110th Anniversary: Modeling and Optimization of a Butyl Glycol Ether

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Cite This: Ind. Eng. Chem. Res. 2019, 58, 13260−13273

110th Anniversary: Modeling and Optimization of a Butyl Glycol Ether Plant Based on an Experimental Kinetic Study Seonghwan Choi,†,‡ Junkyo Jeong,‡ Chan Yeong Yun,‡ Sora Hwang,‡ and Jay H. Lee*,† †

Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea ‡ Lotte Chemical Research Institute, 115, Gajeongbuk-ro, Yuseong-gu, Daejeon 34110, Republic of Korea

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S Supporting Information *

ABSTRACT: A simulation model is developed to estimate the production of four products (butyl monoethylene glycol ether, butyl diethylene glycol ether, butyl triethylene glycol ether, and butyl polyethylene glycol ether) in a glycol ether plant and to predict the amount of utilities consumed by the manufacturing units. To ensure the broad applicability of the developed model, a reaction kinetics model is required. As appropriate data for the kinetic modeling are not available in the literature, experiments are conducted to generate data needed. A thorough comparison between simulation results and operating data of the actual process obtained under various conditions shows that the model is reliable and accurate enough to be used to establish an operating strategy to maximize the overall profit. Major parameters affecting the economic feasibility of the plant are investigated, and their contributions to the cost are expressed as a formula. In addition, their effective ranges are decided in consideration of various constraints. On the basis of the results, several cost functions are derived to form an adjustable objective function. To demonstrate the usefulness of the simulation and economic models, a case study is conducted to find the most economically feasible retrofitting design based on the conditions that maximize the objective function.

1. INTRODUCTION In industrial processes where multiple products are produced simultaneously, it is important yet difficult to accurately predict the production ratios of the individual products. These ratios are used to calculate the amounts of individual products produced and production costs, to estimate the overall economics, and to determine the operating conditions that maximize profit. Engineers at actual production sites commonly try to figure out these ratios based on past operating data and intuition. However, with this empirical approach, the reliability of the estimated values can only be ensured for those operating conditions that have already been experienced. In the present study, a mathematical model based on experimentally identified reaction kinetics is developed to provide a basis for optimizing the operating conditions and ensure the reliability of the estimated values over a broader range. The glycol ether plant of Lotte Chemical, discussed in this article, was built in 2012 and has a process structure where three serially connected reactors produce butyl monoethylene glycol ether (BG), butyl diethylene glycol ether (BDG), butyl triethylene glycol ether (BTG), and butyl polyethylene glycol ether (BPG). These products are then separated by using serially connected distillation columns based on their boiling point differences. © 2019 American Chemical Society

A portion of ethylene oxide (EO) is added to a circulating stream of n-butanol (n-BuOH) at a specific ratio that controls the product distribution. As the reaction mixture is nonaqueous, a catalyst is needed to accelerate the reaction. Potassium hydroxide (KOH) is used as the catalyst, which is added to the n-BuOH solution, preheated, and sent to the first reactor. A series of adiabatic reactors are used to control the temperature rise. A portion of the EO is added to each reactor. The reactor effluent is cooled in an intercooler before being sent to the next reactor. The effluent from the final reactor is sent to the n-BuOH column where the unreacted alcohol is removed and returned to the reactors after the makeup nBuOH is added. The alcohol-free ether mixture is sent to the first product column, the monoglycol ether column, where the monoether product is removed. If a heavy ether distribution is desired, some of the monoether is returned to the reactors. The BG-free bottoms of the monoglycol ether column are sent to the diglycol ether column where the diglycol ether product is distilled overhead, and the bottoms from the column Received: Revised: Accepted: Published: 13260

May 6, 2019 June 23, 2019 June 28, 2019 June 28, 2019 DOI: 10.1021/acs.iecr.9b02490 Ind. Eng. Chem. Res. 2019, 58, 13260−13273

Article

Industrial & Engineering Chemistry Research

Figure 1. Simplified flow diagram for the existing sections and retrofitting sections of glycol ether plant.

Table 1. Types of BG and Physical Properties N 1 2 3 ≥4

full name BG BDG BTG BPG

butyl butyl butyl butyl

monoethylene glycol ether diethylene glycol ether triethylene glycol ether polyethylene glycol ether

molar mass (g/mol)

boiling point (°C)

melting point (°C)

118.2 162.2 206.2 mixture

171 230 278

−77 −68 −35.2

density (g/cm3) 0.90 0.95 0.98 1.06

(25 (25 (25 (25

°C) °C) °C) °C)

BDG has gradually been increasing. Figure S1 shows the trend of product−raw material spreads for BG and BDG over the period from 2005 to 2017. The spread for BDG increases faster over time than that for BG. This means that increasing the production ratio of BDG, rather than BG, will contribute to improving the profitability of this glycol ether plant going forward. Adjusting the mixing ratios of raw materials, including BuOH and EO, may affect the ratios of BG and BDG produced in the reactor. Simply put, a decrease in this BuOH/EO ratio will increase the production ratio of BDG, a high-molecular material. To quantify this mechanism, research on kinetic modeling for n-BuOH ethoxylation reactions was carried out.1 However, the scope of the study was limited to the conversion rate of EO. Thus, the reaction rates in each reaction phase were not examined separately, thereby making it difficult to quantify the production ratios of the various products, including BG, BDG, BTG, and BPG. Furthermore, adjusting the n-BuOH/EO ratio alone will have only a limited effect on the production ratio of BDG, i.e., within the range of 3.7%. This level of change is not sufficient to bring about a significant improvement to the profitability of glycol ether manufacturing plants. Patent US 3,935,279A2 describes a method in which the BG produced in the conventional glycol ether process is used as a raw material and subjected to the ethoxylation reaction to produce additional BDG. This concept is the closest to what

containing mainly polyglycol ethers are sent to the disposal (see Figure 1). The main use for glycol ethers is as solvents for formulations such as paints, inks, and cleaning fluid applications. Glycol ethers combine the solubility characteristics of both ethers and alcohols as both functional groups (ether and hydroxyl) are present in the molecule. Nonsolvent applications for glycol ethers include use as anti-icing agents in jet fuel, as fluids for hydraulic systems, and as chemical intermediates for plasticizers and other compounds. Glycol ether is classified according to the type of raw materials used, including alcohol and alkyl oxide. E-series glycol ether is made from EO as a raw material, while P-series ether is made from propylene oxide. The present study aims to examine the manufacturing process of E-series glycol ethers, especially those using n-BuOH as a precursor: butyl glycol ether (BG), butyl diglycol ether (BDG), butyl triglycol ether (BTG), and butyl polyglycol ether (BPG) in Table 1.

The demand ratios for each product on the market are somewhat different from these production ratios, and the difference in the growth rate of the demand for each product affects their price trends, gradually giving rise to a difference in their profit rates. Notably, the price gap between the BG and 13261

DOI: 10.1021/acs.iecr.9b02490 Ind. Eng. Chem. Res. 2019, 58, 13260−13273

Article

Industrial & Engineering Chemistry Research this study aims to achieve. The patent, however, failed to discuss the economic feasibility of using BG in a quantitative manner, i.e., regarding how much of the total BG produced needs to be reprocessed to maximize the profitability. Simply put, optimization issues were not addressed, and the idea was not implemented commercially. Meanwhile, similar research and development works leading to commercialization have been reported out in the context of the ETA (ethanol amine) and EG (ethylene glycol) manufacturing plants.3,4 In the ETA manufacturing plant, MEA (monoethanol amine), DEA (diethanol amine), and TEA (triethanol amine) were produced simultaneously, and the production ratio of MEA was the highest. In terms of profitability, however, DEA was the best, even though it was produced in least amount among the products. In response, the chemical company engaged in the ETA manufacturing process proposed to raise the production ratio of DEA by installing an additional ethoxylation process, in which MEA, which was produced the most, was used as a raw material in the existing manufacturing plant to increase the production ratio of DEA. The situation was similar in the EG manufacturing process, where, aside from EG, its main products, DEG (diethylene glycol) and TEG (triethylene glycol) were jointly produced. Overall, the demand for EG, its main product, was the highest, but in some regions, the byproducts, such as TEG, turned out to be more profitable. In such cases, attempts to add an additional hydration process using EG as a raw material to the existing process have been made to increase the production ratio of TEG. The feed ratio of BuOH/EO, the raw materials, changes depending on the desired production ratios of these byproducts. To accurately predict the variation in the raw material consumption and product yields, an accurate reaction kinetic model is needed to account for the production ratios of BG, BDG, BTG, and BPG, which are manufactured as a result of the n-BuOH ethoxylation reaction. The model must also be capable of aiding in the evaluation of the possibilities of installing additional ethoxylation units, e.g., estimating the investment costs accordingly. Also, variable costs with respect to the degree of retrofitting need to be estimated. On the basis of these results, the economic value of a given design needs to be evaluated to provide optimal conditions. There have been some studies investigating the BG manufacturing process in a partial manner,1 but these studies have not been comprehensive enough to cover the above-mentioned issues, i.e., estimating the variable costs and overall investment costs with respect to the option of adding ethoxylation process units through retrofitting.

Table 2. Previous Literature on Alcohol’s Alkoxylation Kinetics alcohol Zumbrzuska, J.7 Hermann, P.D., et al.6 Serio, M.D., et al.22 Burczyk, B., et al.8 Talens, F.I., et al.9 Hall, C.A., et al.12 Serio, M.D., et al.5

dodecanol octanol nonylphenol dodecanol n-BuOH n-BuOH lauryl alcohol EG

Serio, M.D., et al.23

dodecanol

An, W.Z., et al.1

n-BuOH

epoxide EO EO EO EO EO EO EO propylene oxide EO propylene oxide EO

catalyst KOH KOH KOH NaOH HY zeolite H2SO4 TEA NaOH KOH KOH NaOH KOH

in the present article is the one by W. Z. An et al.1 However, kinetic parameters were obtained for the reaction rates of only two out of the four reactants, and even the reaction rates of the two reactants were approximated to be equal to each other in the simulation. Given the current state of the literature, it was decided that experiments were needed first to develop kinetic models of the relevant reactions. In the kinetic modeling, special attention was paid to the effect of various reaction parameters on the individual yields of BG, BDG, BTG, and BPG to make sure that experimental data obtained can be effectively used for this purpose. We then incorporated the developed kinetic model into the simulation model of the overall plant, based on which a necessary level of process simulation accuracy could be achieved. 2.1. Apparatus. The main features of the autoclave system used for the experiment are illustrated in the schematic diagram in Figure S2. The 2 L vessel was made of 316 stainless steel and could tolerate a maximum pressure of 130 bar, although the maximum working pressure used here was far below that, around 50 bar. The vessel was equipped with a relief valve and a bursting disk, which above a set pressure discharged the high-pressure gas. The reactor had an agitation system with an anchor-blade-type impeller attached to the shaft, the speed of which could be controlled from 50 to 600 rpm. A jacket was installed outside of the reactor to heat up the feed mixture before initiation of the reaction and to remove heat from the process during the reaction. A temperaturecontrolled oil suited for industrial use was supplied to the jacket from a circulator. The circulator kept the inside of the reactor at a constant temperature as the capacity of the circulator was significantly greater than the heat of reaction and the heat capacity of the feed mixture. EO, one of the key reactants, was first stored in a pressure vessel to prevent vaporization at room temperature and was then supplied to the reactor. A separate coolant was supplied to the jacket of the pressure vessel to keep the storage condition at a temperature equal to or lower than 5 °C. In addition, a sample valve was placed at the bottom of the reactor to allow samples of the intermediate reactants to be taken during the reaction. 2.2. Materials. A variety of reagents were used in this experimental study. For the ethoxylation experiments, n-BuOH (purity ≥99.4%) was supplied by Sigma-Aldrich Korea Ltd. EO (highest purity) and nitrogen (high purity) were supplied by Daesung Industrial Gases Co., Ltd. In the experiments, an aqueous 50 wt % solution of KOH was added to the reactor to

2. EXPERIMENTAL KINETIC STUDY Prior to this study, a literature survey was carried out on the reaction kinetics of the alcohol ethoxylation reactions. As shown in Table 2, experimental reaction kinetic studies were conducted on the ethoxylation and propoxylation reactions of various alcohols. However, a requisite level of simulation accuracy could not be obtained from the available experimental data or kinetic parameter values of the reactions involving different alcohols.4−6 In addition, the reaction rates and product yields of the ethoxylation of n-BuOH found in several previous reports were significantly different from those obtained from a study using KOH as a catalyst, probably due to the differences in the catalyst employed.8,9 A previous study using a reaction system most similar to the one reported 13262


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3. MODELING AND SIMULATION 3.1. Reaction Kinetics. The BG production reaction can be specified from the general mechanism of ethoxylation as shown below.

act as the catalyst. KOH (50% (w/v) in aqueous solution) was supplied by Tokyo Chemical Industry Co., Ltd. For gas chromatography analysis, BG (analytical standard grade) was supplied by Sigma-Aldrich Korea, Ltd. BDG (99%) and BTG (99%) were supplied by Daesan Plant of Lotte Chemical. Dichloromethane (AR grade) was supplied by Tokyo Chemical Industry Co. 2.3. Experimental Procedure. Because EO is explosive even at room temperature, it was necessary to pay full attention to the safety precautions. After checking that the EO injection valve and the reactor’s outlet valve were closed, the stainless steel beaker used to measure n-BuOH, the starting material, was checked to determine whether all moisture had been removed. One hour of wait time was allowed after setting the reactor temperature to 50 °C. The procedures employed were designed to minimize the possibility of moisture inflow and are necessary here because even a tiny amount of moisture can affect the reaction rate of ethoxylation. After feeding a predetermined amount of n-BuOH with the catalyst, the bolt at the top of the reactor was fastened. After closing the gas outlet line of the reactor, the valves for supplying nitrogen to the hood and cooling water were opened. To eliminate the moisture contained in the reactant, the agitator was set to 300 rpm, nitrogen was injected into the reactor, and a vacuum pump was operated for 1 h. After the inside of the reactor was filled with nitrogen to about 3 bar, the pressure was released. This procedure was repeated at least five times. Subsequently, the inside of the reactor was filled with nitrogen again until the internal pressure reached 1 bar. These steps were carried out to prevent the explosion of EO. Then, the temperature of the reactor was set to 110 °C, and the agitator was set to 300 rpm. The reactor was kept at this setting for about 3 h to allow the catalyst to be activated. After checking whether the temperature of the circulator of the EO storage vessel was below 5 °C, the main valve of the EO bombe was opened, and EO was injected into the reactor in the amount needed for the experiment. About 30 g of EO was initially injected, and changes in the reactor pressure and temperature were observed to see if the reaction had been initiated. After confirming the initiation of the reaction, the remaining EO was gradually injected. 2.4. Product Analysis. Gas chromatography and mass spectrometry (GC/MS) were used to analyze the composition of the individual products. The procedures used to analyze the composition of the individual products are as follows: First, experiments were performed under three temperature conditions (150, 170, and 190 °C). Eight samples at different reaction times were obtained under each condition. The obtained samples were analyzed to acquire composition data of six kinds of components. Samples of about 5 mL were obtained through the sampling valve at the bottom of the reactor. Then, 0.05 mL of the obtained mixture of glycol ether was taken by a pipet and dissolved in 10 mL of dichloromethane. The resulting solution was sonicated for about 1 min to produce a uniform concentration of the solution. Then, 1.5 mL of the solution was taken and placed in a vial for measurement to perform the analysis using the method shown in Table S1. The analysis produced the peaks shown in Figure S3. The residence time of each substance was obtained by preparing the standard substances in advance and performing GC/MS analysis. Calibrations were carried out within the effective concentration ranges.

To the starting material, n-BuOH, EO is sequentially added one at a time to produce BG, BDG, BTG, and BPG in that order. BG of a higher molecular weight may be produced if a longer reaction time is given with a continuous supply of EO. However, in the present study, the production ratio of BPG, the product having the highest molecular weight, was as little as less than 3 wt %. All of the products containing four or more moles of added EO were considered to be a single type of substance, BPG. If it is assumed that the reactions occur separately as EO is sequentially added to each reactant one at a time, the reactions may be expressed as the elementary reactions shown in eqs 6−10.9−11 dC BuOH = −k1C BuOHC EO dt

(6)

dC BG = k1C BuOHC EO − k 2C BGC EO dt

(7)

dC BDG = k 2C BGC EO − k 3C BDGC EO dt

(8)

dC BTG = k 3C BDGC EO − k4C BTGC EO dt

(9)

dC BPG = k4C BPGC EO dt

(10)

The ethoxylation reaction of n-BuOH was assumed to follow a first-order dependence on reactant concentration with an Arrhenius temperature association, giving the first-order rate constants (ki) in the form of eq 11 ki = Ai exp( −Ei /RT )

(11)

where Ai is the pre-exponential factor and Ei is the activation energy. Given the Arrhenius temperature association, the values obtained from the experiments performed at different temperatures may be expressed as a single kinetic equation, and the consistency of the experimental ki values obtained at different temperatures can be verified. 3.2. Parameter Estimation for the Kinetic Model. The developed ethoxylation kinetic model includes 12 parameters (4 parameters for each of the 3 temperatures) that needed to be estimated. There are two operational condition parameters 13263


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Industrial & Engineering Chemistry Research that affect the reaction rate: reaction temperature and reaction time. Of the two operation condition parameters, the rate constant is dependent on the reaction temperature. Therefore, if the temperature is the same, the rate constant is the same, even though the reaction time may be different. Table 3 shows the parameters to be obtained through modeling and the experimental values collected to acquire the parameters. Table 3. Kinetic Parameters and Experimental Data for Estimating Parameters kinetic parameter notation ki,j

experimental data

meaning of subscription

notation

i = 1, n-BuOH → BG

Cn‑BuOH,j,l

i = 2, BG → BDG i = 3, BDG → BTG i = 4, j = 1, 150 j = 2, 170 j = 3, 190

total 12 sets

BTG → BPG reaction temp. °C reaction temp. °C reaction temp. °C

meaning of subscription

j = 1, reaction temp. 150 °C CEO,j,l j = 2, reaction temp. 170 °C CBG,j,l j = 3, reaction temp. 190 °C CBDG,j,l l = 0, reaction time 0 min CBTG,j,l l = 1, reaction time 10 min CBPG,j,l l = 2, reaction time 20 min l = 3, reaction time 30 min l = 4, reaction time 40 min l = 5, reaction time 60 min l = 6, reaction time 120 min l = 7, reaction time 240 min l = 8, reaction time 360 min total 162 sets

Figure 2. Effect of temperature on the EO conversion rate.

model provides predictions closely matching the experimental values. One notable point is that the EO conversion over time was higher at 170 °C than that at 190 °C. On the other hand, the EO conversion over time at 150 °C was significantly lower than that at 170 or 190 °C. This is explained by the curve showing the EO solubility in n-BuOH (as EO is fed to the reactor in gaseous form). The EO solubility in n-BuOH is temperature-dependent: It decreases as temperature increases. Therefore, the EO concentration of n-BuOH in the reaction system may have decreased as the temperature was increased. As described before, the reaction rate of ethoxylation is directly affected by the EO concentration. For reference, a plot showing EO solubility change with respect to temperature is given as Figure S4 in the Supporting Information For comparison of the experimental values between 150 and 170 °C, the effect of the increase in the intrinsic reaction rate in relation to the temperature increase may dominate over the effect of the EO concentration. In contrast, for experimental values between 170 and 190 °C, the effect of the increase in the intrinsic reaction rate in relation to the temperature increase may be negligible compared with the effect of the decrease in EO concentration. The same explanation may be applied to interpret the increasing trends of BG, BDG, BTG, and BPG production yields over time under different temperature conditions (see Figure 3). Figure 4 compares all of the experimental values and the estimated values of the concentrations. The RSSE value was 0.98, indicating that the developed reaction kinetic model fitted the experimental data very well. Arrhenius plotting was performed to verify the consistency of the kinetic parameters obtained under the various temperature conditions and to express the temperature dependency, as shown in Figure 5. In order to confirm the parameter estimation quality, 95% confidence intervals of simulation values are represented in Table 4.16−18 An overall linear tendency was found, but the linear tendency was not clear in the plots for k3 and k4. This is expected as the contents of BTG and BPG were low in the samples obtained from the experiments, giving more significant analytical errors. However, the errors are thought to be tolerable because the production ratios of BTG and BPG are small in the actual plant and their effect on the profitability of the production process is also small. As will be described later, the result of the simulation of the overall process, performed using the kinetic parameters, was highly consistent with the

With the four rate constants (k1−k4) as unknown parameters for each temperature, a numerical search was performed to minimize the difference between the experimental values and the values predicted by the kinetic equations. More specifically, for each temperature tried (j = 1−3), the k1−k4 values that minimized the root sum square error were obtained

ij 8 i mod exp 2 y jj 1 jj Ci , j , l − Ci , j , l zyz zzz zz zz RSSE(j) = ∑ jjj ∑ jjj zz zz jj 8 jj Ciexp z zz ,j,l i j l=1 k {{ (12) k exp where Ci,j,l is the experimental value of the concentration of component i at the lth reaction time for the jth temperature and Cmod i,j,l is the corresponding model predicted value. Because the proposed model has nonlinear kinetics, numerical optimization for the parameter estimation can get stuck in local minima. To alleviate this problem, a genetic algorithm (the ga function, which is embedded in Matlab 2018a and is widely used for global optimization) is used to estimate the selected parameters.13,14 Experimental values show the molar concentrations of the components in the product mixture sampled at each condition, and they were obtained through lab-scale ethoxylation experiments conducted under isothermal conditions.15 Figure 2 compares the experimental EO conversion values with the values given by the kinetic model under the different temperature conditions. The results show that the kinetic 13264


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Figure 4. Comparison between simulation and experimental results.

The NRTL (Non-Random Two Liquids) thermodynamic model was used for the phase equilibria calculations in this simulation because all of the compositions in the system were polar compounds. The individual columns were simulated to have the same pressure and the same operating conditions as the actual plant. The reflux ratio was adjusted to the design specification mode so that the same level of top product purity could be acquired despite the variation in the feed flow rate. The boil-up rate was changed relative to the varying reflux ratio, and the change was reflected in the reboiler heat duty of each column. Through this, the utility consumption could be calculated to vary the product yield. This is a critical factor for the variable cost estimation. The simulation was performed under three sets of operating conditions, shown in Table 5. Actual operation data were available for the three sets of conditions. The actual operating data were compared with the simulation data to verify the validity of the overall process simulation model developed in the present study. First, the actual process operation values were compared with the simulation values in terms of the temperature profiles of the three ethoxylation reactors. Five temperature indicator controllers (TICs) were attached to the first and second reactors, and six TICs were attached to the final reactor at different positions. The ethoxylation reaction, adding EO to nBuOH, is an exothermal reaction, and each of the reactors was under adiabatic conditions. The temperature at each position of the individual reactors can be estimated approximately as the heat of reaction divided by the heat capacity of the fluid in the flow stream. The temperature at each position in the individual reactors can serve as an excellent indicator, representing the overall progress of the reaction and the composition of each mixture fluid. Figure 7 shows that the temperature profiles simulated under the three different sets of operating conditions are well matched with the reactor temperature values at different positions obtained from the actual operation data. Table 6 compares the compositions of the individual substances in the final reactor, obtained from the simulation, with those obtained

Figure 3. Concentration change curves of the reactants and products: (a) 190, (b) 170, and (c) 150 °C.

data obtained from the operation of the actual production process. 3.3. Simulation of the Overall Process. The simulation of the overall process was performed using ASPEN Plus version 8.8. As shown in Figure 6, a steady-state simulation model using ASPEN Plus was developed. Table S2 presents a brief description of each unit operation block shown in the ASPEN Plus flowsheet. The reactor was simulated as a rigorous plug flow reactortype reactor operated under adiabatic conditions. The reaction rate coefficients calculated using the experimental kinetic model were used as input parameters. While the experimental kinetic modeling was performed in the batch mode, in the simulation, the reactor was assumed to be a continuous-type reactor operated under steady-state conditions, assuming that the reaction rate coefficients remained unchanged. The time trend of the concentration of each product in the batch reactor was reflected in the variation in the concentration of the products in the continuous plug flow reactor in the movement direction of the reactor. 13265


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Figure 5. Arrhenius plots for k1−k4.

Because the simulated values matched the actual operating data closely, the heat duty of the individual columns, estimated by the simulation, were judged to have good reliability. Table 7 compares the heat duties of the individual columns at different operating conditions in terms of the simulated values and the actual operation data. The heat duty values of the actual operating data were calculated as the product of the flow rate of the low-pressure steam put into the reboiler and the reference value of 450 kcal/kg. While the accuracy of the amounts of the individual products produced as estimated by the simulation was very high, with an error range of about 2%, the accuracy of the heat duty estimated by the simulation was lower, with an error range of about 10%. This was because the pressure of the lowpressure stream put into the reboiler varied somewhat over time. When the pressure of the low-pressure stream decreased, the temperature of the low-pressure stream, used as a heating medium, also decreased, resulting in incomplete use of the reference value of 450 kcal/kg.

Table 4. Kinetic Parameter Estimation Results estimated value k1 k2 k3 k4 k1 k2 k3 k4

95% confidence interval

Pre-exponential Factor (A, min−1) 9.025 × 102 6.342 × 102 6 4.099 × 10 2.675 × 106 5.716 × 109 3.765 × 109 1.419 × 106 9.435 × 105 Activation Energy (Ea, J/mol) 5.200 × 104 4.978 × 104 8.054 × 104 7.866 × 104 1.061 × 105 1.046 × 105 4 7.417 × 10 7.247 × 104

1.315 6.421 8.123 2.043

× × × ×

103 106 109 106

5.421 8.243 1.081 7.583

× × × ×

104 104 105 104

from the actual operation. Because a sampling port was absent at the end of the final reactor, the outlet composition of the final reactor was replaced by the composition of the feed in the alcohol column, the first unit operation after the final reactor, in the comparison. The result was also well matched by the values obtained from the simulation. Then, the temperature profiles of the individual columns at each stage were compared. The temperature profile is highly correlated with the liquid phase flow rate and vapor phase flow rate in a column. Because there was no indicator for measuring the liquid phase flow rate and vapor phase flow rate in the actual columns, comparing the temperature profiles allows an indirect examination of how well the liquid phase flow rate and vapor phase flow rate are predicted by the simulation model. In addition, the liquid phase flow rate and vapor phase flow rate in the column are affected by the reflux ratio and boil-up rate and, ultimately, have a high correlation with the reboiler heat duty of each column. The reboiler heat duty affects the amount of utilities consumed to operate each column. Figure 8 compares the temperature profiles of the individual columns at an n-BuOH feed rate of 33 520 kg/h and EO feed rate of 3330 kg/h, between the simulation and the actual factory operation.

4. CASE STUDY: RETROFITTING AND ECONOMIC ANALYSIS In the Introduction, it was mentioned that adding an additional ethoxylation unit may be a realistic method of increasing the production ratio of the high-value-added product while conforming to other constraints. Various analyses were carried out to minimize the investment cost and the payback period. First, the correlations between the various operational conditions of the ethoxylation section and the unit design were derived. Then the correlations between the unit design and the capital investment cost (CAPEX) were derived, and the investment cost was translated into depreciation per unit production. In addition, the correlations of the various operation conditions with the consumption of each raw material and with the production of each product were derived. 13266


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Figure 6. Main flow sheet for simulation in ASPEN Plus.

exchangers for cooling are installed in order to prevent yellowing and deterioration of the products and reactants (see Figure 6 and Table S2). If the number of reactor units is decreased, the investment cost per unit production is decreased, but the operational stability is decreased and the window of viable operating conditions is narrowed as the reaction heat may not be effectively removed. In contrast, if the number of reactor units is increased, the production flexibility and operating profit are enhanced, but the net profit may be reduced due to the increase in the investment cost. Furthermore, the possibility of realizing the investment proposal is also decreased as the payback period is increased. This issue will be addressed systematically in an optimization study discussed in a later section. At this point, we remark how the reactor unit may be sized based on the feed rate to the added section. The reactor design is assumed to be implemented according to the following design principles: (1) the temperature should not exceed 210 °C; (2) the EO concentration in the reactor effluent should not exceed 10 ppm; (3) the actual length of the third reactor should have a 50% margin in addition to the length derived from the simulation modeling; (4) the diameter of the individual reactors should be designed to be 36 in., which is the same as the existing ethoxylation reactors; (5) the flow regime should be in the turbulent regime. As the flow rate of the BG feeding to the additional ethoxylation section is increased, the production ratio of the high-value-added products may be enhanced, but the invest-

Table 5. Simulation Condition of Each Case

case I case II case III

n-BuOH feed rate (kg/h)

EO feed rate (kg/h)

n-BuOH/ EO feed ratio

operation load (%)

33520 25942 20769

3330 3326 2629

10.1 7.9 7.8

121 96 77

The quantity of the utilities consumed in each case was also functionalized. The acquired objective functions closely represented the correlation between the individual operational conditions and the profit per unit production. The optimal conditions were then obtained by maximizing the objective functions. The optimization was first performed for the case where an additional ethoxylation section was not added (to utilize only the current facilities) and then for the case where an additional ethoxylation section was added. The result of the economic assessment for each case was compared with that of the previous economic assessment to show the usefulness of the developed model as well as the optimization that was carried out in the present study (see Figure 1). 4.1. Design of Additional Ethoxylation Reactors. The ethoxylation reactor is the core unit of the newly added ethoxylation section as it is critical to the profitability of the entire process and the new investment cost. The ethoxylation section comprises not only the reactor but also heat exchangers for heat control and mixers for mixing BG and EO. Because BG ethoxylation is an intense exothermic reaction, heat 13267


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Figure 7. Temperature profiles of the ethoxylation reactors; RTDB: real time database from plant.

Table 6. Comparison between Simulation Values and RTDB Values on Reaction Product Compositions case I unit n-BuOH BG BDG BTG BPG+HB etc. weighted average

case II

case III

simulation

RTDB

error

simulation

RTDB

error

simulation

RTDB

error

wt % 74.81 15.77 3.96 0.98 0.54 3.93

wt % 74.78 15.80 3.97 0.95 0.51 3.99

% 0.04 0.19 0.25 3.16 5.88 1.5 0.58

wt % 70.34 17.76 5.31 1.53 1.07 3.99

wt % 70.19 18.11 5.31 1.45 0.95 3.99

% 0.21 1.93 0.00 5.52 12.63 0.00 0.29

wt % 70.10 17.85 5.39 1.56 1.11 3.99

wt % 70.26 18.14 5.29 1.42 0.91 3.99

% 0.23 1.60 1.89 9.86 21.98 0.00 0.40

design constraints, increases from 7.7 to 10.2 m (see Figure 12). As the BG/EO feed ratio to the additional ethoxylation section is decreased, the production ratio of the high-valueadded products may be increased, but the heat generated per unit mass flow rate is increased in the additional ethoxylation reactor. To adjust this, the operation conditions of the heat exchangers placed before and after the reactors should be changed. Figure 10 shows the variation of the first reactor temperature profile before and after changing the operational conditions of the heat exchangers. As the BG/EO ratio is

ment cost for the additional ethoxylation reactors is increased. Therefore, CAPEX should be an important factor in the overall consideration, which is strongly affected by the reactor size. Because the reactor diameter is fixed as described above, the reactor length remains as the main parameter that affects the CAPEX of the reactors. As shown in Figure 9, as the BG feed flow rate is increased from 2000 to 4000 kg/h, the EO concentration in each reactor also becomes higher. However, the EO concentration’s increase is at a slower rate than the feed flow rate. Therefore, the minimum reactor length that may reduce the EO concentration below 10 ppm, one of the 13268


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Figure 8. Temperature profiles of the four distillation columns (case I).

Table 7. Comparison between Simulation Values and RTDB Values on Heat Duty of the Columns n-BuOH column (C210) unit case I case II case III weighted average

BG column (C310)

BDG column (C320)

BTG column (C330)

simulation

RTDB

error

simulation

RTDB

error

simulation

RTDB

error

simulation

RTDB

error

kcal/h 3 217 663 2 054 830 2 572 134

kcal/h 3 268 714 2 064 423 2 634 562

% 1.56 0.46 2.37 1.54

kcal/h 672 727 371 390 469 704

kcal/h 649 330 356 187 451 213

% 3.60 4.27 4.10 3.92

kcal/h 158 456 111 625 142 342

kcal/h 175 818 120 879 161 321

% 9.87 7.66 11.76 9.95

kcal/h 43 417 41 535 53 251

kcal/h 46 281 45 546 63 023

% 6.19 8.81 15.51 10.75

Figure 9. EO concentration profiles by BG feed flow rate (BG/EO ratio = 7.5).

Figure 11 shows the EO concentration profile in each individual reactor as the BG/EO feed ratio is changed from 6 to 9.6. At a lower BG/EO feed ratio where the EO feed rate is higher, the EO concentration decreases more slowly, and the initial EO concentration to be converted is higher. Therefore, the minimum reactor length that may reduce the EO concentration below 10 ppm, one of the design constraints, increases from 8.6 to 9.2 m (see Figure 12). 4.2. Cost Estimation. Types of cost estimation are summarized with respect to methods and accuracy in Table S3. In this study, cost estimation of the class 3 level has been carried out. It is planned that cost estimation of the class 4 level will be conducted through collaboration with an engineering company.19,20

decreased, the reaction temperature generally increases due to the heat of reaction. As the reaction rate is increased with the increase in the reaction temperature, the reactor length needed for the EO conversion is decreased, allowing for a more economical design. However, if the reaction temperature is over 210 °C, the products degrade and thus lose their commercial value. Therefore, it is necessary to derive the reactor length that decreases the EO concentration below 10 ppm while maintaining the reaction temperature below 210 °C. When the operational conditions of the heat exchangers are changed, the needed heat exchange area is also changed, resulting in changes of the heat exchanger design and the investment cost. These changes are also reflected in the function for calculating CAPEX. 13269


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Figure 10. Compensation of the reactor’s feed temperature to keep the reaction temperature below 210 °C.

Figure 11. EO concentration profiles by the BG/EO feed ratio (BG feed rate = 3000 kg/h).

Figure 12. Determination of the reactor length with respect to the BG/EO feed ratio.

The purchase costs of reactors were calculated through equations of Table S4. The plug flow-type reactors were regarded as horizontal pressured vessels, and the diameter was fixed to be 36 in., which is the same as that of the existing reactor. However, the length of the reactor was derived from the simulation model as the length affects the purchase cost of the reactor. In addition to the three ethoxylation reactors, the purchase cost for four drums, eight pumps, eight motors, four heat-exchangers, one agitator, and four static mixers should be derived. The formulas for calculating these are all summarized in Table S4.

The change in the purchase cost of the reactor due to the change in the length of the additional ethoxylation reactor is shown in Figure 13. In addition, the total investment cost including the construction cost with respect to the varying operating conditions (BG feed rate and BG/EO feed ratio) of the additional ethoxylation section can be expressed as in Figure 14. The consumption of raw materials and the production of the products at the reaction section are calculated by using the reactor model. The OPEX is calculated by multiplying the price of the individual materials of the derived material balance 13270


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Industrial & Engineering Chemistry Research Q C310 = 51.88·FBG + 83.54·FBDG + 93.27·FBTG + 104.02·FBPG

(14)

Q C320 = 55.96·FBDG + 81.12·FBTG + 93.33·FBPG

(15)

Q C330 = 97.43·FBTG + 50.03·FBPG

(16)

4.3. Optimization. The objective function is chosen as below

∑

max(M profit) = −

∑

∑

Fi ·Pi−

i = component

Fj·R j

j = component

(Q k /Q steam) − (CAPEX/8000)

k = column

(17)

The depreciation per unit time can be calculated by dividing the cost by the production per unit time in each case. Here, 8000 operation hours per year was assumed. Unlike the conventional cost function, which is calculated based on the amount of raw materials and utilities required to produce 1 ton of main product, the consumption of raw materials and the utilities calculated per unit time are used in the cost function in this paper. This type of objective function can be more efficient in the cases where multiple products are produced at different amounts and different ratios. The purchase price of the main equipment, including the reactors, accounts for 40% of the total equipment purchase cost, which accounts for about 50% of the total investment cost. The other investment cost includes the costs for construction work, engineering structures, design, and reserved fund. The ratios were provided by the Chemical Plant Business Division of Lotte Engineering and Construction, which has sufficient experience in the chemical plant construction business. Table 8 summarizes the four variables that were varied in the optimization as well as their maximum and minimum values.

Figure 13. Purchase cost by length of the reactor.

Figure 14. CAPEX trends with respect to the additional ethoxylation section’s operation conditions.

Table 8. Variables for Optimization

with the consumption and the production of each material per unit time. Table S5 shows the unit price of the individual materials used in the OPEX evaluation. The prices are the annual average prices for 2018 provided by the glycol ether sales team of Lotte Chemical. In the previous section, the model for calculating the heat duty was described. However, the model does not express the heat duty as a function of the operation conditions. Therefore, the heat duty values corresponding to the various operational parameters, 61 sets for each, were calculated using the ASPEN Plus model to investigate their correlations. The results showed that the mass flow rates of the individual materials fed to the columns are closely correlated with the heat duty of the columns at the reboilers. Therefore, linear regression was carried out to express the heat duty as a function of the mass flow rates. This was carried out by using the optimized selection method of the RapidMiner software (https:// rapidminer.com), which is a widely used data mining and analysis platform.21 The heat duty functions of the individual columns are given as eqs 13−16.

variable FBuOH RBuOH/EO FBG RBG/EO

valid range FBuOH < 28 000 7 < RBuOH/EO < 10 2000 < FBG < 4000 6 < RBG/EO < 9.5

The optimization procedure was designed for the specific case utilizing available process knowledge. First, the BG/EO feed ratio was set to be 6, which is the minimum value, and then the feed flow rate was optimized within the effective range. With respect to the four variables, the BG/EO feed ratio was increased by 0.1 unit each time and the feed flow rate by 1 unit each time to find the case where the net profit became the maximum. Optimization study was conducted in two cases. In case A, only the existing plant was used. In Case B, an optimization was reperformed after including the additional ethoxylation section operating conditions and the capital investment cost. The results of these two cases are compared in Table 9.

Q C210 = 79.76·FBuOH + 83.59·FBG + 137.30·FBDG + 127.92·FBTG + 14.77·FBPG + 101.11·FBE

explanation feed flow rate of n-BuOH to the main reaction section (kg/h) feed ratio of n-BuOH and EO to the main reaction section (based on mass) feed flow rate of BG to the additional ethoxylation section (kg/h) feed ratio of BG and EO to the additional ethoxylation section (based on mass)

(13) 13271


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value-added product at the current time, was increased from 18 to 21% in case A and to 31% in case B. The significant increase of the profitability gave a payback period of ∼2.5 years for the investment cost of 3.99 million USD, satisfying the investment condition according to the internal accounting criteria.

Table 9. Results of Optimization results variable

case A

case B

FBuOH RBuOH/EO FBG RBG/EO

28 000 7.3 0

28 000 7.3 4000 8.9

5. CONCLUSIONS In the present paper addressing a butyl glycol plant, based on an experimental kinetic study, the kinetic parameters of each reaction were obtained and reflected in the reactor model. Modeling of the overall process including not only the reactors but also the latter separation processes was carried out to provide a basis to estimate the amount of production of each product and the utility consumption in the overall process. The estimated values were compared with the actual operating data obtained from the commercial production process. The modeling performed in the present study made it possible to analyze the existing operational data more carefully and to predict the results of the operation under designs and conditions that have not been attempted before. As a result, a new section was designed and optimized to improve the production ratios. With regard to the economic feasibility of the optimal conditions derived from the modeling, it was predicted that additional annual profit of about 1.62 million USD may be achieved in comparison with the current plant operation, and the required investment of about 3.99 million USD may be returned within 2.5 years.

Tables 10 and 11 compare case C based on the current production conditions with the optimized cases A and B. The Table 10. Comparison of Economic Evaluation Results (Production Rate) ton/h price (USD/ ton)

case C (base)

1. raw material 1) n1213 BuOH 2) EO 1157 2. utility 1) 33 steam 3. product 1) BG 1198 2) 1483 BDG 3) 1274 BTG 4) 784 BPG 5) HB 50 total production rate

case A (without retrofitting)

case B (with retrofitting)

3.81

4.87

4.87

2.80

3.83

4.28

8.77

9.61

11.46

4.95 (75%) 1.21 (18%)

5.97 (69%) 1.83 (21%)

5.02 (55%) 2.85 (31%)

0.29 (4%)

0.54 (6%)

0.79 (9%)

0.11 (2%)

0.27 (3%)

0.35 (4%)

0.04 (1%) 6.61

0.10 (1%) 8.71

0.14 (2%) 9.16

■

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.9b02490. Gas chromatography-mass spectrometry analysis conditions, description of unit operation blocks and streams in the ASPEN Plus flowsheet, type of cost estimation, purchase cost of the units except for the reactors, base prices for optimization and economic evaluation, market trends of butyl glycol ether, schematic diagram of the experimental apparatus, GC analysis of butyl glycol ethers, and T−xy diagram for n-buatnol and ethylene oxide at reaction pressure (PDF)

Table 11. Comparison of Economic Evaluation Results (Net Profit) million USD/yr

1. raw material 1) nBuOH 2) EO 2. utility 1) steam 3. product 1) BG 2) BDG 3) BTG 4) BPG 5) HB 4. depreciation Δ total net profit

price (USD/ ton)

case C (base)

case A (without retrofitting)

case B (with retrofitting)

1213

37.02

47.32

47.32

1157

25.92

35.48

39.65

33

2.31

2.54

3.03

1198 1483 1274 784 50

47.47 14.34 2.98 0.68 0.02 0.00

57.25 21.66 5.46 1.70 0.04 0.00

48.19 33.78 8.07 2.22 0.06 0.40

+0.47

+1.62

ASSOCIATED CONTENT

S Supporting Information *

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +82-42-350-3927. Fax: +8242-350-3966. ORCID

Jay H. Lee: 0000-0001-6134-6118 Notes

The authors declare no competing financial interest.

■

NOMENCLATURE CP = purchase cost [USD] FM = material factor CV = empty vessel cost [USD] CPL = added cost for platforms and ladders [USD] CB = base cost for at the standard condition [USD] W = weight of the shell and the two heads [pound] Di = inside shell diameter [ft] L = length of vessel [ft] tS = wall thickness including operational margin [inch]

increase in the production is not significantly different between cases A and B, but the production ratio of the products is quite different. Hence, the net profit per unit time is greatly different over the three cases. The production ratio of BDG, the highest13272


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(17) Ryu, K. H.; Kim, B.; Lee, J. H. A model-based optimization of microalgal cultivation strategies for lipid production under photoautotrophic condition. Comput. Chem. Eng. 2019, 121 (2), 57−66. (18) Kim, B.; Jang, H.; Eom, M.; Lee, J. H. Model-Based Optimization of Cyclic Operation of Acetone-Butanol-Ethanol (ABE) Fermentation Process with ex Situ Butanol Recovery (ESBR) for Continuous Biobutanol Production. Ind. Eng. Chem. Res. 2017, 56 (8), 2071−2082. (19) Seider, W. D.; Seader, J. D.; Lewin, D. R. Product & Process Design Principles: Synthesis, Analysis and Evaluation; John Wiley & Sons, 2009. (20) Heo, S.; Park, H. W.; Lee, J. H.; Chang, Y. K. Design and Evaluation of Sustainable Lactide Production Process with an OneStep Gas Phase Synthesis Route. ACS Sustainable Chem. Eng. 2019, 7 (6), 6178−6184. (21) Hofmann, M.; Klinkenberg, R. RapidMiner: Data mining use cases and business analytics applications; CRC Press, 2013. (22) Di Serio, M.; Tesser, R.; Felippone, F.; Santacesaria, E. Ethylene oxide solubility and ethoxylation kinetics in the synthesis of nonionic surfactants. Ind. Eng. Chem. Res. 1995, 34 (11), 4092−4098. (23) Di Serio, M.; Tesser, R.; Santacesaria, E. Comparison of different reactor types used in the manufacture of ethoxylated, propoxylated products. Ind. Eng. Chem. Res. 2005, 44 (25), 9482− 9489.

Pd = internal design gauge pressure [psig] S = maximum allowable stress of the shell material [psig] E = fractional weld efficiency PO = operating pressure [psig] A = tube outside surface area [ft2] H = head of pump [horsepower] FT = motor-type factor PC = power consumption [horsepower] PT = theoretical horsepower of the pump [horsepower]

■

GREEK LETTERS ηP = fractional efficiency of the pump ηM = fractional efficiency of the motor ρ = density of material [pound/ft2]

■

REFERENCES

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110th Anniversary: Modeling and Optimization of a Butyl Glycol Ether

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