Techno-economic Feasibility of Reactive Distillation for Biodiesel

Article Cite This: Ind. Eng. Chem. Res. 2019, 58, 12028−12040

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Techno-economic Feasibility of Reactive Distillation for Biodiesel Production from Algal Oil: Comparing with a Conventional Multiunit System Biswarup Mondal and Amiya K. Jana* Energy and Process Engineering Laboratory, Department of Chemical Engineering, Indian Institute of Technology, Kharagpur 721302, India Downloaded via BUFFALO STATE on July 19, 2019 at 11:51:23 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

S Supporting Information *

ABSTRACT: Biodiesel from algal oil is widely accepted as a promising alternative to fossil fuels because of its renewability and biodegradability. This work aims to explore the technoeconomic feasibility of biodiesel production through the reactive distillation (RD) route. Initially, the algal oil and biodiesel are modeled on the basis of their major constituents. The thermophysical parameters of all the constituent components are computed using the group contribution method and validated with available experimental data with reasonable accuracy. The sensitivity analysis is performed to identify the process parameters, namely, the algal oil to methanol molar ratio, the reflux ratio, the total number of trays, the number of reactive trays, and the reboiler heat duty. A maximum conversion of 99% is achieved with an algal oil to methanol molar ratio of 1:4, a reflux ratio of 2, a total number of trays of 15, 11 reactive trays, and a reboiler heat duty of 6.4 MJ/min. With this, the proposed RD column produces 65.5 mol % biodiesel at the bottom, which is quiet close to that obtained through other routes. Finally, the performance of the proposed RD column is investigated with reference to a conventional multiunit system (CMS), consisting of a reactor followed by distillation, from an economic, energetic, and environmental perspective in terms of total annual cost (TAC), energy savings, and CO2 emissions. Simulation results indicate a 52.96% savings in TAC, and 43.31 and 40.11% reductions in energy consumption and CO2 emissions are achieved using the proposed RD column with reference to the CMS.

1. INTRODUCTION Increasing energy demand due to rapid industrialization and urbanization is the major cause of the continuous depletion of fossil fuel. About 80% of world’s energy demand is still fulfilled by fossil fuels. This dependency on fossil fuels leads to fluctuations in the petroleum price over decades.1 On the other hand, 98% of carbon dioxide emissions are attributable to fossil fuel combustion,2 and carbon dioxide is the leading greenhouse gas (GHG) responsible for global warming and climate change. Therefore, along with the future energy crisis, environmental concerns in the present have stimulated intensive research in finding a renewable energy vector. Different kinds of renewable energy that differ in terms of their extraction source include bioenergy, solar energy, wind energy, geothermal energy, hydro energy, and nuclear energy. Among them, bioenergy, the energy from biomass, is the most flexible energy source, as it can be converted to liquid, solid, and gaseous fuel. Moreover, it is convenient to store and transport from production sites.3 Biodiesel has gained special attention over other liquid biofuels, namely, bioethanol, biosynthetic oils,4 and others, because of its biodegradability, negligible sulfur content, and similar properties to those of petroleum diesel.5 The properties of biodiesel allow it to be © 2019 American Chemical Society

used in a conventional diesel engine directly or blended with petrodiesel.6 Also, the exponential increase of transport vehicles and their dependency on liquid fuel requires increased production of biodiesel.7 Importantly, the use of biodiesel can ensure almost-closed carbon cycles, and the emissions of CO2 can be reduced by up to 78%.8 In India, the National Biodiesel Mission set a target of 20% blending of biodiesel with petrodiesel by 2017.9 Unfortunately, this target is far away from reality because of agronomical and economic constraints. Biodiesel is a monoalkyl ester of fatty acid, which is produced by either transesterification of triglycerides or esterification of fatty acid with alcohol. Glycerol and water are produced as byproducts of transesterification and esterification reactions, respectively.10 Vegetable oil, animal fat, waste cooking oil, and algal oil are used as sources of fatty acids, which go through transesterification and esterification reactions to produce biodiesel.10 The former reaction with edible vegetable oils such as palm oil,11 soybean oil,12 rapeseed Received: Revised: Accepted: Published: 12028

January 19, 2019 June 2, 2019 June 10, 2019 June 10, 2019 DOI: 10.1021/acs.iecr.9b00347 Ind. Eng. Chem. Res. 2019, 58, 12028−12040

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Industrial & Engineering Chemistry Research oil,13 and coconut oil14 and with nonedible vegetable oils such as jatropha oil,15 polanga oil, karanja oil, mahua oil, Garcinia indica oil, and neem oil have been studied extensively.16 Although the nonedible oil sources have no food value, the required agricultural land to produce the plants still create conflict with food security. Therefore, the scientific community is engaged in finding a viable and sustainable source of fatty acids that can replace vegetable oil (edible and nonedible). In this regard, waste cooking oil (WCO)17,18 and waste animal fat19,20 (WAF) have been used as feedstock for biodiesel to avoid the use of cultivated land. Both of these feedstocks are cheap and waste material. However, the lower availabilities of WCO and WAF and their pretreatment processes are the main obstacles of making them suitable feedstock for biodiesel production. Still more than 95% of biodiesel is produced from edible and nonedible oil crops,21 which raises the debate of “food for the poor or fuel for the rich”.16 In this scenario, algal oil has the potential to fulfill the demand for biodiesel, which has increased sharply in recent years. It is the most photosynthetically efficient plant among feedstocks in the perspective of converting solar energy into chemical energy. Algal oil is a promising feedstock for biodiesel production in terms of its high oil yield (over 200 times those of other vegetable oils), eco-friendliness, cultivation suitability, and sustainability with the ability of CO2 fixation.2 Recent studies reveal the potential of producing biodiesel from algal oil.22−24 Both singlestage25−27 and two-step28,29 transesterification reactions of algal oil are reported in the literature. Subsequently, noncatalytic algal oil transesterification under supercritical conditions is reported.30,31 Further studies have focused on kinetic and thermodynamic studies of transesterification reactions of the different algae species, namely, Chlorella protothecoides,32 Chlorella vulgaris,33 Chorella protothecoides,34 Schizochytrium limacinum,35 Spirulina platensis,36 and others. Industrialization of biodiesel is in its infancy because of the high biodiesel price compared with that of petrodiesel. Still, 75% of the total biodiesel cost depends on the raw material.19 Apart from this, operating expenses are also a barrier.32 To overcome these barriers, a method for the transesterification of algal oil is proposed that can be conducted in a reactive distillation column. This distillation process integrates reaction and separation in a single unit and provides high conversion, selectivity, and flexibility. Additionally, the proper utilization of energy can reduce utility consumption and hence capital cost.37,38 There are a few works that evaluate the feasibilities of the transesterification reactions of some vegetable oils in reactive distillation (RD) columns.39−41 To our knowledge, there has been no progress made on biodiesel production from algal oil through the reactive distillation route. In addition, techno-economic comparative analysis has not been performed for the conventional multiunit system in comparison with an RD column for the stated system. It is with this intention that the present work has been undertaken. In this context, the present work aims to explore the technoeconomic feasibility of transesterification of the algal oil in a reactive distillation column. S. platensis is used as an algae strain for biodiesel production because of its high growth rate, ease of availability, and sustainability at extreme conditions (salinity and pH). The power law is employed to represent the reaction kinetics.36 The pseudocomponent approach is used to identify the major functional groups involved in the algal oil and biodiesel that are required when finding the thermody-

namic characteristics.42 The pure component properties are estimated by the Ceriani group contribution method. With these, a rigorous simulation is performed to explore the feasibility of the proposed system. To compare the proposed RD column, we developed a reactor followed by a distillation column (called here a conventional multiunit system, CMS) for biodiesel production. It was determined that the RD outperforms the CMS in terms of energy and cost savings. Keeping the growing environmental concern in mind, the CO2 emission level is also calculated for the RD column and compared with that of the CMS to show substantial improvement.

2. PROCESS MODELING The previous works on transesterification in RD columns have used single triglycerides to represent the oil. However, vegetable oil or algal oil is typically a mixture of numerous triglycerides. Here, a model component is proposed that can represent algal oil triglyceride and biodiesel more precisely, known as a pseudocomponent approach. This approach is adapted from Machado et al., in which esterification of hydrolyzed soybean oil was performed in a reactive distillation column.42 In this study, we define algal oil as a model triglyceride consisting of fatty acids of their molar compositions: 59.5% palmitic acid, 23.7% linolenic acid, and 16.8% linoleic acid. Similarly, biodiesel is the mixture of 59.5% methyl palmitate, 23.7% methyl linolinate, and 16.8% methyl linoleate.42 2.1. Conventional Multiunit System. Figure 1 depicts a conventional multiunit system (CMS) comprising a continu-

Figure 1. Schematic of a conventional multiunit system.

ous stirred tank reactor (CSTR) followed by a distillation column for the production of biodiesel from algal oil.43 The transesterification reaction occurs in the CSTR, and separation occurs in the subsequent distillation column. The dynamic behavior of the reactor is modeled with the mass and energy balance equations along with reaction kinetics. 44 The assumptions and sequential computational steps are provided in the Supporting Information, allowing simulation of the developed model in a MATLAB environment. The detailed mathematical procedure is provided elsewhere.45 To maintain the desired reactor temperature at 328 K, constant heat is supplied to the reactor at a rate of 5.21 MJ/min, which is 12029

DOI: 10.1021/acs.iecr.9b00347 Ind. Eng. Chem. Res. 2019, 58, 12028−12040

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Industrial & Engineering Chemistry Research

• The reaction takes place in the liquid phase only. • The reaction occurs on the trays, excluding the reboiler and condenser.

locally optimized using the constrained minimum function (fmincon) in the same platform. The products from the reactor are fed to the distillation column to remove the excess methanol, and the final products are collected from the bottom of the column. For the distillation column, a mathematical model is constructed on the basis of the material and energy balance and the algebraic enthalpy and tray hydraulic equations. Furthermore, along with physical properties, the vapor−liquid equilibrium (VLE) is computed and discussed later. 2.2. Reactive Distillation Column. There are two types of approaches followed in modeling an RD column, namely, equilibrium (EQ) and nonequilibrium (NEQ) stage approaches. NEQ is a rate based modeling approach, whereas the EQ stage approach is simple and representative.46 In the present study, the latter approach is used to study the behavior of the RD column. The schematic representation of a typical RD column is shown in Figure 2. The column has a total condenser and a reboiler, and the tray numbering is done from bottom to top.

2.2.2. Tray Modeling. The assumptions stated above lead to the construction of an equilibrium stage model of an RD column. The mathematical model consists of material balance, vapor−liquid equilibrium, molar composition summation, and heat balance equations (MESH). The MESH equations of a typical nth tray (Figure 3) are derived below.

Figure 3. Schematic representation of the nth tray.

total material balance dmn = Ln + 1 + Vn − 1 − Ln − Vn + dt

Nc

∑ δirnεn i=1

(1)

component material balance d(mnxn , i) dt

= Ln + 1xn + 1, i + Vn − 1yn − 1, i − Lnxn , i − Vnyn , i + δirn (2)

heat balance equation d(mnHnL) = Ln + 1HnL+ 1 + Vn − 1HnV− 1 − LnHnL − VnHnV dt + rnεnΔHr , n Figure 2. Schematic of a reactive distillation column.

(3)

equilibrium equation yn , i = kn , i =

2.2.1. Assumptions. The following assumptions are made in the construction of the mathematical model of the reactive distillation column. • The liquid is well mixed on each tray, which offers uniform composition on a particular tray. • Phase equilibrium is established on every tray. • Vapor holdup is negligible compared with liquid holdup. • Thermal losses are negligible. • The operating pressure for the top stage is 101.325 kPa, with a constant stage pressure drop of 0.3 kPa. • The Murphree tray efficiency is 80%. • The vapor phase is ideal. • The mass of the homogeneous catalyst is negligible. • The catalyst has no effect on phase equilibrium. • The reaction is completely described by the kinetic equations.

γiPnvap ,i P

xn , i

(4)

summation equations Nc

∑ xn,i = 1 i=1

(5)

Nc

∑ yn,i = 1 i=1

(6)

In the above modeling equations, mn is the liquid holdup on the nth tray (g mol), whereas the mole fractions of component i in the liquid stream and vapor stream leaving the mentioned tray are xn,i and yn,i, respectively. Vn, the vapor flow rate leaving the nth tray (g mol/min), is computed from the energy balance equation by setting zero in the left-hand side. It is a common 12030


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Industrial & Engineering Chemistry Research Table 1. Groups Present in the Model Algal Oil and Biodiesel component

pseudocomponent

palmitic acid linolenic acid linoleic acid methyl palmitate methyl linolenate methyl linoleate TG (present in algal oil) biodiesel

molar composition (%)

CH3

CH2

CH

COO

COOH

59.5 23.7 16.8 59.5 23.7 16.8

1 1 1 2 2 2 3 2

14 10 12 14 10 12 40.16 12.72

0 6 4 0 6 4 7.28 2.09

0 0 0 1 1 1 3 1

1 1 1 0 0 0 0 0

the molecule, Ak and Bk are the parameters obtained from the regression of experimental data, and k is the group index. 2.2.3.2. Vapor Pressure Model. The vapor pressure model suggested by Ceriani and coauthors is used here and expressed as follows:

convention to assume that the internal energy changes on the trays are much faster than the composition and total holdup changes. Ln (g mol/min), the liquid flow rate leaving the nth tray, is calculated using the nonlinear Francis weir formula, given as ji 999ρavg WL zyzijjj 183.2m MWavg WH yzzz zzjj − L = jjjj z j MWavg zzjj ρavg DcDc 12 zzz k {k {

i y Bi′ Pivap = expjjjjAi′ + 1.5 − Ci′ ln T − Di′T zzzz T k {

1.5

(7)

in which Pvap i is the vapor pressure of component i (Pa), and T is the temperature (K). The terms A′i , B′i , C′i , and D′i are related to the group contribution parameters by the following expressions:

where the liquid flow rate (L), average molecular weight (MWavg), average density (ρavg) of the liquid mixture, column diameter (Dc), weir height (WH), weir length (WL), and liquid tray holdup are in British units. Therefore, the liquid flow rate needs to be converted to metric units to be used in eqs 1−3. Additionally, ΔHr,n, εn, and rn symbolize the heat of reaction (J/g mol), the volume of catalyst (cm3), and the rate of reaction (mol/(cm3 min)) on the nth tray, respectively, and the stoichiometric coefficient of ith component is δi. HLn represents the liquid enthalpy, and HVn represents the vapor enthalpy (J/g mol) of nth tray. The success of the distillation model depends upon the accuracy of the vapor−liquid equilibrium relation, as expressed by the modified version of Raoult’s law in eq 4. Here kn,i, the vapor−liquid equilibrium coefficient, is a function of vapor pressure (Pvap n,i ), total pressure (P, mmHg), and the activity coefficient (γn,i) of the ith component on the nth tray. As indicated, the nonideality of the liquid phase is accounted for by the activity coefficient, and the vapor phase is considered to be at ideal state. This assumption is reasonable at low pressure operation. It is noticeable that the thermophysical properties of pure components are necessary for the above equations, which are detailed in the following section. 2.2.3. Thermophysical Properties. The thermophysical properties in terms of liquid heat capacity and vapor pressure of the components are estimated using the group contribution method that does not require VLE data. The fragment based approach to predicting the vapor pressure and heat capacities of triglycerides and other fatty acid compounds is reported in the literature.47−49 However, the Ceriani group contribution method shows better accuracy and more rigorous results over the fragment based model.50,51 Thus, in the present work, the Ceriani group contribution method is adopted to predict the specific heat and vapor pressure of the components. 2.2.3.1. Heat Capacity Model. The equation of the Ceriani group contribution model for calculating the liquid heat capacity is given below: Cp,L i =

∑ Nk(Ak + Bk T ) k

(9)

Ai′ =

∑ Nk(A1k + MiA 2k ) + α(f0

+ Ncf1 ) + (s0 + Ncss1)

k

(10)

Bi′ =

∑ Nk(B1k + MiB2k ) + β(f0

+ Ncf1 )

k

Ci′ =

∑ Nk(C1k + MiC2k) + γ(f0

+ Ncf1 )

k

Di′ =

∑ Nk(D1k + MiD2k) + δ(f0 k

+ Ncf1 )

(11)

(12)

(13)

where Nk is the number of groups, Mi is the molecular weight of the component, Nc is the total number of carbon atoms, and Ncs is the number of carbons of the alcoholic part. The rest of the parameters are identified by regressing the experimental data. The values of the model parameters for individual groups are reported elsewhere.50,51 2.2.4. Thermodynamic Modeling. Different thermodynamic properties, namely, the liquid enthalpy, vapor enthalpy, and activity coefficient, are modeled below. 2.2.4.1. Enthalpies. These polynomial expressions45 are used for enthalpy calculations: Nc

HL =

∑ xi ∫

T

Tref

i=1

Cp,L i dT

(14)

Nc

HV = HL +

∑ yi λi i=1

(15)

where λi is the enthalpy of vaporization of component i (J/g mol), and Tref is the reference temperature (i.e., 298.15 K). The enthalpy of vaporization is calculated by the Clausius− Clapeyron equation as follows:

(8)

CLp,iis

λi = RT 2

where the liquid heat capacity of component i (J/g mol K), T is the temperature (K), Nk is the number of groups in 12031

d(ln Pivap) dT

(16) DOI: 10.1021/acs.iecr.9b00347 Ind. Eng. Chem. Res. 2019, 58, 12028−12040

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Industrial & Engineering Chemistry Research where R, the universal gas constant, is equal to 8.314 J/g mol K, and Pvap is the vapor pressure. i 2.2.4.2. Activity Coefficient. The nonideality of the liquid phase is to be modeled using the universal quasi chemical functional-group activity coefficient (UNIFAC) method to calculate the activity coefficient of the components (as used in eq 4).52 Here, the vapor phase is assumed to be ideal. For the algal oil system, the experimental VLE data is not available in the open literature. Thus, the UNIFAC model is preferred here for estimating the activity coefficient of a component on the basis of the contributions of different known functional groups. The subgroups in the pseudocomponents are calculated according to the weighted averages of the constituents and are presented in Table 1. The volume and area parameters of different groups and their interaction parameters are obtained through regression of experimental data and are available in the literature.53 The volume, area, and interaction parameters of the groups present in the pseudocomponents are provided in Tables S1 and S2 (Supporting Information). 2.2.5. Reaction Kinetics. The transesterification of algal oil with methanol is governed by the following stoichiometric relationship: TG + 3CH3OH → 3RCOOCH3 + GL

TAC ($/year) = operating cost (OC) +

energy savings (%) =

(17)

1 1 dni i E yz zzCi = k 0 expjjj− εcat νi dt k RT {

Spirulina platensis algae

14.518

2.31

× 100 (20)

(21)

Here, Qfuel refers to the amount of fuel burnt (kW), NHV refers to the net heating value (kJ/kg), C% refers to the carbon content of the fuel, and α = 3.67 represents the ratio of the molar masses of CO2 to C. Here, natural gas is used as fuel with an NHV of 5.16 × 104 and a C% of 75.38. Further, the amount of fuel burnt (Qfuel) can be computed for the steam boiler as follows: Q fuel =

Table 2. Kinetic Data for the Algal Oil Transesterification Reaction36 frequency factor, k0 (1/min)

Q CMS

Q i kg y C% CO2 emission jjjj zzzz = fuel × ×α 100 k s { NHV

(18)

activation energy, E (kJ/mol)

Q CMS − Q RD

Here, QCMS is the total heat consumption in the CMS, and QRD represents the reboiler heat duty of the RD column. 3.3. Environmental Indicator. In this study, CO 2 emission is used as an environmental indicator to quantify the performance of the system. CO2 is produced by burning the fuel in the steam boiler, which provides heat to the reboiler. The quantification of CO2 is done by using the following equation:59

where Ci is the concentration, νi is the stoichiometric coefficient of component i, and εcat is the volume of catalyst. The kinetic constants are presented in Table 2.

feedstock

(19)

The capital cost is obtained by summing the individual equipment costs, whereas the operating cost is calculated by adding the coolant and steam costs. Stainless steel is used for the construction of the equipment for both schemes. A Marshall and Swift (M&S) index of 1704.9 is adopted here, and a payback period of 5 years is assumed. The formulas used for cost calculations are summarized in Table S3.56,57 In addition, 8000 working hours per year is adopted for estimating the operating cost. Here, the steam cost is $17 per ton, and the cooling water cost is $0.06 per ton. 3.2. Energy Savings. The energy consumption of the CMS includes the heat input to the reactor and the reboiler heat duty, whereas for the RD column, it includes only the reboiler heat duty. The energy savings of the RD column is calculated from58

TG represents the triglycerides present in algal oil, GL represents the glycerol, and RCOOCH3 represents the algal oil methyl ester or biodiesel. The transesterification reaction is considered to be first order with the concentration of methyl ester. In the kinetic expression, the mole fraction is used instead of the concentration.45 The reaction kinetics and concerned parameters are determined by transforming the three-step reaction into a single-step reaction in irreversible mode, and they are reported in the literature.36,54 We have adopted it in our work. It is true that in RD operation, the reaction performs in the forward direction. The reaction rate model can be expressed as ri =

capital investment (CI) payback period

Q proc λproc

ij T − TA yz zz (Hproc − H water) × jjj FTB j TFTB − Tstack zz { k

(22)

where Qproc and λproc (kJ/kg) indicate the process heat duty and latent heat of steam supplied to the process, respectively. TFTB, TA, and Tstack are the flame temperature, ambient temperature, and stack temperature, respectively. Hproc and Hwater refer to the enthalpy of the process and the feedwater, respectively.

4. SIMULATION ALGORITHM In order to simulate the process model shown above, we have developed the computer code in the MATLAB environment. For this, the following computational steps are followed in order: Step 1: Specify column dimensions (column diameter, weir height, weir length, and total number of trays), feed (composition, temperature, and flow rate), feed tray position, pressure profile, and tray (efficiency, liquid holdup, and composition).

3. PERFORMANCE INDICATORS The performance of the RD column over the CMS is quantified by assessing the economic, energy, and environmental indicators. 3.1. Economic Evaluation. The economic indicators, such as operating cost, capital cost, and total annual cost (TAC), are used for the evaluation of the RD column and CMS. TAC is commonly used as an indicator to check the economic feasibility of a system. It is calculated using the following wellknown equation:55 12032


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quently, the vapor pressures of two fatty acids and two esters are calculated and compared with the experimental results in Figure 5.50,62 Methyl palmitate and stearic acid are further

Step 2: Input the operating parameters, namely, the reboiler heat duty and reflux ratio. Step 3: Calculate the vapor pressure and specific heat using the Ceriani group contribution model. Step 4: Use the bubble point algorithm to compute the temperature and vapor phase composition on each tray. Then, determine the actual vapor phase composition by utilizing the Murphree tray efficiency. Step 5: Calculate the liquid and vapor phase enthalpies. Step 6: Compute the rate of reaction for each reactive tray. Step 7: Calculate the liquid flow rate with the Francis weir formula. Step 8: Calculate the vapor flow rate by solving the energy balance equations. Step 9: Evaluate the liquid holdup by solving the total mole balance for the future time step. Step 10: Compute the liquid phase composition by using the component mole balance for the future time. To continue the simulation for the next time step, go back to Step 3.

Figure 5. Vapor pressure prediction of different fatty acid components by the group contribution method.

5. RESULTS AND DISCUSSION In this section, rigorous simulation of the RD column is performed in the MATLAB environment to check the feasibility of the system. Here, we find the suitable RD configuration and its optimum operating parameters. Prior to this, an attempt is made to validate the group contribution model used. The behavior of the column is analyzed by performing a sensitivity test; then the composition, extent of the reaction, and temperature profile along the column are presented. Finally, the superiority of the RD column is investigated over its conventional analogue (i.e., CMS). 5.1. Validation of Group Contribution Model. The Ceriani group contribution model is simulated in the MATLAB environment to estimate the said thermophysical parameters. The fatty acid system consists of three different groups, namely, a fatty acid, ester, and triglyceride. Here, four components are chosen from each group such that it covers the entire system and calculates the thermophysical properties. Subsequently, the simulated results are validated with published experimental results. In Figure 4, the simulated results of specific heat are plotted and compare with the experimental results.51,59−61 Subse-

Figure 6. Vapor pressure prediction of fatty acids and fatty esters by the group contribution method.

plotted in Figure 6 for better representation. Similarly the vapor pressure of tripalmitin is plotted in Figure 7.52 From the above figures, good agreement between the experimental and calculated results is observed, which ensures the reliability of the model. 5.2. Parametric Study. As the aim of this study is to investigate the feasibility of the RD column, the one factor at a time (OFAT) approach is adopted to find the suitable design and operating parameters. In the OFAT approach, one parameter is varied while the other parameters are kept fixed in all the simulation experiments. The pseudo-optimal RD column is constructed by performing the sensitivity analysis with an OFAT method. Five degrees of freedom, namely, the molar ratio of feed streams, the reboiler duty, the number of total trays, the number of reactive trays, and the reflux ratio, are considered in the current study. 5.2.1. Effect of Molar Ratio. The molar feed ratio is one of the key parameters on the performance of the RD column. Figure 8 shows the effect of the molar feed ratio between algal

Figure 4. Prediction of liquid heat capacity by the Ceriani group contribution method. 12033


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Figure 9. Effect of reboiler heat duty on biodiesel composition.

Figure 7. Vapor pressure prediction of tripalmitin by the group contribution method.

Figure 10. Effect of the total number of trays on biodiesel composition.

Figure 8. Effect of molar ratio of feed on biodiesel composition.

rectifying section to obtain pure methanol as a distillate. With this, the total number of trays selected here is 15. 5.2.4. Effect of Number of Reactive Trays. The number of reactive trays affects the residence time. Therefore, it has a positive impact on biodiesel production. The results are demonstrated in Figure 11. It is obvious that adding more

oil and methanol on the bottom products. The simulation results indicate that molar compositions beyond 1:4 have a decreasing trend with increasing feed ratio. This is because of the dilution effect of the unreacted methanol. The selected pseudo-optimal molar feed ratio in terms of biodiesel composition is thus 1:4. 5.2.2. Effect of Reboiler Duty. The effect of the reboiler duty on biodiesel composition is illustrated in Figure 9. Increasing the reboiler heat duty increases the temperature of the reactive section and thus increases the rate of the transesterification reaction monotonically. However, a reboiler duty higher than 6.4 MJ/min is not recommended because this may lead to thermal decomposition of the biodiesel and glycerol. The literature suggests that the reboiler temperature should be less than 150 °C to avoid decomposition of glycerol.63 Accordingly, the reboiler duty chosen here is 6.4 MJ/min. 5.2.3. Effect of Number of Total Trays. The total number of trays has a significant role on the purity of the biodiesel. In this study, the number is varied from 15 to 60, and it is observed (Figure 10) that the biodiesel composition remains constant up to 20 trays. Further increases in the total number of trays cause decreases in the biodiesel composition. This interesting fact can be attributed to the large differences in the boiling points of methanol and the other components needing a small

Figure 11. Effect of the number of reactive trays on biodiesel composition. 12034


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Industrial & Engineering Chemistry Research reactive trays increases biodiesel production. However, this increasing trend of biodiesel composition continues until there are 11 trays. Therefore, we select 11 reactive trays, which ensures almost complete conversion of triglyceride. 5.2.5. Effect of Reflux Ratio. The influence of the reflux ratio on biodiesel composition in the bottom product has been studied and is shown in Figure 12. It is observed that the influence becomes insignificant beyond a reflux ratio of 2. A quasi-optimum value of 2 is thus selected to operate the column.

Figure 13. Liquid phase composition profile along the reactive distillation column.

methanol composition is evident as a result of the high reboiler temperature. The red line shows the algal oil molar composition profile, in which the oil composition is highest at stage 15 because of the introduction of feed at this stage. It is obvious that the decreasing trend of algal oil from stage 15 to the bottom of the column is due to the consumption of that oil as the reaction proceeds. On the other hand, the blue and green lines represent biodiesel and glycerol, respectively, both of which show similar trends throughout the column. At the sixth stage, the mole fractions of both products are high because of the accumulation of products at the lower section of the reactive zone. Thenceforth, decreased product composition is noticeable, due to the escalating methanol composition. The tray hydraulics of the column is demonstrated in Figure 14,

Figure 12. Effect of reflux ratio on biodiesel mole fraction in the bottom product.

5.3. Simulation Analysis. By performing sensitivity tests, we finally get the pseudo-optimal operating parameters and design variables, which are reported in Table 3. With this, the Table 3. Specifications of the Reactive Distillation Column variable

specification

pressure no. of trays (excluding reboiler and condenser) reflux ratio reboiler heat duty top feed (algal oil) feed tray location flow rate temperature bottom feed (methanol) feed tray location flow rate temperature

760 mmHg 15 2 6.4 MJ/min 14 100 mol/min 300 K 4 400 mol/min 310 K

liquid composition profile along the height of the RD column is displayed in Figure 13. The RD column discharges excess methanol at the top as an almost pure form (99.54%), and a mixture of biodiesel, glycerol, and methanol at the bottom. It is noticeable that the highest mole fraction of biodiesel obtained from the simulation experiment is 0.655. Further separation is recommended to get high purity product, which is not considered in this work. The black line in the composition profile indicates the mole fraction of methanol. This lightest compound is enriched in the top section of the column, and its composition decreases continuously from stage 16 to 6. Thereafter, an instant increase is observed as a result of the introduction of methanol at the fifth stage. Almost same the methanol composition is observed up to the second stage. In the reboiler, which represents stage 1, a sharp fall in the

Figure 14. Liquid flow rate profile along the reactive distillation column.

analyzing the liquid flow rate profile. From stage 6 to 15, the liquid flow rate decreases smoothly, and a sudden fall is marked at stages 16 and 17. Similarly, an abrupt elevation of the liquid flow rate is detected at stages 5 to 2 as the methanol is fed at a higher flow rate. Moreover, Figure 15 demonstrates the temperature profile of the RD column. It is observed that the temperature of the sixth stage is drastically increased as a result of the enrichment of heavy components.64 It can be seen in the component profile that the biodiesel and glycerol composition are high in 12035


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Figure 15. Temperature profile and extent of reaction along the reactive distillation column.

Table 4. Comparison of Liquid Molar Compositions between Our Results and Published Results top product composition

bottom product composition

transesterification reaction

methanol

TG

BD

GL

methanol

TG

BD

GL

ref

triolein vegetable oil soybean oil soybean oil algal oil

1.00 1.00 1.00 1.00 1.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.000 0.002 0.070 0.334 0.126

0.00 0.00 0.000 0.00 0.001

0.750 0.743 0.810 0.495 0.655

0.250 0.249 0.120 0.161 0.218

7 40 65 66 our study

that stage. Interestingly, the extent of the reaction is high at stages 14 and 15, where the tray temperatures are typically in the range of 327 to 335 K. The literature36 suggests that 328 K is the most suitable temperature for the transesterification reaction. The results obtained from this simulation study are compared with the published results of other transesterification reactions in RD columns. Top and bottom product compositions are shown in Table 4, indicating a close resemblance with our results. 5.4. Performance Analysis. We attempted to keep the input specifications and operating conditions close, if not the same, for both the CMS and RD column to make a fair comparison between them. In this regard, Table 5 reports the compositions of all the involved streams in both schemes. Obviously, the molar compositions of biodiesel between them are quite close. Interestingly, the amount of unreacted reactant

in the CMS is slightly more than that in the RD column; this implies a lower conversion of algal oil in the CMS. This confirms the improvement of selectivity in reactive distillation column. The performance of the RD column is assessed with reference to the CMS in terms of the capital investment, operating cost, TAC and energy savings, and CO2 emission. It is detailed in Table 6. Here, we observe that there is a 57.28% increase in capital investment of the CMS compared with that of the RD column. This is due to the additional reactor installation cost and increased number of trays in the conventional distillation column. Additionally, the transesterification reaction is endothermic in nature, and thus it requires a constant heat supply to the CSTR, which results in higher operating costs. It is determined that there is about a 40.14% increase in operating cost in the case of the CMS. As stated, the economic performance is to be quantified in terms of the TAC, which is calculated by summing the capital cost and operating cost for a payback period of 5 years. For the example system, a 52.96% TAC savings is obtained by the RD column with respect to the CMS, which is quite significant. As far as the energy savings is concerned, it is about 43.41% for the proposed RD column. It is a fact that the energy consumed to run the process is generated through the combustion of fossil fuel, which leads to the production of CO2 gas in the atmosphere. Hence, the amount of CO2 emission and the energy requirements of the system are directly proportional to each other. Here, a 40.11% reduction in the CO2 emission level is obtained by the use of the RD column. Overall, the

Table 5. Comparison of the Exit Bottom Compositions between the RD Column and the Conventional Multiunit System molar composition methanol algal oil biodiesel glycerol

CMS

RD column

0.108 0.024 0.654 0.214

0.126 0.001 0.655 0.218 12036


Article


Table 6. Comparison of Different Key Performance Indicators between the RD column and the Conventional Multiunit System conventional multiunit system key performance indicator

operating cost

operating cost savings (%) capital investment

cost component

reactor

distillation column

area of reboiler (m2) area of condenser (m2) steam ($/year) cost of coolant ($/year) operating cost ($/year)

  18 064.52  18 064.52

3.58 1.67 22 144.43 781.59 22 926.02

reboiler cost ($) condenser cost ($) column cost ($) tray cost ($) reactor cost ($) capital investment ($)

    214 876.13 214 876.13

48 713.58 21 317.44 299 992.96 22 659.64 392 683.62

607 559.76

61 039.74

101 462.75

162 502.49

5209.4

6100

11 309.4

138.08

187.30

325.38

capital investment savings (%) TAC ($/year) TAC savings (%) reboiler heat duty (kJ/min) energy savings (%) CO2 emission (ton/year) CO2 emission saving (%)

proposed RD column secures a substantial improvement in the aspects of energy and cost savings and CO2 emissions.

■

6. CONCLUSION The present article demonstrates that the production of biodiesel from algal oil is techno-economically feasible through the reactive distillation (RD) route. Before achieving this goal, we first computed the thermophysical parameters for all constituent components by use of the Ceriani group contribution model and validated them with the experimental data. The design and operating parameters of the system were obtained by performing sensitivity analysis. Up to 99% conversion is achievable under the conditions of a reflux ratio of 2, 15 total trays, 11 reactive trays, a reboiler heat duty of 6.4 MJ/min, and an algal oil to methanol molar ratio of 1:4. With this, it was determined that the proposed RD column yields biodiesel at the bottom with a 65.5 mol % purity, which was reasonably close to that obtained through several other technological routes. In order to quantify the performance of the RD column, we compared it with the conventional multiunit system (CMS), which consisted of CSTR followed by distillation, in terms of energy and TAC savings and CO2 emission levels. It was determined that the proposed RD column secured a 43.41% savings in energy consumption and a 52.96% savings in TAC. Further, it reduced CO2 emission by 40.11%. Obviously, reactive distillation shows promising performance from energy efficiency, economic, and environmental perspectives. Future work will be directed toward making the diesel purity higher by separating the two bottom components, namely, the biodiesel and glycerol. Apart from this, the economic viability for the end usage of biodiesel is to be explored.

■

total

40 990.54

RD column 3.76 2.97 23 144.13 1392.55 24 536.68 40.14 50 257.71 31 029.42 167 287.14 10 939.14 259 513.41 57.28 76 439.36 52.96 6400 43.41 194.87 40.11

Group volume and area parameters for the UNIFAC model, group−group interaction parameters, and cost estimating formula and parameter values (PDF)

AUTHOR INFORMATION

Corresponding Author

*Tel.: +91 3222 283918. Fax: +91 3222 282250. E-mail: [email protected]. ORCID

Amiya K. Jana: 0000-0003-1367-5480 Notes

The authors declare no competing financial interest.

■

NOMENCLATURE

Abbreviations

BD CM EQ GHG GL MESH

biodiesel conventional multiunit system equilibrium greenhouse gas glycerol material balance, vapor−liquid equilibrium, molar composition summation, and heat balance equations M&S Marshal and Swift cost index NEQ nonequilibrium NHV net heating value (kJ/kg) RD reactive distillation TAC total annual cost ($/year) TG triglyceride UNIFAC universal quasi chemical functional-group activity coefficient VLE vapor−liquid equilibrium WAF waste animal fat WCO waste cooking oil OFAT one factor at a time DOF degrees of freedom

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.9b00347. 12037


Article

Industrial & Engineering Chemistry Research Variables

vap vapor

■

a activity A′, B′, C′, D′ group contribution parameters A1, B1, C1, D1, A2, B2, C2, D2 parameters obtained from data regression Ak, Bk parameters obtained from data regression Cp heat capacity (J/g mol K) Dc column diameter (in.) E activation energy (kJ/mol) f 0, f1, s0, s1 optimized constants H enthalpy of the mixture (J/g mol) k vapor−liquid equilibrium coefficient k0 frequency factor (1/min) L liquid flow rate (g mol/min) m liquid holdup on tray (g mol) M molecular weight MWavg average molecular weight (g/ mol) Nc number of carbon atoms Nc number of components Ncs number of carbon atoms of the alcoholic part Nk number of groups in the molecule NT number of total trays P total pressure (mmHg) Pvap vapor pressure (mmHg) r rate of reaction (mol/(cm 3 min)) R universal gas constant (J/g mol K) T temperature (K) Tref reference temperature (K) U overall heat transfer coefficient (W/m2 K) V vapor flow rate (g mol/min) WH weir height (in.) WL weir length (in.) x liquid phase composition (mole fraction) y vapor phase composition (mole fraction) δ stoichiometric coefficient ε volume of catalyst (cm3) ρavg average density (g/cm3) γ activity coefficient α, β, γ, δ optimized parameters λ enthalpy of vaporization (J/g mol) ΔTLMTD logarithmic mean temperature difference of a heat exchanger (K) Subscripts

i component index k group index n tray index Superscripts

L V

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Techno-economic Feasibility of Reactive Distillation for Biodiesel

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