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Abstract

Flash point plays a Important Role in Industrial Chemical process. it is Necessary that Consuming a Batch Process in large scale. Theoretical Approach plays a Crucial role that to Estimate the behaviour of Minimum Flash point. That to determine the fire and explosion hazards of a liquid. Accurate estimation of flash point is essential for assessing fires and explosion hazards, particularly in large-scale batch operations. The identification of this behaviour is critical, because a hazardous situation results from taking the lowest component flash point value as the mixture flash point. an estimate of the mixture flash point is needed. A procedure to estimate the flash point of binary mixtures is discussed. This work presents a procedure for estimating the flash point of two or more components in a binary liquid mixture by applying Le Chatelier’s mixing rule.

Keywords

Ignition, Explosion, Binary Flash, Ideal

Introduction

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Theoretical estimation of flash points has long been a subject of interest in chemical engineering and safety science, as it provides a predictive framework for assessing flammability without reliance on direct measurement [2]. Early theoretical approaches were based on empirical correlations linking flash points to fundamental properties such as boiling point, molecular weight, and vapor pressure. These models, while limited in scope, established the foundation for predictive estimation in hydrocarbon systems [1]. The flash point is the temperature at which the vapor pressure of a liquid (or mixture) produces enough vapor to reach the Lower Flammable Limit (LFL) in air [9]. The vapor pressure depends directly on the concentration of each component in the liquid phase. Estimation of Minimum Flash Point Behaviour. Some mixtures exhibit a flash point lower than either pure component. This occurs because the vapor composition at certain concentrations produces a more flammable mixture than expected. Identifying this behaviour is critical for safety [7]. In This Discussion some Mixtures are having with Examples Methanol–Water Mixture   Pure methanol has a flash point of nearly 11?°C. Adding water increases the flash point because water dilutes methanol vapor concentration. Same as with Octane–Ethanol Mixture Pure octane has a higher flash point (~-4?°C). Adding ethanol lowers the flash point at certain concentrations due to synergistic vapor composition effects [5]. The determination of flash points in liquid mixtures is a critical aspect of chemical safety, process design, and environmental protection [11]. While ideal solutions can often be described using simplified thermodynamic models, most real world mixtures exhibit non ideal behaviour due to molecular interactions, differences in polarity, and deviations from Raoult’s law. These complexities make the prediction of flash points in non ideal systems both challenging and essential. To approach this activity, we use the Van Laar equation By Approaching this theoretical Methods we can easily predictable in Large scale industrial Areas with out any Instrumentation or Experimental Procedures [2]. This paper is more useful for industrial scale without any cost proposals / cost consumption in Chemical manufacturing Fields. The introduction of activity coefficient models—such as Van Laar, Wilson, NRTL, and UNIQUAC—marked a significant advance, enabling predictions that accounted for non ideal solution behaviour and molecular interactions. In recent decades, computational methods and machine learning have further expanded predictive capabilities, allowing estimation for complex mixtures, biofuels, and hazardous solvents without exhaustive experimentation [6].

II. Theoretical Background:

 

The estimation of flash points through theoretical methods relies on fundamental thermodynamic principles and mixture rules that describe vapor–liquid equilibrium and flammability limits [4]. Unlike experimental determination, these approaches provide predictive capability for complex mixtures, particularly those exhibiting non?ideal behaviour [1].

2.1 Raoult’s Law (Ideal Solutions)

For an ideal liquid mixture, the partial vapor pressure of each component is proportional to its mole fraction in the liquid phase and its pure component vapor pressure:

Pi=xi Pi sat(T)

where:

Pi = partial vapor pressure of component i

xi = mole fraction of component i in the liquid phase

Pi sat(T) = saturation vapor pressure of pure component i at temperature T

2.2 Activity Coefficient Models (Non?Ideal Solutions)

Real mixtures often deviate from Raoult’s law due to molecular interactions. Activity coefficients (γi) are introduced to account for non?ideality:

Pi=xi γi Pi sat(T)

2.3 Le Chatelier’s Mixing Rule (Flammability Limit)

The flash point corresponds to the temperature at which the vapor composition reaches the lower flammable limit (LFL). For mixtures, the effective LFL can be estimated using Le Chatelier.

III. Methodology

The estimation of flash point for binary mixtures requires combining thermodynamic principles with flammability rules. In this study, the methodology integrates

Step by Step approaches are Consider as

 

3.1 Overview:

•Mole fraction Determination: To quantify mixture composition

•Le Chatelier’s mixing Rule: To estimate effective flammability limits.

 

•Antoine equation: To calculate vapor pressures

 

•Raoult’s law: To determine total vapor pressure of the mixture

3.2 Calculation:

Applying Le-Chatelier’s Rule:

Example -1:  Acetone + Methanol:

Data consideration For This Calculation for Acetone =1850 Litres, Methanol= 930 Litres.

Molecular Weight of Acetone = 58.08 g/mol

Molecular Weight of Methanol = 32.04 g/mol

Le Chatelier Law:                                                  1/Total Flash Point (mix) =∑ (xi/Total flash Point)

•Convert Acetone in to Kg’s = 1850 L × 0.785 Kg/m3

Convert Acetone in to Kg’s = 1452.25 Kg

• Convert Methanol in to Kg’s = 930 L × 0.791 Kg/m3

Convert Methanol in to Kg’s = 735.63 Kg

Step-1 Finding No, of Moles in Acetone:

n(A) = WeightMol.Wweight

 

n(A) =  1452250 g58.08 g/mol

n(A) = 25004.30 mole

Finding No, of Moles in Methanol:

n(B) = WeightMol.Wweight

n(B) =  735630 g32.04 g/mol

n(B) = 22959.73 mole

Step-2 Finding Mole Fraction:

Mole Fraction of Acetone:

x(A) = nAnA+nB

x(A) = 25004.30 mole 25004.30 mole+22959.73

x(A) = 0.521%

 

Mole Fraction of Methanol:

x(B) = nBnA+nB

x(B) = 22959.73 mole 25004.30 mole+22959.73

x(B) = 0.478%

 

Now Applying Le-Chatelier Rule:

1Total Flash point   = Mole fraction of Acetone Flash point  of Acetone  + Mole fraction of Methanol Flash point of Methanol

 

 1Total Flash point  = 0.521(273+(-20°C)  + 0.478 (278+13°C)

 

1Total Flash point  = 0.521253 K+ 0.478 284 K

1Total Flash point  = 0.0020592+ 0.0016830

                       = 0.0037422 K-1

                        = 10.0037422

                       = 267.222-273

Total Flash Point = -5.77 °C

After Successfully Reaching the Estimation of Flash point we Have another Important Calculation for Estimation of Vapour Pressure of the Mixture By using Antonie Equation and Applying the Raoult’s Law to reach this Approach

The Antoine equation Calculation for Acetone:

A= 7.0244; B= 1161; C= 224

The Antoine equation Calculation for methanol:

A= 8.08097; B= 1582.271; C= 239.726

Vapour Pressure of Acetone:

Log P = A- BT+C

Log10 P = 7.0244 - 1161-5.77°c+224

 

Log10 P = 7.0244 - 1161218.3

Log10 P = 7.0244 – 5.318

Log10 P = 1.706

Vapour pressure = 10^1.706

Vapour pressure = 50.81 mmHg × 0.1333

Vapour pressure = 6.77 Kpa

Vapour Pressure of Methanol:

Log P = A- BT+C

Log10 P = 8.08097 - 1582.271-5.77°c+239.726

Log10 P = 8.0897 - 1582.271233.956

Log10 P = 8.08097 -6.7631

Vapour Pressure = 10^1.326

Log10 P = 21.212 mmHg × 0.1333

Vapour Pressure = 2.8276 Kpa

Now Applying the Raoults Law:

P Total  = X Methanol . P0 Methanol + X Acetone . P0 Acetone

P Total = 0.478 .2.8276 + 0.521 .6.77

P Total = 4.872 Kpa

P Total = 4.8720.1333

P Total = 36.542 mmHg × 0.00135

P Total = 0.0493 Kg/cm2 of Vapour Pressure at                   -5.77°C in mixture flash point.

Example-2: Acetone + Water:

Data consideration For This Calculation for Acetone =1250 Litres, Water= 520 Litres.

Molecular Weight of Acetone = 58.08 g/mol

Molecular Weight of Water = 18 g/mol

Le Chatelier Law:                                                  1/Total Flash Point (mix) =∑ (xi/Total flash Point)

•Convert Acetone in to Kg’s = 1250 L × 0.785 Kg/m3

Convert Acetone in to Kg’s = 981.25 Kg

• Convert Water in to Kg’s = 520 L × 1.0 Kg/m3

Convert Water in to Kg’s = 520 Kg

•Step-1 Finding No, of Moles in Acetone:

n(A) = WeightMol.Wweight

n(A) =  1250000  g58.08 g/mol

n(A) = 21522.038 mole

• Finding No, of Moles in Water:

n(B) = WeightMol.Wweight

n(B) =  520000 g18 g/mol

n(B) = 28888.88 mole

•Step-2 Finding Mole Fraction:

Mole Fraction of Acetone:

x(A) = nAnA+nB

x(A) = 21522.038 mole 21522.038 mole+28888.88

x(A) = 0.42%

Mole Fraction of Water:

x(B) = nBnA+nB

x(B) = 28888.88 mole 21522.038 mole+28888.88

x(B) = 0.57%

Now Applying Le-Chatelier Rule:

1Total Flash point   = Mole fraction of Acetone Flash point  of Acetone  + Mole fraction of WAterFlash point of WAter

 1Total Flash point  = 0.42(273+(-20°C)  + 0.57 (273+0°C)

 

1Total Flash point  = 0.42253 K+ 0.57 273 K

1Total Flash point  = 0.001660+ 0.00208

                       = 0.00374 K-1

                        = 10.0037422

                       = 267.379-273

Total Flash Point = -5.62 °C

Now Applying the Raoult’s Law to reach this approach

The Antoine equation Calculation for Acetone:

A= 7.0244; B= 1161; C= 224

The Antoine equation Calculation for Water:

A= 8.07131; B= 1730.63; C= 233.426

Vapour Pressure of Acetone:

Log P = A- BT+C

Log10 P = 7.0244 - 1161-5.62°c+224

Log10 P = 7.0244 - 1161218.3

Log10 P = 7.0244 – 5.316

Log10 P = 1.707

Vapour pressure = 10^1.706

Vapour pressure = 51.04 mmHg × 0.1333

Vapour pressure = 6.80 Kpa

Vapour Pressure of Water:

Log P = A- BT+C

Log10 P = 8.0713 - 1730.63-5.77°c+233.42

Log10 P = 8.0713 - 1730.63233.956

Log10 P = 8.0713 -6.7631

Vapour Pressure = 10^1.3082

Vapour Pressure = 20.332 mmHg × 0.1333

Vapour Pressure = 2.7103 Kpa

Now Applying the Raoults Law:

P Total = X Acetone . P0 Acetone + X Water  . P0 Water

P Total = 0.42 .6.80 + 0.57 .2.7103

P Total = 4.400 Kpa

P Total = 4.4000.1333

P Total = 33.014 mmHg × 0.00135

P Total = 0.0488 Kg/cm2 of Vapour Pressure at                   -5.77°C in mixture flash point.

Example-3: Isopropyl Alcohol + Water:

Data consideration For This Calculation for IPA=1000 Litres, Water= 890 Litres.

Molecular Weight of IPA = 60.10 g/mol

Molecular Weight of Water = 18 g/mol

Le Chatelier Law:

                                                  1/Total Flash Point (mix) =∑ (xi/Total flash Point)

•Convert IPA in to Kg’s = 1000 L × 0.785 Kg/m3

Convert IPA in to Kg’s = 785 Kg

• Convert Water in to Kg’s = 890 L × 1.0 Kg/m3

Convert Water in to Kg’s = 890 Kg

•Step-1 Finding No, of Moles in IPA:

n(A) = WeightMol.Wweight

n(A) =  1000000  g60.10 g/mol

n(A) = 16638.93 mole

• Finding No, of Moles in Water:

n(B) = WeightMol.Wweight

n(B) =  890000 g18 g/mol

n(B) = 49444.44 mole

•Step-2 Finding Mole Fraction:

Mole Fraction of IPA:

x(A) = nAnA+nB

x(A) = 16638.93 mole 16638.93 mole+49444.44

x(A) = 0.25%

 

Mole Fraction of Water:

x(B) = nBnA+nB

x(B) = 49444.44 mole 16638.93 mole+49444.44

x(B) = 0.74%

Now Applying Le-Chatelier Rule:

1Total Flash point   = Mole fraction of IPA Flash point  of IPA  + Mole fraction of Water Flash point of Water

 

 1Total Flash point  = 0.25(273+(12°C)  + 0.74(273+0°C)

 

1Total Flash point  = 0.25285 K+ 0.74 273 K

1Total Flash point  = 0.000877+ 0.00271

                       = 0.00358 K-1

                        = 10.003587

                       = 278.78-273

Total Flash Point = 5.78 °C

Now Applying the Raoult’s Law to reach this approach

The Antoine equation Calculation for IPA:

A= 6.86618; B= 1360.13; C= 197.592

The Antoine equation Calculation for Water:

A= 8.07131; B= 1730.63; C= 233.426

Vapour Pressure of IPA:

Log P = A- BT+C

Log10 P = 6.86618 - 1360.135.78°c+197.592

Log10 P = 6.86618 - 1360.13203.372

Log10 P = 6.86618 – 6.6878

Log10 P = 0.1783

Vapour pressure = 10^0.1783

Vapour pressure = 1.5076 mmHg × 0.1333

Vapour pressure = 0.2009 Kpa

Vapour Pressure of Water:

Log P = A- BT+C

Log10 P = 8.0713 - 1730.635.78c+233.42

Log10 P = 8.0713 - 1730.63239.2

Log10 P = 8.0713 -7.2350

Vapour Pressure = 10^0.8363

Vapour Pressure = 6.8596 mmHg × 0.1333

Vapour Pressure = 0.9145 Kpa

Now Applying the Raoults Law:

P Total = X IPA . P0 IPA + X Water  . P0 Water

P Total = 0.25 .0.2009 + 0.74 .0.9145

P Total = 0.7269 Kpa

P Total = 0.72690.1333

P Total = 5.4518 mmHg × 0.00135

P Total = 0.00736 Kg/cm2 of Vapour Pressure at                   5.78°C in mixture flash point.

IV. RESULTS & DISCUSSION

The theoretical estimation of flash points for binary mixtures was carried out using Le Chatelier’s mixing rule, Raoult’s law, and the Antoine equation. The calculated flash points, vapor pressures, and mole fractions are summarized

 

Table 1. calculated Flash Point vs Total Vapour Pressure vs Mole fraction

Mixture

Calculated Flash Point

Total Vapour Pressure

Mole Fraction

Acetone + Methanol

-5.77°C

0.0493 Kg/cm2

0.52, 0.47%

Acetone + Water

-5.62°C

0.0488 Kg/cm2

0.42%, 0.57%

IPA + Water

5.78°C

0.0073 Kg/cm2

0.25%, 0.74%

 

Table.2: Effect of Water in Component Flash Vs Mixture Flash

Solvent

Component Flash Point

Mixture Flash Point (°C)

Effect of water

Acetone

-20°C

-5.62°C

Raise in Flash Point

IPA

12°C

5.78°C

Raise in Flash Point

Methanol

11°C

-

-

 

Table.3: Theoretical Observation in Components in Mixture:

Mixture

Observed trend

Theoretical Observation

Acetone + Methanol

Lowest flash point (-5.77 °C)

More flammable than either pure component alone.

Acetone + Water

Flash point raised

Water dilutes acetone vapor concentration

IPA + Water

Flash point raised significantly

Strong hydrogen bonding suppresses vaporization

 

4.1 Flash Point Behaviour

The acetone–methanol mixture exhibited the lowest flash point (-5.77 °C), indicating a synergistic effect between the two solvents. This result highlights that mixtures can sometimes be more flammable than either pure component, a critical consideration for industrial safety [1]. In contrast, the IPA–water mixture showed a significantly higher flash point (5.78 °C), consistent with the diluting effect of water on flammable vapor concentration [10]. The rise in flash point for the acetone–water mixture reflects the role of water in diminishing vapor-phase flammability through dilution and molecular interactions [6]

4.2 Vapor Pressure effect

The total vapor pressures calculated for acetone–methanol and acetone–water mixtures were similar (~0.049 kg/cm²), whereas the IPA–water mixture exhibited a much lower vapor pressure (0.0073 kg/cm²). This reduction reflects the strong hydrogen bonding interactions between water and IPA, which suppress vaporization and increase the flash point. These findings confirm that molecular interactions significantly influence nonideal behaviour, deviating from predictions based solely on Raoult’s law.

4.3 Theoretical Validation

The application of Le Chatelier’s mixing rule successfully predicted effective flammability limits for all mixtures studied. The Antoine equation provided reliable vapor pressure estimates, which, when combined with Raoult’s law, yielded consistent results. The observed deviations in flash point behaviour underscore the importance of incorporating activity coefficient models for nonideal systems, particularly in mixtures involving polar solvents such as alcohols and water.

4.4 Industrial Impact

Process safety perspective, the results emphasize that relying on the lowest component flash point is insufficient for hazard assessment. Mixtures such as acetone–methanol can exhibit unexpectedly low flash points, increasing fire and explosion risks in large-scale batch operations. Conversely, water-containing mixtures demonstrate elevated flash points, offering potential safety advantages in industrial formulations. Theoretical estimation methods thus provide a cost-effective and rapid approach to predicting flash points, reducing reliance on experimental determination while ensuring compliance with safety standards.

4.5 FUTURE SCOPE

The present study demonstrates the utility of theoretical models for binary mixtures, further refinement using advanced activity coefficient models (e.g., NRTL, UNIQUAC) and computational methods is recommended. Extending this methodology to ternary and multicomponent mixtures will enhance predictive accuracy for complex industrial systems, including biofuels and pharmaceutical solvents.

 

Fig.1 Explained that the Comparison Between Acetone+ Methanol used quantity, Mole Fraction of both Solvents and Final Flash Point Temperature.

Fig.2 Explained that the Effect of Water in Solvent

 

Water has a very high flash point (close to 100 °C) and low volatility compared to organic solvents. When mixed with solvents like acetone, methanol, or isopropyl alcohol, water reduces the partial vapor pressure of the flammable component [8]. This dilution effect means fewer flammable molecules enter the vapor phase, raising the overall flash point of the mixture. Water forms strong hydrogen bonds with polar solvents (e.g., alcohols). These interactions reduce the tendency of solvent molecules to escape into the vapor phase. as a result, mixtures such as IPA–water show significantly higher flash points than pure IPA. Water addition generally makes mixtures safer by raising flash points and lowering vapor pressures. In industrial practice, water is sometimes deliberately added to reduce fire hazards in solvent systems [7]. However, the effect depends on concentration: small amounts of water may not significantly change flammability, while higher proportions can strongly suppress ignition risk.

REFERENCES

  1. Liaw, H.-J., & Lin, S.-C. (2007). Binary mixtures exhibiting maximum flash-point behaviour. Journal of Hazardous Materials, 140(1–2), 155–164.
  2. Jiang, J., et al. (2015). Flash points measurements and prediction for binary miscible mixtures. Journal of Loss Prevention in the Process Industries, 34, 56–64.
  3. Chen, C.-C., et al. (2017). Calculation and prediction of binary mixture flash point using correlative and predictive local composition models. Fluid Phase Equilibria, 440, 95–102.
  4. Prausnitz, J. M., Lichtenthaler, R. N., & de Azevedo, E. G. (1999). Molecular Thermodynamics of Fluid-Phase Equilibria (3rd ed.). Prentice Hall.
  5. Lakzian, K., Liaw, H.-J., Lakzian, E., & Gerbaud, V. (2025). A comprehensive review on flash point behavior of binary ignitable mixtures: Trends, influencing factors, safety and fuel design implications, and future directions. Progress in Energy and Combustion Science.
  6. ScienceDirect. (2025). Evaluation criteria for predicting flash point behavior of binary miscible mixtures using extreme flash point behavior indicators for fuel and safety applications. Fuel, 397, 135444.
  7. Fuel Journal (2025). Evaluation criteria for predicting flash point behavior of binary miscible mixtures using extreme flash point behavior indicators for fuel and safety applications. Fuel, Volume 397, Article 135444.
  8. Liaw, H.-J., & Tang, Q.-R. (2025). Flash point prediction of binary mixtures of ionic liquid and flammable solvent. Journal of Loss Prevention in the Process Industries, 96, 105631.
  9. Process Safety and Environmental Protection (2023). Flash point of binary and ternary monoterpene mixtures: Experimental and modelling. Process Safety and Environmental Protection, Volume 172, April 2023, Pages 1048–1057.
  10. Fluid Phase Equilibria (2024, early online). Flash point of binary and ternary mixture of biojet blends: Experimental data and modelling
  11. Vilas-Boas, S. M., et al. (2023). Flash point of binary and ternary monoterpene mixtures: Experimental and modelling. Process Safety and Environmental Protection, 172, 1048–105

Reference

  1. Liaw, H.-J., & Lin, S.-C. (2007). Binary mixtures exhibiting maximum flash-point behaviour. Journal of Hazardous Materials, 140(1–2), 155–164.
  2. Jiang, J., et al. (2015). Flash points measurements and prediction for binary miscible mixtures. Journal of Loss Prevention in the Process Industries, 34, 56–64.
  3. Chen, C.-C., et al. (2017). Calculation and prediction of binary mixture flash point using correlative and predictive local composition models. Fluid Phase Equilibria, 440, 95–102.
  4. Prausnitz, J. M., Lichtenthaler, R. N., & de Azevedo, E. G. (1999). Molecular Thermodynamics of Fluid-Phase Equilibria (3rd ed.). Prentice Hall.
  5. Lakzian, K., Liaw, H.-J., Lakzian, E., & Gerbaud, V. (2025). A comprehensive review on flash point behavior of binary ignitable mixtures: Trends, influencing factors, safety and fuel design implications, and future directions. Progress in Energy and Combustion Science.
  6. ScienceDirect. (2025). Evaluation criteria for predicting flash point behavior of binary miscible mixtures using extreme flash point behavior indicators for fuel and safety applications. Fuel, 397, 135444.
  7. Fuel Journal (2025). Evaluation criteria for predicting flash point behavior of binary miscible mixtures using extreme flash point behavior indicators for fuel and safety applications. Fuel, Volume 397, Article 135444.
  8. Liaw, H.-J., & Tang, Q.-R. (2025). Flash point prediction of binary mixtures of ionic liquid and flammable solvent. Journal of Loss Prevention in the Process Industries, 96, 105631.
  9. Process Safety and Environmental Protection (2023). Flash point of binary and ternary monoterpene mixtures: Experimental and modelling. Process Safety and Environmental Protection, Volume 172, April 2023, Pages 1048–1057.
  10. Fluid Phase Equilibria (2024, early online). Flash point of binary and ternary mixture of biojet blends: Experimental data and modelling
  11. Vilas-Boas, S. M., et al. (2023). Flash point of binary and ternary monoterpene mixtures: Experimental and modelling. Process Safety and Environmental Protection, 172, 1048–105

Photo
Srikanth Reddy Jonnala
Corresponding author

Lee Pharma Limited, Visakhapatnam.

Photo
Patnala Ranjith Kumar
Co-author

Lee Pharma Limited, Visakhapatnam.

Srikanth Reddy Jonnala, Ranjith Kumar Patnala, Theoretical Estimation of Flash Point for Industrial Scale Applications, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 8, 390-398, https://doi.org/10.5281/zenodo.22248261

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