Kinetic study of fuel bio-additive synthesis from glycerol esterification with acetic acid over acid polymeric resin as catalyst

Kinetic study of fuel bio-additive synthesis from glycerol esterification with acetic acid over acid polymeric resin as catalyst

Fuel 264 (2020) 116879 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel Full Length Article Kinetic s...

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Fuel 264 (2020) 116879

Contents lists available at ScienceDirect

Fuel journal homepage: www.elsevier.com/locate/fuel

Full Length Article

Kinetic study of fuel bio-additive synthesis from glycerol esterification with acetic acid over acid polymeric resin as catalyst D.M. Reinosoa, D.E. Boldrinib,c,

T



a

Instituto de Química del Sur-INQUISUR (UNS-CONICET), Av. Alem 1253, Bahía Blanca CP 8000, Argentina Departamento de Ingeniería Química, Universidad Nacional del Sur (UNS), Av. Alem 1253, Bahía Blanca CP 8000, Argentina c Planta Piloto de Ingeniería Química-PLAPIQUI (UNS-CONICET), Camino “La Carrindanga” Km 7, Bahía Blanca CP 8000, Argentina b

GRAPHICAL ABSTRACT

ARTICLE INFO

ABSTRACT

Keywords: Bio-additives Glycerol esterification Acetylglycerides Acidic ion exchange resin Kinetic modeling

The increase of remnant of glycerol from biodiesel production results in the need to develop valorization strategies for this low-cost by-product. In this context, bio-additives synthesis from glycerol such as acetylglycerides has emerged as an economically viable alternative. In this work, a kinetic study of glycerol esterification with acetic acid over Dowex 650C ion exchange resin as catalyst to obtain fuel bio-additives was performed. The experimental study was carried out in a batch reactor at 353–393 K temperature range using acetic acid to glycerol molar ratio from 3:1 to 9:1 and catalyst loading between 4 and 8 wt%. Furthermore, different kinetic models based on the Langmuir-Hinshelwood-Hougen-Watson (LHHW), EleyRideal (ER) and Power Law (PL) approaches were proposed in order to correlate the experimental data. In addition, it was determined that the intraparticle and extraparticle mass and heat transfer resistances in the polymeric resin are negligible. The obtained results from kinetics modeling evidence that the best model to describe the studied reaction was the Eley-Rideal. In this model, it was considered that the catalytic surface reaction was the controlling reaction stage, which involves the reaction between adsorbed acetic acid and glycerol as well as mono- and diglycerides from the bulk liquid phase, generating the corresponding acylglycerides and water. Moreover, it was found that glycerol and water adsorption on the catalytic surface is stronger than that for acetic acid and acetylglycerides.



Corresponding author at: Departamento de Ingeniería Química, Universidad Nacional del Sur (UNS), Av. Alem 1253, Bahía Blanca CP 8000, Argentina. E-mail address: [email protected] (D.E. Boldrini).

https://doi.org/10.1016/j.fuel.2019.116879 Received 15 September 2019; Received in revised form 11 December 2019; Accepted 13 December 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

Nomenclature

DAG, TAG and W ), mol ml 1 Cpi Heat capacity of component i (i = G, AA , MAG and W ), J mol 1 K 1 CS Concentration of free active sites on catalyst , mmol g 1 CTS Concentration of total active sites on catalyst , mmol g 1 D Diffusivity , cm2 s 1 De Effective diffusivity, cm2 s 1 Diacetylglycerides DAG Activation energy of reaction i (i = 1, 2 and 3), J mol 1 Ei ER Eley Rideal model G Glycerol ki j Forward rate constant (i = 1, 2 and 3; j = ER,

Universal gas constant , J mol 1 K 1 R RP Particle radious, cm Selectivity to AG, dimensionless SAG SDAG Selectivity to DAG, dimensionless Selectivity to MAG, dimensionless SMAG STAG Selectivity to TAG, dimensionless Time, s t T Temperature, K Triacetylglycerides TAG Molar volume of acetic acid , ml mol 1 VAA VC, i Critical molar volume of component i (i = AA and G ), ml mol 1 Molar volume of glycerol, ml mol 1 VG Molar volume of component i (i = AA and G ), ml mol Vi w Catalyst loading , mmol ml 1 Water W XG Glycerol conversion , dimensionless yAA Mass fraction of acetic acid, dimensionless Mass fraction of glycerol, dimensionless yG Parameter defined in Eq. 6, mPa cm3 K 1 mol 1/3 Y z ijk k th model predicted value of variable j in experiment i , mol ml 1 z ijk k th measured value of variable j in experiment i, mol ml 1

LHHW and PL), [ml g 1s 1, mol g ki0j

Greek letters

AA AG AICi

Acetic acid Acetylglycerides (MAG, DAG and TAG) Akaike information criterion of model i (i = ER, LHHW ,

AICmin. Ci

PL), dimensionless Minimum AICi (i = ER, LHHW , PL), dimensionless Concentration of component i (i = G, AA, MAG, DAG,

TAG and W ), mol ml 1 CiEq. Concentration of component i at equilibrium (i = G, AA, MAG,

1

s 1, ml2 g

Pre

exponential factor (Eq. 57), [ml g

ml 2 g ki j

1

s

1

1

s

1,

1

s

1

mol 1]

mol g

1

1,

s

Prater Number , dimensionless Hi Enthalpy of adsorption of component i, J mol 1 Hi0 Standard enthalpy of formation of component i (i = G, AA,

mol 1] Forward rate constant (i = 1, 2 and 3;

j = ER and LHHW ), [ml2 g 1 s 1 mol 1] k ji Backward rate constant (i = 1, 2 and 3; j = ER, 1

1,

1

1,

ml 2

1

LHHW and PL), [ml g s mol g s g s k ji Backward rate constant (i = 1, 2 and 3;

K Ki

MAG and W ), J mol 1 Enthalpy of Reaction 1, J mol Hr 1

mol

1

1]

1

i

AIC difference of model i (i = ER, LHHW and PL), dimensionless Porosity, mlliq. mlcat1.

j = ER and LHHW ), [ml2 g 1 s 1 mol 1] Number of estimable parameters, dimensionless

i

Estimated residuals of model i (i = ER, LHHW and PL), mol ml Set of model parameters to be estimated, dimensionless Thermal conductivity of acetic acid , W m 1 K 1 AA Effective thermal conductivity catalyst , W m 1 K 1 e Thermal conductivity of fluid , W m 1 K 1 f Thermal conductivity of glycerol, W m 1 K 1 G Thermal conductivity of solid, W m 1 K 1 S Dynamic viscosity of acetic acid, mPa s µAA Particle density , g ml 1 P

Adsorption equilibrium constant of component i (i = G, AA, MAG , DAG, TAG and W ), ml mol 1

Pre exponential factor (Eq. 58), ml mol 1 Ki0 Langmuir Hinshelwood Hougen Watson model LHHW m Number of components involved in Reaction 1, dimensionless Monoacetylglycerides MAG AA to G molar ratio, dimensionless MR n Sample size , dimensionless N Total number of measurements taken during all the experiments,

1

2 ijk

Variance of the k th measurement of variable j in experiment i , mol 2 ml

dimensionless Number of experiments performed, dimensionless NE NMij

2

Tortuosity, cmliq. cmcat1.

i

Number of measurements of the jth variable in the ith experiment ,

Estequiometric coefficient of component i in Reaction 1(i = G, AA,

dimensionless NVi Number of variables measured in the ith experiment , dimensionless Power Law model PL Rate of reaction i (i = 1, 2 and 3), mol mmol 1 s 1 ri Observed initial rate of reaction , mol mmol 1 s 1 robs.

MAG and W ), dimensionless L

Estequiometric coefficient of limiting reactant in reaction1, dimensionless

Weisz Prater number , dimensionless Objective function, dimensionless Catalyst loading , wt . % respect to glycerol

1. Introduction

main byproduct, which represents a 10 wt% of biodiesel total production. However, the current market does not cope with the purification cost of crude glycerol for conventional applications; consequently, it is essential to develop value-added compounds to make the overall biodiesel process costs competitive [2–3]. In this context, glycerol has

In the last years, biodiesel industry has gained considerable attention due to environmental issues resulting in its production increases [1]. Therefore, considerable amounts of glycerol are produced as the 2

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

become a chemical platform for valuable chemicals production through several chemical pathways like oxidation, dehydration, hydrogenolysis, ammoxidation, acetalization, etherification, esterification or transesterification [4–9]. Particularly, acetylglycerides (AG) synthesis is among the promising and economically viable approaches to glycerol large surplus [10–11]. Conventionally, acetylglycerol is obtained via acetylation reaction requiring an acylation agent such as acetic acid, or acetic anhydride. However, for practical uses acetic acid is considered more suitable in terms of environmental and cost features [12]. In general, the glycerol (G) esterification with acetic acid (AA) involves consecutive and reversible three-step reactions obtaining monoacylglycerides (MAG), diacylglycerides (DAG) and triacylglycerides (TAG) as products used on food and cosmetic industry, cryogenics and raw materials for biodegradable polymer synthesis [13–14]. Moreover, DAG and TAG have been recognized as potential high-quality liquid bio-additive to improve fuel properties and could replace the controversial tertiary alkyl ethers (MTBE and ETBE) [2,15,16]. Particularly, glycerol esterification reaction rate depends on kinetic and thermodynamic parameters influenced by the operating conditions such as temperature, reaction time, AA:G molar ratio and catalyst loading, which can be optimized in order to maximize DAG and TAG production. Taking these aspects into account, it is feasible to attain proper selectivity to interest products through certain technical strategies for instance, produced water removal, reaction temperature, or acetic acid molar ratio increases; or a combination of these [12,17,18]. Regarding the conventional process, it is performed over corrosive and toxic acids (H2SO4, HCl, or H3PO4) as homogeneous catalysts which are hard to separate from the products. Thereby, in order to overcome these drawback, several solid catalysts with high Brönsted acidity have been studied; for example, ion exchange resins [10,19,20], supported heteropolyacids [11,17,21–24], functionalized mesoporous materials [25–27], and other acid solids [11,28–30]. Despite the remarkable advantage of heterogeneous acid catalysts, their cost, complex methodology of synthesis and functionalization and reuses are still an unsolved issue for industrial application. In a previous work [31], it was found that acid ion exchange resin (Dowex Monosphere 650C) has a potential for fuel bio-additive synthesis with high glycerol conversion (~100%) and elevated selectivity towards TAG (~34%) and DAG + TAG mixture (~88%) at 240 min and 393 K, operating under kinetic control. Also, the catalyst was reusable over five catalytic cycles without regeneration, or active species leaching. Considering these results [31], the purpose of the present work is to perform a kinetic study of the heterogeneous catalytic esterification of glycerol with acetic acid in order to provide a tool able to accomplish an adequate analysis, design, and simulation of the process. To correlate the experimental data, kinetic parameters including the reaction rate constants at different temperatures and the corresponding activation energies were determined using different models based on the Langmuir-Hinshelwood-Hougen-Watson (LHHW), Eley-Rideal (ER) and Power Law (PL) approaches.

9:1 and catalyst loading from 4 to 8 wt% respect to glycerol (ω). Besides, all the reactions were carried out employing a 0.3 mol initial acetic acid loading. In a typical experiment, glycerol was heated in a silicone bath until the required temperature and then, the acetic acid heated in a separate container at the reaction temperature was added into the reactor together with the catalyst (initial time of reaction). Fig. 1 shows the experiment setup for the catalytic reaction tests. Reaction samples were collected periodically, and the reactants and products were quantified by gas chromatography (GC), using a HewlettPackard 4890D instrument equipped with a flame ionization detector (FID) and Perkin Elmer Elite Wax capillary column (15 m length, 0.25 mm diameter, and 0.25 μm film thickness). The samples analysis was performed using the internal standard technique and the specific response factors were determined from GC analysis of known composition standard compounds.

XG =

SAG =

molG converted molG feeded

(1)

molAG formed (2)

molG converted

2.2. Evaluation of diffusional controls In order to evaluate the perfect mixing condition and isothermicity in the reactor bulk, the concentration and temperature on various points into the reactor were measured at a specific time (5 min) and using different reaction conditions. The simultaneous concentration measurements were performed introducing 3 syringes at the bottom, middle and top of the reaction mixture taking samples at the same time (Runs 1 and 3). Analogously, simultaneous temperature measurements were done measuring at the aforementioned points using 3 thermocouples (Runs 2 and 5). The experimental results for both tests are shown in the supplementary material (Tables A1 and A2). It was previously reported for this same system that to attain a negligible internal and external mass transfer resistance, the optimal stirring rate and particle size should be 500 rpm and < 100 µm, respectively [31]. Nevertheless, to validate the experimental data in this work, the WeiszPrater criterion was considered as a theoretical approach, which was calculated from Eq. (3) and dictates that if value is < 1, the intraparticle mass diffusional limitation can be negligible [34]:

=

robs CTS

2 PRp

(3)

De CG

The observed rate of reaction (robs ) was determined at the initial stage (0.25 min) when it can be considered that no significant amounts of products are formed. It represents the glycerol moles (limiting reactant) converted per unit of both catalyst active sites mmoles and time. The effective diffusivity (De ) is defined as: Table 1 Main characteristics of the Dowex Monosphere 650C ion exchange resin.

2. Experimental

Dowex Monosphere 650C

2.1. Catalytic reactions and samples analysis

Matrix* Functional group* Concentration of active sites (CTS ) [31] Standard ionic form Particle density* Shipping density* Maximum operating temperature pH range Particle diameter [31] Porosity [32] Thermal conductivity ( S ) [33]

Glycerol esterification tests were performed in a glass batch reactor with a reflux condenser, a temperature control system and a magnetic stirrer. The reactants were glycerol (Cicarelli, 99.9%) and acetic acid (Anedra, Glacial 99.8%). The commercial ion exchange resin Dowex Monosphere 650C (Dow Chemical Co) was used as heterogeneous acid catalyst and their main characteristics are shown in Table 1. Previous to the reaction tests, the polymeric resin was pretreated at 353 K under vacuum conditions for 24 h to remove the residual moisture. The reaction tests were conducted for 6 h, at temperature from 353 to 393 K, using initial AA:G molar ratio (MR) between 3:1 and

* Data provided by the manufacturer. 3

Styrene/divinylbenzene gel Sulfonic acid 4.8 mmol g−1 H+ 1.22 g ml−1 785 g l−1 403 K 0–14 < 100 µm 0.61 0.2769 W m−1 K−1

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

and viscosity are expressed in K and mPa s [41]. Additionally, to determine the absence of heat diffusional limitation inside the catalyst pores, Prater number ( ) [34] was calculated from Eq. (9). Taking into account that only Reaction (1) presents exothermic behavior (reactions (2) and (3) are endothermic) [37], was calculated at the reaction initial stage (conservative value).

=

Tmax T = T

Hr De CG

(9)

e

where e corresponds to catalyst effective thermal conductivity, estimated from the following equation: e

=

S

S

(10)

f

Reaction (1) enthalpy ( Hr ) was estimated considering products and reactants standard formation enthalpies (298.15 K, 1 atm), corrected by temperature and considering the calorific capacity of each component [39,40,42,43]: T

Fig. 1. Experimental setup for the catalytic reaction tests.

De =

L

Cpi

(11)

m

D

Hr (298.15K ) =

(4)

KT µAA VG1/3

i

Hi0

(12)

i=1

Product and reactant property values used in Eqs. (11) and (12) are displayed in the supplementary material (Table A3). Thermal conductivity of the reactant mixture was calculated from Filippov and Novoselova method [44], based on the mass fraction of the binary mixture: f

=

AA yAA

+

G yG

0.72 |

AA

G | yAA yG

(13)

Glycerol and acetic acid pure species thermal conductivity was taken from the literature [45].

(5)

where:

K = 8.210

i

298.15K i = 1

Tortuosity ( ) was calculated considering the porosity ( ) inverse, taking into account the catalyst material [35,36] and the catalyst porosity was taken from the literature [32]. As acetic acid is the prevailing component in all experiments, the glycerol molecular diffusion coefficient (D ) was estimated using the Scheibel relation, approximating it as the binary diffusion coefficient of the glycerol in acetic acid according to the following equation [13,37]:

D=

m

Hr (T ) = Hr (298.15K ) +

2.3. Kinetic modeling 8

1+

3VAA VG

2/3

Glycerol esterification with acetic acid is a consecutive reaction involving three reversible steps: conversion of glycerol to MAG, MAG to DAG and DAG to TAG with a simultaneous water production as byproduct according to the following subsequent reactions [19,46]:

(6)

Viscosity is given in mPa.s, temperature in K and molar volumes in cm3 mol−1. In addition, the solute and solvent molar volumes (VG and VAA ) were calculated considering the Tyn and Calus method [38], according to the following expression:

G + AA

k1/ k 1

(7)

MAG + AA

where critical molar volume VC, G and VC, AA values correspond to 250 and 170 ml mol−1, respectively [39,40]. Acetic acid viscosity ( µAA ) was determined according to Vogel equation:

DAG + AA

Vi = 0.285VC1.048 ,i

B

µAA = e A + C + T

MAG + W (Reaction1)

k2/ k 2

k3/ k 3

DAG + W (Reaction2)

TAG + W (Reaction3)

In this work, the reactor was modeled as an ideal batch, considering: - Perfect mixture of reactants and product in the bulk reactor. - Heterogeneous model without intraparticle and extraparticle diffusional resistances. - Isothermal reactor.

(8)

Particularly for acetic acid, A, B and C parameters correspond to −4.04077, 1118.6 and −29.5037, respectively, and the temperature Table 2 Assumptions made for LHHW, ER and PL models. Model

Assumptions made

LHHW

(i) Both reactants (AA-G, AA-MAG and AA-DAG) are adsorbed on catalyst surface. (ii) Reaction occurred between both adsorbed reactants on surface producing adsorbed AG and water molecules. (iii) All diffusion process are considered relatively rapid compared with the surface reaction between adsorbed reactants. (i) Only one reactant is adsorbed on the catalysts surface (AA). (ii) Reaction occurred between adsorbed reactant with another reactant from the bulk (G, MAG or DAG) producing an adsorbed AG molecule and non-adsorbed water. (iii) All diffusion steps are rapid and considered relatively fast compared with surface reaction. (i) Pseudo-homogeneous second-order reversible. (ii) First order respect each reactive and product.

ER PL

4

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D.M. Reinoso and D.E. Boldrini

Regarding kinetic modeling, different heterogeneous models for the glycerol esterification based on Langmuir-Hinshelwood-HougenWatson (LHHW), Eley-Rideal (ER) and Power Law (PL) approaches were performed. Table 2 presents the assumptions considered for each one of the models aforementioned. Thereby, for a well-mixed batch reactor, the mass balances of the species involved in the reaction can be expressed as follows:

dCG = w ( r1) dt

k LHHW = 1

k LHHW = 2 k LHHW = 3

(14)

dCAA = w ( r1 dt

r2

dCMAG = w (r1 dt

r2)

(16)

dCDAG = w (r2 dt

r3)

(17)

r3)

(18)

dCW = w (r1 + r2 + r3) dt

(19)

CS =

k LHHW KMAG CMAG KW CW CS2 1

CS =

k LHHW KDAG CDAG KW CW CS2 2

(21)

r3 = k3LHHW KDAG CDAG KAA CAA CS2

k LHHW KTAG CTAG KW CW CS2 3

(22)

CS =

CTS 1 + KG CG + KAA CAA + KMAG CMAG + KDAG CDAG + KTAG CTAG + KW CW (23)

where:

k LHHW = 1

Eq . k1LHHW K G CGEq.KAA CAA Eq . KMAG CMAG KW CWEq.

k LHHW = 2

Eq . Eq . k 2LHHW KMAG CMAG KAA CAA Eq . KDAG CDAG KW CWEq .

k LHHW = 3

Eq . Eq . k3LHHW KDAG CDAG KAA CAA Eq . KTAG CTAG KW CWEq .

(24) (25) (26)

2 k1LHHW = k1LHHW K G KAA CTS

(27)

2 k 2LHHW = k 2LHHW KMAG KAA CTS

(28)

k3LHHW

(29)

=

(31)

Eq . Eq . k3LHHW CDAG CAA

(32)

Eq . Eq . CTAG CW

CTS 1 + K G CG + KW CW +KAA CAA

CTS 1 + K G CG + KW CW

(33)

(34)

CTS 1 + KW CW

r1 = k1ER CG KAA CAA CS

Grouping constants, the following is obtained:

2 k3LHHW KDAG KAA CTS

Eq . CDAG CWEq .

(35)

2.3.2. Eley-Rideal (ER) model Regarding Eley-Rideal mechanism, previous studies [51,52] have proposed that the esterification reaction can be carried out between the adsorbed alcohol molecule on the active sites and the carboxylic acid from the liquid phase. Nevertheless, although high glycerol polarity allows it to adsorb on the catalyst active sites, the produced protonated nucleophile seems not to be an active species for the esterification reaction [53]. Similarly, the high polarity of the water molecule allows it to be adsorbed on the catalyst surface, but it is not considered in the reaction mechanism. Thus, most authors [32,37,51,53,54,55] indicate that the single-site mechanism with the adsorbed carboxylic acid is the most precise one. In this way, Eley-Rideal (ER) model considers that both reactants diffuse from the liquid phase bulk to the catalyst external surface, nevertheless, only the acetic acid is adsorbed on the catalyst surface. The surface reaction is the limiting stage and the esterification takes place between the adsorbed acetic acid and the glycerol from the bulk, producing an adsorbed MAG molecule and non-adsorbed water. DAG and TAG synthesis are produced from an analogous to MAG mechanism and the reaction cycle concludes with the esters desorption and diffusion to the liquid phase bulk. Eley-Rideal model rate expressions can be described by:

CS =

Eq . Eq . k 2LHHW CMAG CAA

Furthermore, an additional model (LHHW-III) which only contemplates the term related to the water adsorption according to Faria et al. [50] was explored. Eq. (35) details the free active sites concentration term according to this model:

(20)

r2 = k 2LHHW KMAG CMAG KAA CAA CS2

(30)

The model developed according to this simplification was denoted as LHHW-I. In addition, a second model (LHHW-II) in which the acetic acid adsorption term was disesteem owing to the less affinity for the catalyst active sites respect to the glycerol and water molecules was studied [47,48]. Consequently, it is possible to represent the free active sites concentration on the catalyst surface as follows:

2.3.1. Langmuir-Hinshelwood-Hougen-Watson (LHHW) model Previous studies [33,47,48,49] established that the best experimental data fitting for carboxylic acids esterification using different alcohols corresponds to a dual-site model. Thereby, Langmuir-Hinshelwood-Hougen-Watson (LHHW) model includes multiple stages. First, both glycerol and acetic acid molecules diffuse from the liquid phase bulk to the catalyst external surface, subsequently, both species are adsorbed. In the second stage, the adsorbed molecules on the catalyst surface react with each other to produce adsorbed MAG and water. In addition, DAG and TAG are produced from an analogous mechanism to MAG. These steps are considered as the global process rate-controlling. Finally, the products are desorbed from the catalyst surface and diffuse to liquid phase bulk. The rate expressions for Langmuir-Hinshelwood-Hougen-Watson model can be described as follows:

r1 = k1LHHW K G CG KAA CAA CS2

Eq . CMAG CWEq.

Considering that the polymeric resin with sulfonic acid functional group presents a strong affinity to the most polar molecules (water, glycerol, and acetic acid), previous reports [47,48] propose rejecting AG adsorption terms in Eq. (23). Therefore, the terms associated with the free active sites concentration on the catalyst surface (CS ) can be represented according to the following expression:

(15)

dCTAG = w (r3) dt

Eq . k1LHHW CGEq .CAA

k ER 1 KMAG CMAG CW CS

(36)

r2 = k 2ER CMAG KAA CAA CS

k ER 2 KDAG CDAG CW CS

(37)

r3 = k3ER CDAG KAA CAA CS

k ER 3 KTAG CTAG CW CS

(38)

where:

k ER 1 = 5

Eq . k1ER CGEq .KAA CAA Eq . KMAG CMAG CWEq .

(39)

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

Table 3 i values for the kinetic models studied. Kinetic model LHHW-I LHHW-II LHHW-III ER-I ER-II ER-III PL

Table 4 Kinetic parameters fitted for ER-II, LHHW-II and PL models with the corresponding confidence intervals of 95%.

i

Parameter

210 263 1170 4 0 1046 914

ER-II Model

Eq . KDAG CDAG CWEq .

2 −1 k 2ER 0 [ml mmol ER 2 k30 [ml mmol−1 E1ER /R [K] E2ER /R [K] E3ER /R [K] −1 K GER 0 [ml mol ] −1 ER [ml mol ] KW 0 HGER/R [K] ER HW / R [K]

(40)

Eq . Eq . k3ER CDAG KAA CAA

k ER 3 =

Eq . Eq . KTAG CTAG CW

(41) (42)

k 2ER = k 2ER KAA CTS

(43)

k3ER = k3ER KAA CTS

(44) (45)

Eq . CDAG CWEq .

(46)

Eq . Eq . CTAG CW

mol

]

s

3.07 ± 0.037 0.24 ± 0.003 5550 ± 79 5838 ± 87 5893 ± 100 417 ± 12 569 ± 11 −5933 ± 170 −1442 ± 150

−1

mol

s

(47)

The free active sites concentration on the catalyst surface (CS ) can be represented taking into account the same considerations established for the LHHW model:

−1

]

mol−1 s−1]

10.31 ± 0.064 3.06 ± 0.020 0.29 ± 0.002 4043 ± 40 4438 ± 48 3869 ± 58 214 ± 3.34 230 ± 2.27 −4116 ± 98 −3515 ± 65

PL Model

Eq . Eq . k3ER CDAG CAA

k ER 3 =

]

−1

s

−1

k 2LHHW [ml2 mmol−1 0 LHHW k 30 [ml2 mmol−1 E1LHHW /R [K] E2LHHW / R [K] E3LHHW /R [K] [ml mol−1] K GLHHW 0 LHHW [ml mol−1] KW0 HGLHHW / R [K] LHHW HW /R [K]

Eq . Eq . k 2ER CMAG CAA

k ER 2 =

mol

−1

k1LHHW [ml2 mmol−1 mol−1 s−1] 0

k1ER = k1ER KAA CTS

k ER 1 =

−1

LHHW-II Model

Grouping constants, the following is obtained:

Eq . k1ER CGEq .CAA Eq . CMAG CWEq .

9.31 ± 0.098

2 −1 k1ER mol−1 s−1] 0 [ml mmol

Eq . Eq . k 2ER CMAG KAA CAA

k ER 2 =

Value

2 −1 mol−1 s−1] k1PL 0 [ml mmol

4.22 ± 0.007

2 −1 k 2PL 0 [ml mmol 2 PL k30 [ml mmol−1 E1PL/ R [K] E2PL/R [K] E3PL/R [K]

7185 ± 13

−1

mol

−1

mol

s

−1

]

s

−1

]

0.95 ± 0.002 0.073 ± 0.00018 7083 ± 21 7857 ± 24

CTS 1 + K G CG + KW CW +KAA CAA

(48)

k PL3 =

CS =

CTS 1 + K G CG + KW CW

(49)

CS =

CTS 1 + KW CW

(50)

For all the models proposed in this study, the kinetic constants ki and the adsorption equilibrium constants Ki depend on temperature following the Arrhenius and Vańt Hoff equations respectively according to:

CS =

Eq . Eq . k3PL CDAG CAA Eq . Eq . CTAG CW

These models were named as ER-I, ER-II and ER-III, respectively.

ki = ki0 e

2.3.3. Power Law (PL) model Additionally, previous studies [19,46,56,57] have proposed Power Law kinetic model to correlate the experimental data. In this work, Power Law model has been proposed considering a first-order dependence for all the reactants and products. Power Law model rate expressions can be described by:

r1 = k1PL CG CAA

k PL1 CMAG CW

Ki = K i 0 e

k PL2 CDAG CW

(52)

r3 = k3PL CDAG CAA

k PL3 CTAG CW

(53)

k PL2 =

Eq . k1PL CGEq .CAA Eq . CMAG CWEq .

(54)

Eq . Eq . k2PL CMAG CAA Eq . CDAG CWEq .

(58)

2.3.4. Mathematical tools In order to solve the algebraic and differential equations and fit the experimental data, the advanced modeling software GProms (Process Systems Enterprise Ltd.) under academic license was employed. It is based on the statistical method of maximum likelihood estimation. The used objective function was minimized in order to maximize the mathematical model fitting and achieve a good experimental data prediction [59]:

where:

k PL1 =

Hi RT

(57)

Reverse reactions rate constants for all the models presented were calculated as a function of the forward reactions rate constants at the equilibrium (reaction final state), taking into account their thermodynamic consistency (elementary reactions) according to Eqs. (30)–(32), (45)–(47), and (54)–(56) described above [58].

(51)

r2 = k 2PL CMAG CAA

Ei RT

(56)

(55) 6

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

Objective function (dimensionless)

45000 40000 35000 30000 25000 20000 15000 10000 5000 0

A

B

C

D

E

F

G

H

I

J

K

Parameter Fig. 2. Sensitivity analysis of calculated parameters for ER-II model. Ref.: +20% perturbations (blue), −20% perturbations (orange), A = 0% perturbation (red), B=E1ER / R , C=E2ER / R , D=E3ER / R , E= HGER/ R , F = HWER/ R , G=k1ER , H=k 2ER , I=k 3ER , J=K GER , K=K WER0 . (For interpretation of the references to colour in this figure 0 0 0 0 legend, the reader is referred to the web version of this article.)

order to establish if the set of parameters determined correspond to the global minimum and not to local minima. For each parameter involved in the aforementioned models, +/- 20% perturbations were made keeping the others parameters constant reevaluating . If all perturbations in all the parameters give the minimum with their original values (0% perturbation), then the global minimum has been achieved [63].

Table 5 Glycerol conversion and acetylglycerides selectivity for the glycerol esterification reaction with acetic acid using Dowex 650C as catalyst. Run

T [K]

ω [%]

MR

XG [%]

SMAG [%]

SDAG [%]

STAG [%]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

373 353 353 393 393 353 353 393 393 373 373 353 393 373 373

6 4 4 4 4 8 8 8 8 6 6 6 6 4 8

6 3 9 3 9 3 9 3 9 3 9 6 6 6 6

98 91 99 92 99.6 91.6 99.3 93 99.7 93 99.6 98.3 98.8 98.6 98.6

21 41 18 38 11 41 15 36 12 37 13 23 20 21 20

57 54 69 52 52 52 63 53 52 53 56 65 55 60 57

22 5 13 10 37 7 22 11 36 10 31 12 25 19 23

=

N 1 ln (2 ) + min 2 2

NE NVi NMij

ln ( i=1 j=1 k=1

2 ijk )

+

(z ijk

3. Results and discussion Regarding eventual heat transfer limitations, the tests performed evidenced no temperatures changes within the liquid phase bulk during the reaction runs. Also, the highest intraparticle temperature gradient calculated according to Prater criterion [34] was 0.7 K, evidencing the absence of intraparticle heat diffusional limitations. Furthermore, no concentration changes were observed on the reactor volume and WeiszPrater number calculated for all the reaction conditions was between 0.05 and 0.55, displaying the mass transfer limitations absence. Hence, it is possible to asseverate the reactor isothemicity and that the reaction tests were performed under kinetic control. The i values for the different proposed models are presented in Table 3. The obtained results evidence that ER-II model presented the minimum AIC value, indicating that this model represents the experimental data with the highest precision. The determined kinetic parameters from ER-II, LHHW-II and PL models are shown in Table 4. The obtained parameters for the rest of the explored models are displayed in the supplementary material (Table A4). Fig. 2 shows the sensitivity analysis performed for ER-II model. As can be seen, it is clear that the estimated parameters correspond to the global minimum since at +/- 20% perturbations the values found are bigger than at 0% perturbation. Analogously, the same behavior was found in the rest of the proposed models. The corresponding figures to these models are presented in the supplementary material (Figures A1A6). As can be seen, the obtained fitting with ER-II model indicates that the glycerol esterification with acetic acid using the ion exchange resin Dowex Monosphere 650C as catalyst follows the same mechanistic steps as homogeneous acid-catalysts, as previously proposed [64]. The heterogeneously catalyzed reaction mechanism has been extensively studied, highlighting the essential function of the Brönsted

z ijk ) 2 2 ijk

(59)

A relative constant variance of 0.02 was considered, corresponding to the typical error associated with the analytical technique used to quantify the reaction sample composition [60]. To perform a direct comparison of the different models fitting goodness, Akaike Information Criterion (AIC) was used [61]. AIC, defined in Eq. (60), provides a means for model selection. A good model is the one that has minimum AIC among all the other models [62]:

AICi = nlog

2 i

n

+ 2K

(60)

To compare the models, Burnham and Anderson [62] recommend to considerer the AIC differences ( i ) given by: i

= AICi

AICmin.

(61)

If the i value is between 0 and 2 the model empirical support level is substantial, between 4 and 7 the level is considerably less and > 10 essentially none. For the proposed models, a sensitivity analysis was performed in 7

Fuel 264 (2020) 116879

Concentration (mol/mL)

Concentration (mol/mL)

0.004

(a)

0.0026 0.0024 0.0022 0.002 0.0018 0.0016 0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

Concentration (mol/mL)

D.M. Reinoso and D.E. Boldrini

0.0022 0.002 0.0018 0.0016 0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

(a)

0.0036 0.0032 0.0028 0.0024 0.002 0.0016 0.0012 0.0008 0.0004 0

0

30

60

90

120 150 180 210 240 270 300 330 360

Time (min)

(b)

Concentration (mol/mL)

0.0022

(b)

0.002 0.0018 0.0016 0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

0

30

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90

120 150 180 210 240 270 300 330 360

Time (min)

0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

30

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90 120 150 180 210 240 270 300 330 360

0

30

60

90 120 150 180 210 240 270 300 330 360

0

30

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90 120 150 180 210 240 270 300 330 360

Time (min)

Time (min)

0.0016 0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

0

30

0.0018

(c) Concentration (mol/mL)

(c)

Concentration (mol/mL)

0.0016

0

Time (min)

120 150 180 210 240 270 300 330 360

Time (min)

G

MAG

DAG

G

MAG

DAG

TAG

TAG Fig. 4. Product distribution of glycerol esterification with acetic acid employing 6:1 molar ratio, 6 wt% catalyst loading and different temperature: 353 (a), 373 (b) and 393 K (c).

Fig. 3. Product distribution of glycerol esterification with acetic acid at 373 K employing 6 wt% catalyst loading and using different molar ratio: 3:1 (a), 6:1 (b) and 9:1 (c).

catalysts, respectively. For this comparison, the highest values of the kinetic constant found correspond to Dowex Monosphere 650C and Amberlyst-15 catalysts. It has been previously reported that the catalyst activity in the glycerol acetylation reaction is directly related to the acid strength, moreover, previous studies indicated that Brönsted acidity results in superior glycerol conversion and selectivity to DAG and TAG compared with Lewis acids [13]. Additionally, it has been suggested that the configuration and spatial arrangement of the surface acid moieties dominantly affect the catalyst performance, finding high conversion and selectivity in ion exchange resins [68]. Equilibrium constants, defined as the relationship between forward and backward rate constants were in the magnitude order of 100, 10-1 and 10-2 for the Reaction (1)–(3), respectively, which are consistent with reported data [37,58]. The low values found indicate that glycerol esterification with acetic acid is thermodynamically resisted as has been previously reported, evidencing the strong reversibility particularly in Reaction (3) [14,69]. MAG prevails over DAG and TAG formation because glycerol has the most reactive hydroxyl groups and no acetyl residue although acetic acid can react with G, MAG and DAG. In fact, acetyl residue can sterically interfere with the surface reaction, making the esterification reaction of acetic acid with glycerol to yield MAG proceed predominantly over the undesired consecutive esterification to DAG and TAG [58].

acid sites [21,25,65,66]. After acetic acid adsorption on the catalyst surface, the double bond of the oxygen atom with a lone pair electrons links to the proton of the catalyst Brönsted acid site generating positively charged oxygen. The positive charge is subsequently transferred to the carbon through resonance effect. Thus, the positively charged carbon atom facilitates the nucleophilic attack by the glycerol hydroxyl group accompanied by the removal of a water molecule, eventually leading to the deprotonated ester formation and yielding the respective mono-, di- and triacetylglycerol [67]. ER ER The rate constant values k1ER > k2ER > k3ER and k ER 1 > k 2 > k 3 were in the order and in agreement with a previous report [37]. Besides, k1ER values for the different models were divided by (1 + K G CG + KW CW ) in order to compare the bibliographic data with the proposed models in this work. The obtained values were in the magnitude order of 101 h−1 which are consistent with reported data. Rane et al. [56] found a constant value of 2.1 h−1 at 373 K for a pseudofirst-order kinetic model for the glycerol esterification with acetic acid over alumina-based catalyst. Moreover, Zhou et al. [19] obtained a value of 2.5 h−1 (373 K) assuming a homogeneous first-order system for the acetylglycerides synthesis using Amberlyst-15. Similarly, Veluturla et al. [57] and Khayoon et al. [28] found values of 0.98 and 1.08 h−1 employing SO42-/CeO2-Al2O3 and yttrium containing SBA-3 as 8

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

Thus, the product between KAA CAA was in the order of the magnitude of 10-5, indicating that this term can be dismissed in Eqs. (33) and (48). Also, ER-II model showed better fitting than ER-I and LHHW-I models ER ER / R terms have higher and the confidence interval of K AA and HAA 0 magnitude order than the fitted parameters, indicating that the sensitivities of those constants are low. ER-III and LHHW-III models displayed lower fitting than ER-II, indicating that this assumption is not valid. Table 5 presents the obtained glycerol conversion and acetylglycerides selectivity for the glycerol esterification reaction with acetic acid using Dowex 650C as acid catalyst at 6 h reaction time. Figs. 3–5 show the fitted results for different reaction conditions. As can be seen, the attained fitting properly represents the experimental data and the fitting achieved for all the reactions sets are validated by the parity plot (Fig. 6). Table 5 shows that MR increases for all the reactions sets, keeping the other variables fixed generates an enhancement in the combined selectivity to DAG + TAG with the consequent MAG selectivity reduction (Runs 1–10-11, 2–3, 4–5, 6–7, 8–9). This fact can be explained by the extra esterification agent which shifts the esterification reaction towards the formation of higher glycerol acetates [70]. Fig. 3 shows the experimentally obtained products distribution and the fitted values by ER-II model considering differents MR (T = 373 K, ω = 6%). Glycerol concentration rapidly declines for all test during the first 30 min. However, as MR increases, the conversion stabilization required time decreases. For the analyzed conditions and molar ratios of 3:1, 6:1 and 9:1, the conversion stabilization required time was 180, 300 and 330 min, respectively. Regarding the temperature effect on the reaction kinetics, Fig. 4 (MR = 6:1, ω = 6%) shows that temperature increase from 353 K to 393 K improves the glycerol conversion and product distribution equilibrium values are reached in a shorter time. Temperature increase allows that the final selectivity to MAG (equilibrium condition) decrease and TAG selectivity increase. This fact is attributable to MAG synthesis reaction exothermicity and TAG formation reaction endothermic nature [71]. In this way, temperature increment directly affects products distribution, shifting the reactions thermodynamic equilibrium to DAG esterification to produce TAG and the MAG hydrolysis. Furthermore, catalyst loading changes keeping the other variables fixed, generate no variations in the conversion or products distribution at 6 h reaction time as expected (Runs 1–14-15, 2–6, 3–7, 4–8, 5–9). The reaction final state (equilibrium) is directly related to the system thermodynamics and not to its kinetics (dynamic state), which depends

0.0018

Concentration (mol/mL)

(a)

0.0016 0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

0

30

60

90 120 150 180 210 240 270 300 330 360

0

30

60

90 120 150 180 210 240 270 300 330 360

Time (min)

0.0016

Concentration (mol/mL)

(b)

0.0014 0.0012 0.001 0.0008 0.0006 0.0004 0.0002 0

Time (min) G

MAG

DAG

TAG

Fig. 5. Product distribution of glycerol esterification with acetic acid at 353 K employing 9:1 molar ratio and catalyst loading of 4 (a) and 8 wt% (b).

Additionally, the adsorption equilibrium constants (KW and K G ) and water and glycerol highest concentration were in the magnitude order of 102 ml mol−1 and 10-3 mol ml−1, respectively. Therefore, the product between adsorption equilibrium constant and water and glycerol highest concentration were in the order of the magnitude of 100 y 10-1, respectively. These results indicate that these terms cannot be neglected in Eq. (48). Moreover, it is important to highlight that water adsorption equilibrium constant was higher than the glycerol constant due to the water molecule highest affinity with the acid active site as a result of its elevated polarity [37,48,58]. Likewise, the estimated adsorption equilibrium constant of acetic acid from ER-I and LHHW-I models (Appendix A) were in the magnitude order of 10-4 ml mol−1 and the highest AA concentration was in the magnitude order of 10-1 ml mol−1.

0.0045

Model Prediction (mol/mL)

0.004 0.0035 0.003 0.0025 0.002 0.0015 0.001 G

0.0005 0

0

0.0005

0.001

0.0015

0.002

0.0025

MAG

0.003

DAG

0.0035

TAG

0.004

Experimental Masurement (mol/mL) Fig. 6. Parity plot of the calculated vs. experimental concentrations for ER-II model. 9

0.0045

Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini

on the catalyst loading. On the other hand, an increase in catalyst loading leads to higher reaction rate and shorter time to achieve a given conversion (Fig. 5). Moreover, it was determined that the activation energies of the forward reactions are in the order E1ER < E2ER < E3ER . Higher activation energy values imply that the reaction is more sensitive to temperature changes. Thus, the found values suggest that the DAG to TAG reaction is the most sensitive to temperature changes, followed by the MAG to DAG and G to MAG reactions.

[7] Esteban J, Domínguez E, Ladero M, García-Ochoa F. Kinetics of the production of glycerol carbonate by transesterification of glycerol with dimethyl and ethylene carbonate using potassium methoxide, a highly active catalyst. Fuel Process Technol 2015;138:243–51. [8] Esteban J, Fuente E, Blanco A, Ladero M, García-Ochoa F. Phenomenological kinetic model of the synthesis of glycerol carbonate assisted by focused beam reflectance measurements. Chem Eng J 2015;260:434–43. [9] Ravelo M, Fuente E, Blanco Á, Ladero M, García-Ochoa F. Esterification of glycerol and ibuprofen in solventless media catalyzed by free CALB: kinetic modeling. Biochem Eng J 2015;101:228–36. [10] Gonçalves VLC, Pinto BP, Silva JC, Mota CJA. Acetylation of glycerol catalyzed by different solid acids. Catal Today 2008;133:673–7. [11] Ferreira P, Fonseca IM, Ramos AM, Vital J, Castanheiro JE. Acetylation of glycerol over heteropolyacids supported on activated carbon. Catal Commun 2011;12(7):573–6. [12] Liao X, Zhu Y, Wang SG, Li Y. Producing triacetylglycerol with glycerol by two steps: esterification and acetylation. Fuel Process Technol 2009;90(7–8):988–93. [13] Okoye PU, Abdullah AZ, Hameed BH. A review on recent developments and progress in the kinetics and deactivation of catalytic acetylation of glycerol-A byproduct of biodiesel. Renew Sust Energ Rev 2017;74:387–401. [14] Liao X, Zhu Y, Wang SG, Chen H, Li Y. Theoretical elucidation of acetylating glycerol with acetic acid and acetic anhydride. Appl Catal, B 2010;94(1–2):64–70. [15] Delgado PJ. European Patent 1 331 260, 2008. [16] Casas A, Ruiz JR, Ramos MJ, Perez A. Effects of triacetin on biodiesel quality. Energy Fuels 2010;24(8):4481–9. [17] Zhu S, Zhu Y, Gao X, Mo T, Zhu Y, Li Y. Production of bio-additives from glycerol esterification over zirconia supported heteropolyacids. Bioresour Technol 2013;130:45–51. [18] Zhou L, Al-Zaini E, Adesina AA. Catalytic characteristics and parameters optimization of the glycerol acetylation over solid acid catalysts. Fuel 2013;103:617–25. [19] Zhou L, Nguyen TH, Adesina AA. The acetylation of glycerol over amberlyst-15: Kinetic and product distribution. Fuel Process Technol 2012;104:310–8. [20] Kale S, Umbarkar SB, Dongare MK, Eckelt R, Armbruster U, Martin A. Selective formation of triacetin by glycerol acetylation using acidic ion-exchange resins as catalyst and toluene as an entrainer. Appl Catal A 2015;490:10–6. [21] Jagadeeswaraiah K, Balaraju M, Prasad PS, Lingaiah N. Selective esterification of glycerol to bioadditives over heteropoly tungstate supported on Cs-containing zirconia catalysts. Appl Catal A 2010;386(1–2):166–70. [22] Patel A, Singh S. A green and sustainable approach for esterification of glycerol using 12-tungstophosphoric acid anchored to different supports: kinetics and effect of support. Fuel 2014;118:358–64. [23] Gonçalves CE, Oliveira Laier LO, Cardoso AL, da Silva MJ. Bioadditive synthesis from H3PW12O40-catalyzed glycerol esterification with HOAc under mild reaction conditions. Fuel Process Technol 2012;102:46–52. [24] Ferreira P, Fonseca IM, Ramos AM, Vital J, Castanheiro JE. Glycerol acetylation over dodecatungstophosphoric acid immobilized into a silica matrix as catalyst. Appl Catal, B 2009;91(1–2):416–22. [25] Melero JA, Van Grieken R, Morales G, Paniagua M. Acidic mesoporous silica for the acetylation of glycerol: synthesis of bioadditives to petrol fuel. Energy Fuels 2007;21(3):1782–91. [26] Kim I, Kim J, Lee D. A comparative study on catalytic properties of solid acid catalysts for glycerol acetylation at low temperatures. Appl Catal, B 2014;148:295–303. [27] Dalla Costa BO, Decolatti HP, Legnoverde MS, Querini CA. Influence of acidic properties of different solid acid catalysts for glycerol acetylation. Catal Today 2017;289:222–30. [28] Khayoon MS, Triwahyono S, Hameed BH, Jalil AA. Improved production of fuel oxygenates via glycerol acetylation with acetic acid. Chem Eng J 2014;243:473–84. [29] Reddy PS, Sudarsanam P, Raju G, Reddy BM. Synthesis of bio-additives: Acetylation of glycerol over zirconia-based solid acid catalysts. Catal Comm 2010;11(15):1224–8. [30] Arun P, Satyanarayana SM, Prakash P. Acetylation of glycerol over sulfated alumina: reaction parameter study and optimization using response surface methodology. Energy Fuels 2016;30(1):584–93. [31] Reinoso DM, Tonetto GM. Bioadditives synthesis from selective glycerol esterification over acidic ion exchange resin as catalyst. J Environ chem Eng 2018;6(2):3399–407. [32] Bart HJ, Kaltenbrunner W, Landschützer H. Kinetics of esterification of acetic acid with propyl alcohol by heterogeneous catalysis. Int J Chem kinet 1996;28(9):649–56. [33] Pankajakshan A, Pudi SM, Biswas P. Acetylation of Glycerol over Highly Stable and Active Sulfated Alumina Catalyst: Reaction Mechanism, Kinetic Modeling and Estimation of Kinetic Parameters. Int J Chem kinet 2018;50(2):98–111. [34] Fogler HS. Elements of Chemical Reaction Engineering. 4th ed. New Jersey: Prentice Hall; 2006. [35] Dogu T, Aydin E, Boz N, Murtezaoglu K, Dogu G. Diffusion resistances and contribution of surface diffusion in TAME and TAEE production using Amberlyst-15. Inter J Chem React Eng 2002;1(1).. [36] Orjuela A, Yanez AJ, Santhanakrishnan A, Lira CT, Miller DJ. Kinetics of mixed succinic acid/acetic acid esterification with Amberlyst 70 ion exchange resin as catalyst. Chem Eng J 2012;188:98–107. [37] Gelosa D, Ramaioli M, Valente G, Morbidelli M. Chromatographic reactors: esterification of glycerol with acetic acid using acidic polymeric resins. Ind Eng Chem Res 2003;42(25):6536–44. [38] Poling BE, Prausnitz JM, ÓConnell JP. The Properties of Gases and Liquids. 4th ed. New York: McGraw-Hill; 2001.

4. Conclusion In this work, a kinetic study of glycerol esterification with acetic acid using the DOWEX 650C Monosphere ion exchange resin as catalyst was realised. The performed study determined that Eley Rideal model represents the experimental data with the highest precision. In this model, it was considered that the reaction on the catalytic surface is the controlling stage in which the acetic acid adsorbed on the catalyst active sites reacts with the glycerol and the AG from the liquid phase bulk. Likewise, it was determined that only the adsorption of glycerol and water are representative in the kinetic expression denominator due to the greater affinity of these compounds with the ion exchange resin generated by their highest polarity. Moreover, the developed mathematical model is a useful and versatile tool to propose reaction strategies defining reaction conditions (AA:G molar ratio, temperature, time, catalyst loading) in order to attain a determined product distribution minimizing process costs which could be compared with other technologies. Credit authorship contribution statement D.M. Reinoso: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - review & editing, Project administration, Funding acquisition. D.E. Boldrini: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - review & editing, Project administration. Acknowledgments The authors thank the Consejo Nacional de Investigaciones Científicas y Técnicas (National Council for Scientific and Technological Research, CONICET, Argentina) and the Agencia Nacional de Promoción Científica y Tecnológica (National Agency of Scientific and Technological Promotion, Argentina)-PICT 2014-3211 for the financial support. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.fuel.2019.116879. References [1] Hajjari M, Tabatabaei M, Aghbashlo M, Ghanavati H. A review on the prospects of sustainable biodiesel production: a global scenario with an emphasis on waste-oil biodiesel utilization. Renew Sust Energ Rev 2017;72:445–64. [2] Rahmat N, Abdullah AZ, Mohamed AR. Recent progress on innovative and potential technologies for glycerol transformation into fuel additives: a critical review. Renew Sust Energ Rev 2010;14(3):987–1000. [3] Quispe CAG, Coronado CJR, Carvalho Jr JA. Glycerol: Production, consumption, prices, characterization and new trends in combustion. Renew Sust Energ Rev 2013;27:475–93. [4] Bagheri S, Julkapli NM, Yehye WA. Catalytic conversion of biodiesel derived raw glycerol to value added products. Renew Sust Energ Rev 2015;41:113–27. [5] Luo X, Ge X, Cui S, Li Y. Value-added processing of crude glycerol into chemicals and polymers. Bioresour Technol 2016;215:144–54. [6] Johnson DT, Taconi KA. The glycerin glut: Options for the value-added conversion of crude glycerol resulting from biodiesel production. Environ Progress 2007;26(4):338–48.

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Fuel 264 (2020) 116879

D.M. Reinoso and D.E. Boldrini [39] Cheméo Home Page. Chemical Properties of Glycerol, https://www.chemeo.com/ cid/16-205-6/Glycerol%2C%20triformate; 2019 [accessed 12 September 2019]. [40] Cheméo Home Page. Chemical Properties of Acetic acid, https://www.chemeo. com/cid/51-574-8/Acetic%20acid; 2019 [accessed 12 September 2019]. [41] DDBST Gmbh Home Page. Liquid Dynamic Viscocity Calculation by Vogel Equation (Acetic Acid), http://ddbonline.ddbst.de/VogelCalculation/VogelCalculationCGI. exe?component=Acetic+acid; 2019 [acceseed 12 September 2019]. [42] Perry RH, Green DW. Perrýs Chemical Engineerś Handbook. 7th ed. New York: McGraw-Hill; 1997. [43] Cheméo Home Page. Chemical Properties of Glycerol monoacetate, https://www. chemeo.com/cid/84-107-0/Glycerol%20monoacetate; 2019 [accessed 12 September 2019]. [44] Filippov LP. Liquid thermal conductivity research at Moscow University. Int J Heat Mass Transfer 1968;11(2):331–45. [45] Vargaftik NB. Tables on the Thermophysical Properties of Liquids and Gases. 2nd ed. Washington, DC: Hemisphere Publishing Corporation; 1975. [46] Mufrodi Z, Rochmadi R, Budiman A. Chemical kinetics for synthesis of triacetin from biodiesel byproduct. Int J Chem 2012;4(2):101–7. [47] Teo HTR, Saha B. Heterogeneous catalysed esterification of acetic acid with isoamyl alcohol: kinetic studies. J Catal 2004;228(1):174–82. [48] Lee MJ, Wu HT, Lin HM. Kinetics of catalytic esterification of acetic acid and amyl alcohol over Dowex. Ind Eng Chem Res 2000;39(11):4094–9. [49] Sert E, Buluklu AD, Karakuş S, Atalay FS. Kinetic study of catalytic esterification of acrylic acid with butanol catalyzed by different ion exchange resins. Chem Eng Process 2013;73:23–8. [50] Faria RPV, Pereira CSM, Silva VMTM, Loureiro JM, Rodrigues AE. Glycerol valorization as biofuel: thermodynamic and kinetic study of the acetalization of glycerol with acetaldehyde. Ind Eng Chem Res 2013;52(4):1538–47. [51] Altiokka MR, Çitak A. Kinetics study of esterification of acetic acid with isobutanol in the presence of amberlite catalyst. Appl Catal, A 2003;239(1–2):141–8. [52] Izci A, Uyar E, Izci E. Determination of adsorption and kinetic parameters for synthesis of isobutyl acetate catalyzed by Amberlite IR-122. Chem Eng Commun 2008;196(1–2):56–67. [53] Liu Y, Lotero E, Goodwin Jr JG. A comparison of the esterification of acetic acid with methanol using heterogeneous versus homogeneous acid catalysis. J Catal 2006;242(2):278–86. [54] Santacesaria E, Gelosa D, Danise P, Carra S. Vapor-phase esterification catalyzed by decationized Y zeolites. J Catal 1983;80(2):427–36. [55] Akyalçın S, Altıokka MR. Kinetics of esterification of acetic acid with 1-octanol in the presence of Amberlyst 36. Appl Catal, A 2012;429:79–84.

[56] Rane SA, Pudi SM, Biswas P. Esterification of glycerol with acetic acid over highly active and stable alumina-based catalysts: a reaction kinetics study. Chem Biochem Eng Q 2016;30(1):33–45. [57] Veluturla S, Narula A, Rao DS, Kulkarni RM. Experimental and Kinetic Studies of Esterification of Glycerol Using Combustion Synthesized SO42-/CeO2-Al2O3. Korean Chem Eng Res (화학공학) 2018;56(4):592–9. [58] Fukumura T, Toda T, Seki Y, Kubo M, Shibasaki-Kitakawa N, Yonemoto T. Catalytic synthesis of glycerol monoacetate using a continuous expanded bed column reactor packed with cation-exchange resin. Ind Eng Chem Res 2009;48(4):1816–23. [59] gPROMS. Advanced User Guide Release 2.3. London: Process System Enterprise, Ltd.; 2004. [60] Guiochon G, Guillemin CL. Quantitative Gas Chromatography for Laboratory Analyses and On-Line Process Control-Volume 42. 1st ed. Amsterdam: Elsevier Science; 1988. [61] Akaike H. Selected Papers of Hirotugu Akaike. New York: Springer; 1974. [62] Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. New York: Springer-Verlag; 2002. [63] Alcázar LA, Ancheyta J. Sensitivity analysis based methodology to estimate the best set of parameters for heterogeneous kinetic models. Chem Eng J 2007;128(2–3):85–93. [64] Solomons TWG, Fryhle CB. Organic Chemistry. 7th ed. New York: Wiley; 2000. [65] Liu X, Ma H, Wu Y, Wang C, Yang M, Yan P, et al. Esterification of glycerol with acetic acid using double SO 3 H-functionalized ionic liquids as recoverable catalysts. Green Chem 2011;13(3):697–701. [66] Balaraju M, Nikhitha P, Jagadeeswaraiah K, Srilatha K, Sai Prasad PS, Lingaiah N. Acetylation of glycerol to synthesize bioadditives over niobic acid supported tungstophosphoric acid catalysts. Fuel Process Technol 2010;91(2):249–53. [67] Kirumakki SR, Nagaraju N, Chary KVR. Esterification of alcohols with acetic acid over zeolites Hβ, HY and HZSM5. Appl Cat, A 2006;299:185–92. [68] Kim I, Kim J, Lee D. A comparative study on catalytic properties of solid acid catalysts for glycerol acetylation at low temperatures. Appl Catal B Environ 2014;148–149:295–303. [69] Cavallero KV, Guerrero-Amaya H, Baldovino-Medrano VG. Revisting glicerol esterification with acetic acido ver Amberlyst-35 via statistically designed experiments: overcoming transport limitations. Chem Eng Sci 2019;207:91–104. [70] Pagliaro M, Ciriminna R, Kimura H, Rossi M, Della Pina C. From glycerol to valueadded products. Angew Chem Int Ed 2007;46(24):4434–40. [71] Mota CJA, Pinto BP, De Lima AL. Glycerol: A Versatile Renewable Feedstock for the Chemical Industry. Cham: Springer; 2017.

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