Heating Value Estimation for Natural Gas ApplicationsVijay Sarathy
For natural gas custody transfer applications, the gross calorific or gross heating value is necessary for both the buyer and seller to estimate the sales price of natural gas. In case of fuel suppliers, heat content is expressed in terms of Higher Heating value (HHV) to estimate fuel charges in kWh. Whereas Lower Heating Value (LHV) is employed to estimate fuel requirements since the total energy input for a specific power output is already fixed. To understand how fuel heating values are affected, LHV and HHV is explained as,
Key Thermo-Physical Properties of Light Crude OilsVijay Sarathy
Process facilities are equipped with protection measures, such as pressure safety valves (PSV) & as a minimum, PSVs are sized for a fire case. To do so for a pressure vessel containing crude oil a key parameter is the Latent heat of Vaporization [Hv].
For pure components, the Joback’s Method can be employed which uses basic structural information of the chemical molecule to estimate thermo-physical data. However it can be complex for equipment that contains crude oil because the plus fractions [C7+] can contain thousands of straight chain, cyclic & functional groups. Therefore by splitting and lumping the crude fractions, a smaller number of components are arrived at, to characterize and be able to apply Equation of State (EoS) correlations to estimate the fraction’s thermo-physical properties.
Heating Value Estimation for Natural Gas ApplicationsVijay Sarathy
For natural gas custody transfer applications, the gross calorific or gross heating value is necessary for both the buyer and seller to estimate the sales price of natural gas. In case of fuel suppliers, heat content is expressed in terms of Higher Heating value (HHV) to estimate fuel charges in kWh. Whereas Lower Heating Value (LHV) is employed to estimate fuel requirements since the total energy input for a specific power output is already fixed. To understand how fuel heating values are affected, LHV and HHV is explained as,
Key Thermo-Physical Properties of Light Crude OilsVijay Sarathy
Process facilities are equipped with protection measures, such as pressure safety valves (PSV) & as a minimum, PSVs are sized for a fire case. To do so for a pressure vessel containing crude oil a key parameter is the Latent heat of Vaporization [Hv].
For pure components, the Joback’s Method can be employed which uses basic structural information of the chemical molecule to estimate thermo-physical data. However it can be complex for equipment that contains crude oil because the plus fractions [C7+] can contain thousands of straight chain, cyclic & functional groups. Therefore by splitting and lumping the crude fractions, a smaller number of components are arrived at, to characterize and be able to apply Equation of State (EoS) correlations to estimate the fraction’s thermo-physical properties.
Chemical Process Calculations – Short TutorialVijay Sarathy
Often engineers are tasked with communicating equipment specifications with suppliers, where process data needs to be exchanged for engineering quotations & orders. Any dearth of data would need to be computed for which process related queries are sometimes sent back to the process engineer’s desk for the requested data.
The following tutorial is a refresher for non-process engineers such as project engineers, Piping, Instrumentation, Static & Rotating Equipment engineers to conduct basic process calculations related to estimation of mass %, volume %, mass flow, actual & standard volumetric flow, gas density, parts per million (ppm) by weight & by volume.
Cooling Towers in Process Industries are part of Utilities design. As the name suggests their primary purpose is to provide cooling requirements to industrial hot water from unit operations & unit processes. Examples include chillers and air conditioners. The principle of operation is to circulate hot water through a tower and allow heat dissipation to the ambient. Cooling towers can operate by natural draft or forced draft methods wherein fans are used to increase heat transfer.
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Natural Gas Pipelines often suffer from production losses due to hydrate plugging. For an effective hydrate plug to form, factors can vary from pipeline operating pressure and temperature, presence of water below its dew point, extreme winter conditions & Joule Thomson cooling. In the event hydrates form in the pipeline section, their consequence depends on how well the hydrates agglomerate to grow and form a column. If the pipeline section temperature is only at par with the hydrate formation temperature, the particles do no agglomerate; instead they have to cross the metastable region which is of the order of 50C to 60C, before hydrate formation accelerates to block the pipeline.
Although engineering softwares exist to estimate pipeline process conditions and also generate a P-T hydrate curve, the following tutorial provides a guidance summary to estimate the expected pipeline temperature profile and the associated hydrate formation temperatures.
Influence of input power in Ar/H2 thermal plasma with silicon powder by numer...TELKOMNIKA JOURNAL
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Energy Audit and Heat Recovery on the Rotary Kiln of the Cement Plant in Ethi...IJAEMSJORNAL
This study deals with the energy audit and heat recovery on the rotary kiln taking a cement factory in Ethiopia as a case study.The system is a dry type rotary kiln equipped with a five stage cyclone type preheater, pre-calciner and grate cooler. The kiln has a capacity of 2,000 tons/day.Mass and energy balance has been performed for energy auditing. The energy lost from the kiln shell is about 4.3 MW. By using secondary shell on the rotary kiln about 3.5MW could be recovered safely.This energy saving reduces fuel consumption (almost 9%) of the kiln system, and increases the overall system efficiency by approximately 2–3%.
Energy balance of Diesel Production plant in refinery. Calculation of make up hydrogen requirement in the reactor. Calculation of Steam requirement in fractionator for distillation.
Chemical Process Calculations – Short TutorialVijay Sarathy
Often engineers are tasked with communicating equipment specifications with suppliers, where process data needs to be exchanged for engineering quotations & orders. Any dearth of data would need to be computed for which process related queries are sometimes sent back to the process engineer’s desk for the requested data.
The following tutorial is a refresher for non-process engineers such as project engineers, Piping, Instrumentation, Static & Rotating Equipment engineers to conduct basic process calculations related to estimation of mass %, volume %, mass flow, actual & standard volumetric flow, gas density, parts per million (ppm) by weight & by volume.
Cooling Towers in Process Industries are part of Utilities design. As the name suggests their primary purpose is to provide cooling requirements to industrial hot water from unit operations & unit processes. Examples include chillers and air conditioners. The principle of operation is to circulate hot water through a tower and allow heat dissipation to the ambient. Cooling towers can operate by natural draft or forced draft methods wherein fans are used to increase heat transfer.
Empirical Approach to Hydrate Formation in Natural Gas PipelinesVijay Sarathy
Natural Gas Pipelines often suffer from production losses due to hydrate plugging. For an effective hydrate plug to form, factors can vary from pipeline operating pressure and temperature, presence of water below its dew point, extreme winter conditions & Joule Thomson cooling. In the event hydrates form in the pipeline section, their consequence depends on how well the hydrates agglomerate to grow and form a column. If the pipeline section temperature is only at par with the hydrate formation temperature, the particles do no agglomerate; instead they have to cross the metastable region which is of the order of 50C to 60C, before hydrate formation accelerates to block the pipeline.
Although engineering softwares exist to estimate pipeline process conditions and also generate a P-T hydrate curve, the following tutorial provides a guidance summary to estimate the expected pipeline temperature profile and the associated hydrate formation temperatures.
Influence of input power in Ar/H2 thermal plasma with silicon powder by numer...TELKOMNIKA JOURNAL
Numerical simulation in inductively coupled thermal plasma was made on the temperature distribution in argon (Ar)+hydrogen (H2) induction thermal plasma torch with silicon (Si) powder injection to obtain the temperature distribution and gas flow fields. The ICTP model was used in this research because it has benefit of good repeatability and no contamination process. Interactions between ICTP and injected powder are very complicated to be understood only by related experiments. Influence of input power in ICTP was numerically investigated on thermal plasma temperature fields and powder evaporation. The temperature distributions of thermal plasma and Si vapor distribution were compared at input powers of 20 kW, 30 kW, and 40 kW. Results indicated that higher input power increases the temperature of the thermal plasma with doughnut shape but it slightly enhances evaporation of the powder at the center axis of the plasma torch.
Energy Audit and Heat Recovery on the Rotary Kiln of the Cement Plant in Ethi...IJAEMSJORNAL
This study deals with the energy audit and heat recovery on the rotary kiln taking a cement factory in Ethiopia as a case study.The system is a dry type rotary kiln equipped with a five stage cyclone type preheater, pre-calciner and grate cooler. The kiln has a capacity of 2,000 tons/day.Mass and energy balance has been performed for energy auditing. The energy lost from the kiln shell is about 4.3 MW. By using secondary shell on the rotary kiln about 3.5MW could be recovered safely.This energy saving reduces fuel consumption (almost 9%) of the kiln system, and increases the overall system efficiency by approximately 2–3%.
Energy balance of Diesel Production plant in refinery. Calculation of make up hydrogen requirement in the reactor. Calculation of Steam requirement in fractionator for distillation.
Selection of amine solvents for CO2 capture from natural gas power plant - presentation by Jiafei Zhang in the Natural Gas CCS session at the UKCCSRC Cardiff Biannual Meeting, 10-11 September 2014
Selection of amine solvents for CO2 capture from natural gas power plant - presentation by Jiafei Zhang of Imperial College London at the UKCCSRC Natural Gas CCS Network Meeting at GHGT-12, Austin, Texas, October 2014
This presentation speaks about competitive adsorption of CO2 and CH4 on coal. However, this project may be extended to any porous media on which competitive adsorption is to be observed.
Constructing regression models using forward selection, backward elimination, and stepwise regression I found the best model that explains the variation in miles per gallon that is predictable from other car characteristics from the dataset mtcars in R
Similar to IA data based, boiling point estimation using molecular model vs carbon fraction with molecular weight model (20)
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
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Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
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Embracing GenAI - A Strategic ImperativePeter Windle
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IA data based, boiling point estimation using molecular model vs carbon fraction with molecular weight model
1. Which model, molecular weight or molecular weight with carbon fraction model, a better
predictor for b/p.
Independent variable
Dependent
variable
Data from CRC. Click here data
IA secondary data based – Regression analysis for boiling point estimation
Boiling point model = 49.5(carbon fractions)0.2791 + (molecular weight)0.5039 - 273
This model is based on density function theory and polarized continuum model
done by researcher. Click here to more info
Research Question
Use 5 - 25 carbon chains for regression model
Use regression eqn to estimate the b/p for carbon length of 10, 26, 30.
Find the % error using expt values with predicted values.
Using molecular weight model 3rd order as predictor for b/p.
Using carbon fractions with molecular weight model as predictor for b/p.
Molecular
formula
Carbon
fraction
Molecular
weight
Boiling point
CH4 0.2 16 -161.5
C2H6 0.25 30 -89.42
C3H8 0.272 44.1 -42
C4H10 0.285 58.12 -1
C5H12 0.294 72.15 36
C6H14 0.3 86.18 69
C7H16 0.304 100.21 98.4
C8H18 0.307 114.23 114.23
C9H20 0.31 128.2 128.2
C10H22 0.3125 142.28 142.28
Data PubChem. Click here Data PubChem. Click here Data PubChem. Click here
Boiling point model = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
Molecular
formula
Number
carbon
Molecular
weight
Carbon
fraction
Boiling
point
CH4 1 16 0.2 -161.5
CH4 = carbon fraction =
1 𝑐𝑎𝑟𝑏𝑜𝑛
1 𝑐𝑎𝑟𝑏𝑜𝑛+4 ℎ𝑦𝑑𝑟𝑜𝑔𝑒𝑛
=
1
5
= 0.2
Is molecular weight, carbon fraction a good predictor
2. Class Functional group/name Examples
alkene C = C Alkenyl ethene
alkyne C ≡ C Alkynyl ethyne
alcohol OH Hydroxyl ethanol
ether C – O - C Ether methoxymethane
ketone O
‖
C – C - C
Carbonyl propanone
aldehyde CHO Aldehyde ethanal
Carboxylic
acid
COOH Carboxyl ethanoic acid
ester O
‖
C – O -R
Ester ethyl ethanoate
amide O
‖
C – NH2
Amide propanamide
amine NH2 Amine ethanamine
nitrile C ≡ N Nitrile propanenitrile
Class Functional gp Suffix Example Formula
Alkane C - C - ane ethane CnH2n+2
Homologous Series
carbon IUPAC
name
Structure formula Molecular
formula
Boiling
point
1 Methane CH4 CH4 Gas
2 Ethane CH3CH3 C2H6 Gas
3 Propane CH3CH2CH3 C3H8 Gas
4 Butane CH3(CH2)2CH3 C4H10 Gas
5 Pentane CH3(CH2)3CH3 C5H12 Liquid
6 Hexane CH3(CH2)4CH3 C6H14 Liquid
Physical properties
• Increase RMM / molecular size
•RMM increase ↑ - Van Der Waals forces stronger ↑
↓
Melting /boiling point increases ↑
(Increasing polarisability ↑)
London dispersion forces/temporary dipole ↑
1 2 3 4 5 6 7 8 9 10
number carbons – RMM ↑
150
100
50
0
-50
-100
-150
-200
b/p
increase ↑
boiling point
room temp
gas
liquid
Homologous Series
number
Carbons / RMM ↑
1 2 3 4 5 6 7 8 9 10
boiling point
boiling point increase with increase carbon atoms
alcohol
alkane
alkene
alkyne
London dispersion force
(temporary dipole)
H2 bonding
carboxylic acid > alkane/alkene/alkyne
alcohol
carboxylic acid
3. Number
carbon
Molecular
weight b/p
predicted b/p
using model
1
2
3
4
5 72.15 36
6 86.18 69
7 100.21 98.4
8 114.23 125.6
9 128.2 151
10 142.28 174.1
11 156.31 196
12 170.33 216.2
13 184.37 234
14 198.39 253.6
15 212.42 270.6
16 226.41 286.9
17 240.4 302
18 254.5 317
19 268.5 330
20 282.5 343
21 296.6 363
22 310.6 368
23 324.6 379
24 338.6 391
25 352.7 403
IA secondary data based –Regression analysis for boiling point estimation
Research Question
Use 5 - 25 carbon chains for regression model
Use regression eqn to estimate the b/p for carbon length of 10, 26, 30
Find the % error using expt values with predicted values.
Using molecular weight model 3rd order as predictor for b/p.
Using carbon fraction with molecular weight model, as predictor for b/p.
Molecular weight 3rd order as predictor for b/p
Number
carbon
Molecular
weight
Carbon
fraction b/p
predicted b/p
using model
1
2
3
4
5 72.15 0.294 36
6 86.18 0.3 69
7 100.21 0.304 98.4
8 114.23 0.307 125.6
9 128.2 0.31 151
10 142.28 0.3125 174.1
11 156.31 0.314 196
12 170.33 0.315 216.2
13 184.37 0.317 234
14 198.39 0.318 253.6
15 212.42 0.319 270.6
16 226.41 0.32 286.9
17 240.4 0.3207 302
18 254.5 0.3214 317
19 268.5 0.322 330
20 282.5 0.3225 343
21 296.6 0.323 363
22 310.6 0.3235 368
23 324.6 0.3239 379
24 338.6 0.3243 391
25 352.7 0.3246 403
Carbon fraction and molecular weight as predictor for b/p
Independent
variable
Independent variable Dependent
variable
Dependent
variable
4. Predicted b/p for carbon 10 – MW of 142.3
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(142.3)3 – 0.0064(142.3)2 + 3.0855(142.3) – 153.76 = 173
Research Question
Which model, molecular weight model, 3rd order, better predictor for b/p.
Which model, molecular weight and carbon fraction model, better predictor for b/p.
y = 6E-06x3 - 0.0064x2 + 3.0855x - 153.76
R² = 0.9998
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400
b/p
molecular weight
molecular weight vs b/p
Predicted b/p carbon 10 – using model CF – 0.3125, MW – 142.28
y = 49.5(carbon fractions)0.2791 x (molecular weight)0.5039 - 273
b/p=49.5(0.3125)0.2791 x (142.28)0.5039 -273 = 162
Predicted b/p for carbon 26 – MW of 366.7
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(366.7)3 – 0.0064(366.7)2 + 3.0855(366.7) – 153.76 = 413
Predicted b/p for carbon 30 – MW of 422.8
3rd order fit, y = 0.000006x3 – 0.0064x2 + 3.0855x – 153.76
b/p=0.000006(422.8)3 – 0.0064(422.8)2 + 3.0855(422.8) – 153.76 = 460
Molecular weight 3rd order as estimator for b/p
Molecular weight and carbon fraction as predictor for b/p
Boiling pt model = 49.5(carbon fractions)0.2791 x (molecular weight)0.5039 -273
This model is based on density function theory and polarized continuum
model done by researcher. Click here to more info
Number
carbon
Molecular
weight
Carbon
fraction b/p
predicted b/p
using model
10 142.28 0.3125 174.1 162
26 366.7 0.325 412 435
30 422.8 0.326 449.7 489
Predicted b/p carbon 26 – using model CF – 0.325, MW – 366.7
y = 49.5(carbon fractions)0.2791 x (molecular weight)0.5039 -273
b/p=49.5(0.325)0.2791 x (366.7)0.5039 - 273 = 435
Predicted b/p carbon 30 – using model CF – 0.326, MW – 422.8
y = 49.5(carbon fractions)0.2791 x (molecular weight)0.5039 -273
b/p=49.5(0.326)0.2791 x (422.8)0.5039 -273 = 489
6. Molecular weight and carbon fraction model is a weaker fit
Molecular weight model is a better fit
% error, smaller compared to the other model
% error increases as molecular weight increases
3rd order fit – % error changes from 0.5% to 0.2% to 2% as
carbon chain changes from 10 to 26 to 30
y = 6E-06x3 - 0.0064x2 + 3.0855x - 153.76
R² = 0.9998
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400
b/p
molecular weight
molecular weight vs b/p
Number
carbon
Molecular
weight b/p
predicted
poly fit
3rd order
(% error)
10 142.28 174.1 173 (0.5%)
26 366.7 412 413 (0.2%)
30 422.8 449.7 460 (2%)
% error, bigger compared to molecular weight model.
% error increases as carbon chains increases
% error changes from 7% to 5.5% to 8.7% as
carbon chain changes from 10 to 26 to 30
Research Question
Which model, molecular weight model, 3rd order, better predictor for b/p.
Which model, molecular weight and carbon fraction model, better predictor for b/p.
Boiling pt model = 49.5(carbon fractions)0.2791 x (molecular weight)0.5039 -273
This model is based on density function theory and polarized continuum
model done by researcher. Click here to more info
Number
carbon
Molecular
weight
Carbon
fraction b/p
predicted
(% error)
10 14228 0.3125 174.1 162 (7%)
26 366.7 0.325 412 435 (5.5%)
30 422.8 0.326 449.7 489 (8.7%)