single and two-phase fluids are covered. Condensation and boiling heat transfers are included. Annular, shell-and-tube, and fin-plate types of exchangers are considered. The heat exchange surfaces are the characterizing dimensions. These devices are covered under the first category.

• Compressors, pumps, and turbines:

- The coverage is limited to two types of compressors, centrifugal pumps, axial gas turbines and axial steam turbines. The compressors are axial and radial air and/or steam compressors. The momentum exchange surfaces (blades) are the characterizing dimensions. The generation of correlating matrix is automated for the pumps and is manual for the compressors and the turbines. The devices are covered under the second category.

• Seawater desalting processes:

- Four main processes are considered. These are the multistage flash distiller MSF, the multiple effect distiller ME, the vapor compression distiller VC, and the reverse osmosis desalter. The separating surfaces are their characterizing dimensions. The surfaces are heat transfer surfaces for the first two, heat and momentum exchange surfaces for the third and a mass exchange surface for the fourth. The surfaces are estimated provisionally by assumed overall transfer coefficients. Design models then finalize the surfaces and the overall transfer coefficients. The design model of the MSF is integrated and the generation of the correlating matrix is manual. The design models of ME, VC, and RO are still to be integrated.

8.2.6 Tutorial tool

TutorTL.EXE (Last revision February 2001)

8.2.6.1 Book-solved examples and problems

Example 4: counter flow ht exchanger, Tutorial 1, TutorTL. (Chapter 3.7.1)

Example 2: solve one variable property eqn., Tutorial 2, TutorTL. (Chapter 4.6.1)

Example 3: solve two nonlinear eqns., Tutorial 3, TutorTL. (Chapter 4.6.1)

Example 4: solve two variables dissociation, Tutorial 4, TutorTL. (Chapter 4.6.1)

Example 5: optzn simple gas turbine, Tutorial 5, TutorTL. (Chapter 4.6.1)

Desalination design improvement journey, composed from DesalTL. (Problem7.1.1) Gas turbine design improvement journey, composed from SystemTL. (Problem7.1.2) Cost-effectiveness of higher efficiency coal-fired power plants,

NovelsysTL. (Problem 7.1.3)

A small fuel-cell cogeneration system, NovelsysTL. (Problem 7.1.4) C. Cycle, off-design performance prediction, composed from

VloadTL. (Problem7.2.1)

Variable demands of power and heat, composed from VloadTL. (Problem7.2.2) Optimal operation of a power-generation facility, composed from VloadTL. (Problem7.2.3)

8.2.6.2 Example program units in the source code (a BASIC Language)

Thermodynamic properties of H20, TutorTL

Transport properties of all included working fluids, TutorTL

The Tutorial Solved Examples

Renewable Energy Eco Friendly

Renewable Energy Eco Friendly

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable.

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