Design and optimization of kemira leonard process for formic acid production using ASPEN

1. Under the guidance of Dr. Upasna Balyan
COMPUTATIONAL METHODS FOR
BIOCHEMICAL ENGINEERS
Design and optimization of Kemira-Leonard
process for formic acid production
Group 2 : Sanjana Singh, Manyata Gupta, Preity Yadav, Abhishek Bhardwaj,
Anubhav Raj

2. Objective of study
Formic acid
Production of Formic acid
Overall reaction
Process Development
Thermodynamic model
Modeling of reactor
Designed KL process
Sizing and Cost estimation
Heat Integration
Multi-objective optimization
Result and conclusion
Conclusion
Reference
TOPICS TO BE COVERED
TOPICS TO BE COVERED

3. INTRODUCTION
INTRODUCTION
FORMIC ACID (FA)
It is used mainly as a preservative and antibacterial agent in livestock feed, and to treat textiles
and leather.
FA is the simplest carboxylic acid, it is also used for the production of several other chemical
products.
There is significant demand globally for FA, totaling 950,000 tonnes/year in 2014, and The
demand for FA is expected to continue increasing, particularly with recent technological
advancements in fuel cells and renewable energy.
OBJECTIVE OF STUDY
To develop and provide a complete design of the Kemira-Leonard (KL)
process for Formic Acid, along with relevant cost estimation, Heat
Integration, and optimization.
KL process will first be simulated in Aspen Plus V9.0, then Heat
Integration will be performed, and effects of design variables on
capital/operating costs will be assessed via sensitivity analysis.
Formic Acid

4. The two main routes of FA synthesis practiced today are:
PRODUCTION OF FORMIC ACID
PRODUCTION OF FORMIC ACID
Methyl formate (MF) hydrolysis
Preparation of free FA from formates
Kemira-Leonard (KL) process.
BASF
USSR processes.
The current market is dominated by the first route, whereby 90% of the
world’s FA is synthesized.
Methyl Formate hydrolysis can be done by 3 process
Among these 3 FA processes, the BASF and KL processes are the most
common.
This study focuses on the production of FA through MF hydrolysis via
the Kemira-Leonard (KL) process.

5. CO + H2O HCOOH
CH3OH + CO HCOOCH3
HCOOCH3 + H2O HCOOH + CH3OH
OVERALL REACTION
OVERALL REACTION
Carbonylation of methanol (methyl alcohol, MA) to form Methyl formate in a continuously stirred
tank reactor (CSTR) in the presence of sodium methoxide catalyst.
Hydrolysis of Methyl formate to obtain Formic acid
The KL process involves two separate hydrolysis reactors, One as a preliminary reactor to first
produce a small amount of FA while the next serves as the main reactor, where the hydrolysis
reaction is catalyzed by the FA produced.

6. Methyl formate - MF
Methyl alcohol - MA
Formic acid - FA
PROCESS DEVELOPMENT
PROCESS DEVELOPMENT
Carbonylation
Product
Bottom-MA
MF
Prim. Hydrolysis
Main Hydrolysis
Less FA Flash column
Vapour product
Liquid product
Acid separation column
FA+Water
MF+MA Recyle column
MF
MA
First product column
FA

7. According to the ‘Methods Assistent in Aspen Plus, activity coefficient models (for example, NRTL, UNIQUAC, or Wilson)
should be used for non-ideal systems like methanol-water and FA-water.
The researcher has used UNIQUAC (Universal Quasichemical) with Hayden O’Connell equation of state for vapor
phase (UNIQHOC) as the fluid package for the simulation in their study on the Reactive Distillation based FA process.
This model copes with the dimerization in the vapor phase of mixtures containing carboxylic acids like FA. Hence, the
present study chose the UNIQ-HOC model for the KL process simulation and optimization.
Henry’s law was used to account for CO dissolution in the liquid phase, which is appropriate at temperatures well above
the compound’s critical temperature.
The binary interaction parameters for the UNIQ-HOC model were obtained via thermodynamic model regression taken
from various literature sources.
Selecting the appropriate fluid package is a crucial step before process simulation in a simulator.
These binary parameters and the Henry parameters used for CO are:
Thermodynamic model
Thermodynamic model

8. CH3OH + CO HCOOCH3
MODELLING OF REACTOR
MODELLING OF REACTOR
Rate equation for Carbonylation reaction
r is the reaction rate in mol/(L.min)
[cat]L is the liquid phase concentration of sodium methoxide catalyst in mol/L
[CH3OH]L is the liquid phase concentration of MA in mol/L
[CO]L is the liquid phase concentration of CO in mol/L
[HCOOCH3]L is the liquid phase concentration of MF in mol/L
T is the temperature in K and R is the gas constant in J/(mol.K)
The reaction kinetics proposed gives both forward and reverse reaction rates as functions of concentrations of reactants,
product and sodium methoxide catalyst in the liquid phase.
Sodium methoxide catalyst is a solid powder, dissolves in methanol to participate in the liquid phase reaction.
This equation is valid for a temperature range of 60 to 110 C and a pressure range of 2 to 4 MPa.
For the design of CSTR for carbonylation is considered to be at a temperature of 102 degrees C, the
pressure of 2.4 MPa, and residence of 0.72 h for the KL process simulation before optimization.

9. HCOOCH3 + H2O HCOOH + CH3OH
The autocatalytic hydrolysis reaction of the preliminary PFR, which occurs when FA
concentration is low (e.g., initially).
Two rate equation for Hydrolysis reaction are used
r is the reaction rate in mol/(kg.min)
T bar is the reference temperature (=368.15 K),
T is the reaction temperature in K,
k bar is the rate constant for the un-catalyzed reaction in kg/(mol.min),
k' bar is the rate constant for the autocatalyzed reaction in kg2 /(mol2 .min),
Ea is the activation energy of the un-catalyzed reaction in kJ/mol,
Ea' is the activation energy for the autocatalyzed reaction in kJ/mol,
R is the Gas constant in kJ/(mol.K),
Kc is the equilibrium constant (dimensionless),
Ca is MF concentration in mol/kg,
Cb is water concentration in mol/kg,
Cc is FA concentration in mol/kg, and Cd is MA concentration in mol/kg

10. The second kinetic expression used for modeling the FA-catalyzed hydrolysis reaction, which
occurs when FA concentration is sufficient, when a minimum FA to MF ratio of 0.05 is satisfied.
k' bar is the reaction rate constant in kg2 /(mol2 .min),
Kd is the acid dissociation constant (mol/kg),
Ea' is the activation energy in kJ/mol and
T bar is the reference temperature (=368.15 K).
The significances and units of r, T, R, Ca, Cb, Cc , Cd and Kc are identical to those in previous equation.
Value of Kd is taken to be 1.8 10-4 mol/kg,
The temperature and pressure of the two PFRs will be 90 to 140 degrees C and 120 degrees C and 5 to 18 atm and 18 atm for
a satisfactory yield of FA.
MF conversion in the preliminary and main reactors are respectively 0.18 and 0.13
The two PFRs were designed to operate adiabatically, which eliminated the need for heating/cooling utility.
The preliminary PFR was designed to be 8.96 m in length and 0.30 m in diameter, whereas the main PFR was determined to
be 20.08 m long with a diameter of 0.67 m.
Both reactors have a length to diameter ratio of 30.

11. The KL process was designed to produce 98 wt% FA at 3,310
kg/h, which translates to a plant capacity of 27,476 t/y. This
capacity was set based on the FA production rate of 27,100 t/y.
Elimination of purge streams in the developed process resulted
in the production of more FA without the loss of any CO
reactant.
Hence, methanol makeup is expected to be very small, as it
should account only for trace quantities of MA and MF lost in
the product stream.
Designed KL process
Designed KL process

12. The module costing method was used to estimate the capital cost of all equipment.
The accuracy range for this methodology is within 25% to 40%.
Total capital cost (TCC) was obtained by taking the sum of the grassroots cost of all equipment in the designed
process.
Chemical Engineering Plant Cost Index (CEPCI) of 600 was assumed for updating capital cost to the present time.
Annual utility cost (AUC) was calculated by using unit utility costs in Table.
Cost of steam contributes to more than 75% of the total utility cost, and therefore the accuracy of the utility cost
calculations depends mainly on the accuracy of the unit price of steam in Table.
Unit operations involving or processing FA were designed using titanium, as Formic Acid can be very corrosive,
particularly at high concentration and elevated conditions.
Material Of Construction (MOC) for all other unit operations were assumed to be carbon steel (CS) due to its lower cost.
Cost estimation procedures comprised of two main parts: Capital and Utility Cost Calculations.
SIZING AND COST ESTIMATION
SIZING AND COST ESTIMATION

13. Heat Integration
Heat Integration
Heat Integration was carried out before optimization to minimize utility consumption.
The Heat Exchanger Network (HEN) arrangement obtained for the preliminary design may or
may not be optimal for alternative designs.
Indeed, by changing some of the design parameters in the process, stream temperatures and
heating/cooling duties may change and consequently the optimal HEN.
Therefore, the HEN arrangement needs to be updated for some trial solutions during the
optimization.
In the simulation of these trial solutions, extra heat exchangers were necessary to achieve the
target temperatures of certain streams. For these cases, they were sized considering cooling and
heating by the utility.
Other cases arose where some heat exchangers were no longer necessary or feasible due to the
temperature approach constraint. Then, the capital cost of such exchangers was taken as zero.

14. MULTI-OBJECTIVE OPTIMIZATION (MOO)
MULTI-OBJECTIVE OPTIMIZATION (MOO)
The aim of the MOO problem is to minimize overall costs associated with the FA process.
TCC and AUC are chosen as the objective functions for the MOO of the KL process.
TCC was calculated assuming undeveloped site for the plant, which meant the calculation of total
grassroots cost.
AUC, on the other hand, considers the annual cost of utilities like steam, cooling water, electricity, and
fuel oil for the plant.
Like in most applications, TCC and AUC are conflicting objectives ie. its difficult to optimize both together.
These optimal solutions provide a quantitative trade-off between the objectives employed, which will be
useful for choosing one of them.
Practical applications require the simultaneous optimization of more than one objective function, as can
be seen in many chemical engineering applications, where multiple and often conflicting objectives are
part of the optimization problem. Multi-Objective Optimization (MOO) is particularly useful in this regard,
allowing for multiple objectives to be optimized simultaneously with respect to decision variables (DVs)
subject to bounds and constraints if any.
Objective function

15. For FA process optimization, DVs were chosen considering the main units (i.e., reactors and distillation columns) and their
design variables that were not fixed based on preliminary analysis stated in the table below.
A sensitivity analysis was conducted to determine the lower and upper bounds of DVs. For this, each DV was varied one at a time
(with all others at the initial design values in Table below), and the corresponding TCC and AUC were recorded for comparison.
Each DV was decreased (or increased) from the initial design value until the process simulation failed to converge or the
converged process has larger capital and utility costs.
This point gave the lower (or upper bound) for each DV. These bounds are also presented in Table.
In this table, T-100/101/102/103/104 Stages refer to the number of theoretical plates in the column, including condenser and
reboiler.
All governing equations for the FA process including product purities are satisfied as part of the process simulation within Aspen
Plus for each set of DV values given by the MOO algorithm.
A minimum temperature approach of 10 degrees C was used for all heat exchangers.
Decision variables & constraints

16. Results and discussion
Results and discussion
The Pareto-Optimal front
To confirm the convergence of the Pareto fronts for
FA process optimization, MOO results are plotted at
intervals of 10 generations. Each Pareto front shows
the set of all non dominated solutions after a certain
number of generations shows that the Pareto fronts
had converged by the 50th generation, showing the
sufficiency of MNG of 80 generations for KL process
optimization.
No clear trend was observed for the following DVs
and their optimal ranges are stated within brackets:
T-100 number of stages (16 or 17), T-102 feed stage
fraction (0.64 to 0.89), T-103 feed stage fraction (0.22
to 0.39), T-103 recycle feed stage fraction (0.67 to
0.88), CSTR residence time (0.49 to 0.77), CSTR
temperature (85 to 109) and Main PFR volume (6.83
to 7.46).
This implies that these DVs have a minor effect on the
objectives.

17. CONCLUSION
CONCLUSION
This project reports the design, simulation, optimization, and economic evaluation of
the Kemira-Leonard process for Formic acid production.
The process was successfully developed and simulated using Aspen Plus V9 with the
UNIQ-HOC as the thermodynamic model and appropriate kinetics for carbonylation
and hydrolysis reactions.
Heat Integration was applied to the process to reduce external utilities required.
The equipment sizing and cost estimation were conducted to estimate the Total
Capital Cost of the plant, and Annual Utility Cost was also estimated.

18. REFERENCE
REFERENCE
Design and optimization of Kemira-Leonard process for
formic acid production,W.X. Chua, S. da Cunha, G.P.
Rangaiah, K. Hidajat, Department of Chemical and
Biomolecular Engineering, National University of
Singapore, Singapore 117585, Singapore, 28 March 2019
https://www.sciencedirect.com/science/article/pii/S2590
140019300280