Optimization of Methanol Process by Flowsheet Simulation S. H. Ballman and J. L. Gaddy' Department of Chemical Engineering, University of Missouri-Rolla,
Rolla, Missouri 6540 1
Optimization of large complex chemical processes is a necessary step in economic design of these systems. Flowsheet simulation models afford an ideal mechanism for detailed optimization of these processes. The methanol process offers several interesting optimization variables (reformer temperature, pressure, and steam and carbon dioxide to hydrocarbon ratios; synthesis loop pressure, temperature, purge rate, and conversion) which require detailed models of the reaction kinetics. This paper presents the results of optimization of the intermediate pressure process using the PROPS model and the adaptive random search routine. The costs associated with performing such a detailed process optimization study are easily justified by even nominal improvements in the process economics.
Modular computer simulation programs are used extensively today for designing and studying chemical processes, and development of such programs is continuing (Motard et al., 1975). Sophisticated optimization algorithms have been developed and applied to unit processes, such as reactors (Barneson et al., 1970) and separators (Umeda and Ichikawa, 19711, or to simplified models of complex processes (Bracken and McCormick, 1968; Komatsu, 1968). Yet, few studies have been reported of the optimization of large complex processes, modeled with flowsheet simulators. Shannon et al. (1966) optimized a single variable of the sulfuric acid process with PACER. Seader and Dallin (1972) also used PACER to study the toluene dealkalation process, with a case study search procedure. Friedman and Pinder (1972) optimized the production rate of a gasoline polymerization unit, modeled with CHESS. This same process was studied with PROPS by Gaines and Gaddy (1976), with the objective of maximizing the profitability of the process. Each of the above studies was conducted a t a university, and process optimization studies, using flowsheet simulators, if conducted in industry, have gone unreported. The reluctance by industry to utilize simulators for detailed optimization can probably be explained by a combination of factors. There is considerable uncertainty over the amount of real time and computing time required to perform a detailed process optimization study. Industry may lack the experienced personnel to work in this area. Bounded by tight time constraints during design and construction. the necessary approach may be to come as close to optimal conditions as possible using experienced engineers. and optimize the operation of the facility after it is operating. Little information IS available on the design improvements available with optimization that might justify such studies. Most companies now use simulators during some phase of the design, perhaps for material and energy balance data. Therefore, it seems reasonable that additional simulation runs could be justified for optimization to improve the design. Such reasoning must be based upon the availability of a dependable process model, probabiy with economics, and a suitable optimization algorithm. The purpose of this study is to develop a detailed simuiation model of a complex chemical process and to measure the investment in real time and computer time for optimization of this process. The progress of the optimization is monitored so that the improvements obtained through optimization. even perhaps when starting very close to the optimum, can be determined. The process chosen for study is the intermediate pressure methanol process. This process is simulated with PROPS and optimized using the adaptive random search technique (Heuckroth and Gaddy, 1976).
T h e Methanol Process Figure 1 is a typical MeOH flow diagram, showing the equipment used for producing methanol from natural gas. Natural gas is reacted with steam over a nickel based catalyst in a steam-methane reformer to produce a gas mixture containing hydrogen, carbon monoxide, and carbon dioxide. Since excess hydrogen is available from the reforming, carbon dioxide is added to the reformer feed to lower the ratio of hydrogen to carbon in the reformed gas. Reforming is carried out at temperatures between 1400 and 1700 OF. The steam methane reaction is endothermic, and the reactor is heated by burning a mixture of natural gas and process off gases. Energy is recovered from the reformed gas and the combustion gases as 900 psia steam (500 O F superheat) in a waste heat boiler. This steam is used to drive the synthesis and recycle gas compressors. Exhaust steam from the compressor turbines is used both as process steam for the reformer and as a source of heat in the purification section of the process. Centrifugal compressors are used to raise the pressure of the makeup and recycle gases to 200 atm. The synthesis gas (recycle plus makeup gas) is heated to 570 O F by the effluent gas from the methanol converter. Methanol is synthesized in the converter using a zinc oxide-chromium oxide catalyst with a copper promoter. The methanol synthesis reaction is highly exothermic. T o control the temperature of the reaction, cool synthesis gas is injected between the catalyst beds. The gases from the converter exchange heat with the converter feed and are then cooled to atmospheric temperature to condense methanol. The pressure of the condensed liquid is reduced to 50 psia and dissolved gases are removed. A significant amount of the methanol is vaporized with the dissolved gases, and the MeOH is recovered by scrubbing with water. The crude methanol is then purified by distillation. Mehta and Ross (1970) and Shah and Stillman (1970) have reported optimization studies of the methanol process. Mehta and Ross studied only the effect of Cor,addition to the process. Shah and Stillman developed a FORTRAN modei of the major equipment items in the methanol process. They studied thp effect of several variables on the control of the process; however. only six iterations were used in the optimization procedure. Methanol Process Simulation The methanol process, shown in Figure 1, was modeled using PROPS (Process Optimization System), a modified version of CHESS. PROPS has the capability of computing the investment, operating cost, revenue, and profitability of the simulated process. A description of PROPS has been Ind. Eng. Chem., Process Des. Dev., Vol. 16, No. 3, 1977
337
Table I. Results of Base Case Study Independent Variables O F (Vl) Reformer operating pressure, psia (V2) H20/CH4 ratio reformer feed (V3) CO*/CHd ratio reformer feed (V4) Synthesis loop pressure, psia (V5) Synthesis loop purge (V6) Methanol converter feed temperature, OR (V7) CO conversion methanol converter (V8) Dependent Variables Investment, MM$ Working capital, MM$ Earnings after taxes, MM$ Payout period, years Return on investment, % Methanol production, tons/day Natural gas reformer feed, mol/h
1. Reformer operating temperature,
2. 3. 4.
5. 6. 7. 8. 1. MET HA NOL PRODUCT
FRACTIONATOR
L E T DOWN T A N K
Figure 1. Flow diagram of the 200 atm methanol process.
2. 3. 4. 5. 6. 7.
presented earlier (Gaines and Gaddy, 1974) and will not be reviewed here. Standard equipment modules in PROPS were used for modeling exchangers, separators, compressors, etc. in the MeOH process. In addition, modules were developed for: steam-methane reformer, waste heat boiler, boiler feed-water heater, methanol converter, partial condenser, optimization procedure, and production rate control. T h e details of the simulation model are given by Ballman (1976) and are summarized in the supplementary material. (See the paragraph a t the end of the paper regarding supplementary material.) As usual, modeling of the reactors required the greatest effort. The reformer model uses a modified Newton relaxation method to solve the algebraic material and energy balance equations to obtain the exit compositions and enthalpy. Reactor conditions are also checked to avoid carbon deposition. The methanol converter consists of four catalyst beds with interstage injection of cool synthesis gas to control bed temperatures. The differential equations resulting from the material and energy balance are solved simultaneously using a fourth-order Runge-Kutta method. As noted in Figure 1, the process has a recycle loop that must be converged. Other iterative loops within the process include selection of the proper synthesis gas splits between catalyst beds, selection of reformer conditions to avoid carbonization, as well as numerous dew point, bubble point, and temperature from enthalpy determinations. Convergence acceleration using the secant method is used in each case. In addition to the above iterative calculations, further computations were required to ensure a fixed production rate. A simulation study normally begins calculating in the first equipment item with a given input of raw materials. In optimization, where certain variables are being manipulated, the quantity of product varies for a specified feed rate. Therefore, optimization of a particular MeOH plant size imposes further iterative computations to determine the proper quantity of natural gas feed. These computations were guided by a production rate control module, which uses either a direct ratio for linear systems or a secant convergence acceleration for nonlinear systems. Once all recycle calculations are converged, the economic calculations are made to determine equipment and operating cost and profitability. A complete simulation of this model for a fixed production rate requires about 40 s of CPU time (IBM 370/168). Development of the model required about six man-months of a semi-experienced engineer’s time. Verification of the Process Model A study of this type, conducted in academia, is not privy to the best industrial data, and must depend upon published 338
Ind. Eng. Chem., Process Des. Dev., Vol. 16, No. 3, 1977
1540.0 350.0 3.2 1.00 3000.0 0.01 1030.0 0.29 28.8
3.9 3.4 4.93 12.1 800.0
2600.0
sources. The methanol process is reasonably well documented, and no amount of detail was spared in using the available data to create a representative model. T o establish that a realistic simulation model had been created, data obtained from the simulation were compared with data published in the literature. T h e steam-methane reformer model was found t o give equilibrium computations in agreement with those given by Hougen et al. (1966b),Morse, (19731, and Wellman and Kate11 (1963). The methanol converter model produced reactor temperatures and material flow rates which agree with those given by Natta (1955), Strelzoff (19701, and Shah and Stillman (1970). Design conditions given by Shah and Stillman (1970) and Strelzoff (1970) were used to establish a base case design. The results of this run for a plant to produce 800 tons/day (TPD) are given in Table I. For the design conditions shown, a plant investment of $28.8 M M was computed and the return on investment was found to be 12.1%. Hedley (1970) gives a capital cost of an 800 T P D plant of $26.4 M M (corrected to 1976). T h e economic evaluation given by Hedley shows a 10.0%return on investment. Comparison of these data confirm the ability of the model to predict realistic values of the process economics. Optimization P r o c e d u r e Other studies of the methanol process reactions have used the following independent variables: reformer operating temperature and pressure, the ratios of steam to methane and carbon dioxide to methane in the reformer feed, and methanol converter feed temperature (Shah and Stillman, 1970; Mehta and Ross, 1970). In addition, the following variables have been identified in this study: synthesis loop pressure, synthesis loop purge, and conversion of carbon monoxide in the methanol converter. These eight independent variables are labeled as V1 through V8 in Table I. The return on investment, ROI, was used as the objective function in optimizing the methanol process. The optimization can be stated as: Max ROI = f ( V1, V2, . . . , V8)
(1)
subject to the constraints: 1600 I V1 I 2200; 100 I V2 I 500; 1I V3 I 6; 0 I V4 I 1;2500 I V5 I 3500; 0.01 I V6 I 0.15; 1000 5 V7 I 1030; 0.15 I V8 50.35; D 1 , 0 3 , D 5 , 0 7 I 1100; 1000 I D2,D4,D6 I 1030; 0 8 , D9, D10 < 1. T h e function, f, in eq 1 represents all the computations performed by the equipment modules, the equipment design module, and the economic evaluation module in the PROPS simulation. Constraints on the independent variables are given in eq 1. Implicit constraints involving dependent vari-
Table 11. History of Optimization of 800 Ton/Day Methanol Process
CPU time, min
Step 1 2
3 4 5 6 7 8 9 10 11
Function evaluations
24 15 10 10 15 20
Objective function improvements
9 9
3 2 4
12
0
11
are encountered in the search region. Also, this procedure, although random, has been found to have equal or better efficiency when compared with other methods, and demonstrates good reliability in locating the optimum (Heuckroth et al., 1976; Kelahan and Gaddy, 1977).
13 5 14 2 8 5 1 15 8 1 10 5 2 10 6 1 20 13 1 12 20 8 3 13 8 4 1 14 15 8 3 Total 200 125 29 Total" 158 97 19 Results of search using a different starting point. ables, D ,in eq 24 represent temperatures in the methanol converter and carbon deposition ratios in the reformer. Constraint boundaries were set from the usual values found in the literature. The Adaptive Random Search procedure (Gaines and Gaddy, 1976; Heuckroth et al., 1976) was used to guide the selection of new values of the independent variables in the search for the optimal value of ROI. This procedure searches randomly in a restricted region about the best known objective function. The Adaptive Random Search was selected because of its ability to function effectively when implicit constraints
Results and Discussion Four optimization searches were performed in this study. Plant sizes of 800 T P D (two searches from different starting points) and 1000 T P D were studied. In addition, a run was made with fixed feed rate to determine the variations in production rate that could be expected and the savings in computer time that might be possible if the feed rate were known, or could be estimated. Sensitivity runs were made about the optimum in each case. Since a large amount of computer time was expected to be required for each study, the search procedure was halted periodically to review the progress of the search. This procedure allows adjustment in the parameters of the search algorithm and allows a periodic judgement on the convergence of the search. 1. Optimization of 800 TPD MeOH Plant. Table I1 shows the history of the search for the optimum design conditions for an 800 T P D plant. The search was conducted in 14 steps requiring from 8 to 24 min per step and a total of 200 min to locate the optimum. A total of 125 evaluations of the ROI function (eq 1) were made, for an average of 1.6 min per evaluation. Twenty-nine improvements in ROI were found. Similar results are noted for the search from a different starting point. T h e optimal results are given in Table I11 (last two rows). A t the optimum, several of the variables lie on or near their boundaries. Reformer temperature and pressure and meth-
Table 111. Results of Optimization of 800 Ton/Day Methanol Process
v1
v2
x lo-,' "R
x 10-2 psia
v5 V3
V4 x 10'
x 10-3 psia
V7 V6 x 102
x 10-3
V8 10'
"R
X ~~~~
2.0000 3.5000 3.5000 5.0000 1.9997 3.5107 3.4998 5.5233 2.0830 4.7279 3.4345 5.1401 2.2000 5.0000 2.7837 3.4295 2.2000 5.0000 2.8909 3.4295 2.2000 5.0000 2.8906 4.4308 2.1941 3.8393 2.7739 8.2982 2.1941 5.0000 2.3804 8.3951 2.1938 5.0000 2.3674 8.3356 2.1266 5.0000 2.3259 7.8053 2.1266 4.9988 2.3751 5.1592 2.1265 4.9461 2.3751 5.1592 2.1266 4.9575 2.3751 5.1564 2.1266 4.9575 2.3817 5/73 2.1267 4.9570 2.5244 4.8012 2.1266 4.9570 2.7056 4.6308 2.1266 4.9570 2.7448 6.9891 2.1484 5.0000 2.7144 4.5092 2.1915 5.0000 2.7144 10.0000 2.2000 5.0000 2.5618 10.0000 2.2000 5.0000 2.3957 10.0000 2.2000 5.0000 2.5618 9.9699 2.2000 5.0000 2.5620 9.9700 2.2000 5.0000 2.5620 9.9700 2.2000 5,0000 2.5620 9.9700 2.2000 5.0000 2.5620 9.9700 2.2000 5.0000 2.5620 7.4700 2.2000 5.0000 2.5620 6.9700 2.2000 5.0000 2.5620 6.1700 2.2000" 4.9500" 2.2677" 6.2594" " Results of search using a different starting point.
3.0000 3.1204 3.1787 3.4947 3.4947 3.4979 3.3500 3.3497 3.1145 3.1371 3.1354 3.1354 3.1255 3.1255 3.0413 3.0128 2.8411 2.8204 2.8451 2.8447 2.7496 2.8456 2.5009 2.6546 2.6555 2.5000 2.5000 2.5000 2.5000 2.5000"
4.0000 4.1968 4.1966 3.9441 3.6139 4.5111 4.5110 4.4878 4.4870 2.7331 3.0588 3.0591 3.0592 3.0582 3.3124 2.2251 1.9637 2.2232 2.2232 2.2201 2.7565 2.4591 1.2391 1.2391 1.4565 1.0000 1.0000 1.0000 1.0000
1.0000"
1.0150 1.0214 1.0149 1.0290 1.0300 1.0300 1.0300 1.0164 1.0163 1.0143 1.0147 1.0101 1.0164 1.0146 1.0146 1.0146 1.0146 1.0141 1.0279 1.0139 1.0139 1.0139 1.0130 1.0130 1.0130 1.0130 1.0130 1.0130 1.0130
1.oooo "
Tons/day
ROI
806 807 802 788 796 807 804 804 796 812 804 803 798 810 810 798 805 797 809 791 791 807 798 796 801 811 811 803 806 807"
6.827 7.349 10.221 10.497 10.838 10.866 11.351 12.351 12.994 13.165 13.242 13.258 13.696 13.923 13.929 14.626 15.017 15.449 15.534 15.609 15.636 15.885 16.509 16.542 17.289 18.431 19.22 20.00 20.03 19.94"
~
2.5000 2.5132 2.6789 3.1173 3.1618 3.1766 3.1766 3.1765 3.1745 3.4603 3.4654 3.4654 3.4649 3.4652 3.4652 3.4652 3.4652 3.4420 3.4459 3.4459 3.4547 3.4459 3.4459 3.4459 3.4459 3.4459 3.4459 3.4459 3.4459 3.2640"
Ind. Eng. Chem., Process Des. Dev., Vol. 16,No. 3, 1977
339
Table IV. Sensitivity Study for 800 Ton/Day Methanol Process. Variable and ROI Values v1 ROI V2 ROI v3 ROI v4 ROI v5 ROI V6 ROI V7 ROI V8 ROI
2200.0 20.03 500.0 20.03 2.66 19.15 0.717 19.90 2900.0 18.17 0.040 17.60 1019.0 19.50 0.344 20.03
2160.0 19.35 480.0 19.63 2.56 20.03 0.667 19.92 2800.0 18.06 0.030 18.25 1016.0 19.28 0.334 20.03
anol conversion are a t their upper limit, while synthesis loop pressure and recycle purge are a t their lower limit. Converter temperature and steam and carbon dioxide to methane ratios are intermediate. No attempt was made to adjust the boundaries of these variables during this study, although this step would certainly be indicated for an industrial study. The most significant cost items are raw materials and compression; therefore, it is expected that the combination of variables would tend to minimize these costs. High temperatures and low pressures favor conversion in the reformer. However, low reformer pressure increases compression costs. Therefore, the combination of maximum temperature and pressure is not unexpected and in accordance with current design practice for reforming in ammonia synthesis, although somewhat different from other MeOH studies (Shah and Stillman, 1970). Case studies about the optimum, listed in Table IV, show the ROI to be particularly sensitive to a reduction in temperature. Lower pressures reduce ROI, but less significantly, as expected. Low synthesis pressure and high conversion per pass lower compression costs but increase the size of the converter. Low purge rates conserve raw materials, but slightly increase compressor cost. Therefore, optimal selection of the minimum pressure and purge rate and the maximum conversion are reasonable for the synthesis loop. Table IV shows the ROI to be particularly sensitive to changes in pressure and purge rate. The optimal result of intermediate values of steam and carbon dioxide to methane ratios and near minimal converter inlet temperature are in accord with the findings of Shah and Stillman (1970). The values of the variables for the 29 improved search points are also given in Table 111. The ROI was improved from an initial value of 6.8 to an optimal value of 20.3. In this case, optimization resulted in additional net earnings of about $4.5 MM per year, for an investment in computer time of about $2500. As expected, the fastest improvement is obtained early in the search, the first half of the search reaching an ROI of 15 percent. The last hour and a half of the search resulted in improving the ROI about 4 percentage points. This improvement represents a saving of about $1 MM per year for a minimal investment in computer time. For this plant, am improvement of 1% in ROI represents about $250 000 per year in net earnings. The engineering time (6 man-months) to create the model might cost as much as $20 000. Therefore, the engineering time and computing time can easily be justified by even a slight improvement in ROI. Clearly, optimization with a detailed simulation model is one of the best investment opportunities available to the chemical company. 340
Ind. Eng. Chem., Process Des. Dev., Vol. 16, No. 3, 1977
2120.0 18.40 460.0 19.25 2.46 19.96 0.617 20.03 2700.0 18.98 0.020 18.64 1013.0 20.03 0.324 18.92
440.0 19.50 2.36 20.29 0.567 18.94 2600.0 19.15
2.26 20.62 0.517 18.86 2500.0 20.03
0.010
20.03 1010.0 19.28 0.314 19.20
1007.0 19.50 0.304 18.33
The experienced engineer may be able to design much closer to the optimum than the starting points in this study, possibly selecting the proper boundary for those variables which were optimal a t a boundary. Such a selection for all five bounded variables is somewhat questionable, however, in view of the findings of other studies and the usual design conditions listed in Table I. Observation of the progress of the search in Table I11 shows that even after all variables reached a bounded condition, the ROI was still improved about 2%. Table IV shows that very small changes in some variables have a pronounced effect on the ROI. Therefore, considerable process improvement may be possible, even when starting much closer to the optimum. Optimization of a process after it is operating is certainly important. However, unless the initial design is first optimized, many opportunities may be lost. For example, once the compressors are ordered, the opportunity to change pressure, purge or reactor conversion may be lost, or a t least, tightly constrained. The greatest flexibility in optimization is available during the early design period. Therefore, the preparation of the process simulation model should be one of the first objectives. Other advantages, of course, acrue from having the model, such as development of control strategies, studying synthesis alternatives, evaluating effluent problems, etc. The accuracy of the simulation must be considered in deciding how far to carry the optimization procedure and in establishing the credibility of the results. In all of the iterative computations, convergence errors are introduced. These errors can be reduced by specifying tight tolerances, of course, at the expense of computer time. The production rates shown in Table I11 indicate the variability allowed in this study. Tolerances were specified to control the production rate within 1.5% (12 TPD). This same level of variability would be reflected in the ROI; therefore, convergence of the optimization should not be attempted below the tolerance specified for the material and energy balances (0.3% ROI in this study). Looser tolerances might be used early in the search, with some saving in computer time. The results of the second optimization run of the 800 T P D plant are shown as the last entry in Table 111. The objective functions agree within the specified tolerance, as do the values of the independent variables. The agreement of these two cases confirm location of the optimum. 2. Optimization of 1000 TPD MeOH Plant. The search history and sensitivity studies for a 1000 T P D plant are given in Tables V, VI, and VI1 of the supplementary microfilm. The history of this search shows that in the first hour, the ROI improved from 7 to 18%. Another 1.5 h of searching resulted
in a maximum ROI of 22010. Again, the improvement in process economics clearly appears t o justify the expenditure of engineering and computer time. As expected, the values of the independent variables a t the optimum are the same as for the 800 T P D plant. T h e improvement in ROI from 20 to 22% is, of course, due to the economies of scale of the larger plant. 3. Optimization of MeOH Plant with Fixed Feed Rate. The results of optimization of a MeOH plant with a fixed feed rate of 1350 mol of methane per hour are shown in Tables VIII, IX and X of the supplementary material. This study was performed without use of the production rate control module. The optimal values of the variables were nearly identical with those of the larger plants. Variations in the production rate for the improved search points were found to be a t most f6%,indicating t h a t a successful optimization might be performed without iterations to control the production rate. However, the CPU time for this run was about the same as for the other studies. T h e time for each ROI computation is reduced, as expected, for the fixed feed case (1 min compared to 1.6 min); but the search was much slower in locating valid search points. This can be explained by the fact that the ROI is a much smoother function lor a fixed plant size. Therefore, it becomes easier to locate values of the variables that will improve the economics when the plant size is fixed. It may be concluded that while the computer time is reduced in evaluating the function with a fixed feed rate, the time for optimization cannot be expected to improve. This is probably true, regardless of the optimization algorithm employed, when ROI or earnings are chosen as the objective function.
Conclusions It may be concluded that a detailed flowsheet simulation model of a complex chemical process can be used for optimization without use of unreasonable amounts of computer time. The expenditure of engineering time to prepare the model and computer time for optimization represents one of the best investment opportunities available. T h e lack of experienced personnel to do optimization studies may be a realistic barrier, but one that can only be overcome by practice.
Nomenclature DJ = exit temperature of J t h catalyst bed ( J = 1, 3, 5 , 7), I3 D K = inlet temperature of the K t h catalyst bed ( K = 2 , 4 , 61, O R D8,D9, D10 = critical ratios of carbon deposition Literature Cited Ballman, S. H., M. S. Theis, University of Missouri, Rolla, Mo., 1976. Barneson, R. A . , Brannock. J. C., Moore, J. G.. Morris, C., Chem. Eng., 77 (16), 132 (July 27,1970). Bracken. J., McCormick, G. P., "Selected Applications of Non-linear Programming," Wiley, New York, N.Y., 1968. Friedman, P., Pinder, K. L., Ind. Eng. Chem., Process Des. Dev., 11, 512
(1 972). Gaines, L. D., Gaddy. J. L . , Ind. Eng. Chem., Process Des. Dev., 15, 1 (1976). Gaines, L. D., Gaddy, J. L., "University of Missouri-Rolla PROPS User's Guide," University of Missouri-Rolla, Mo., 1974. Hedley. B., Powers, W., Stobaugh, R. B., Hydrocarbon Process, 50 (9), 275
(1970). Heuckroth, M. W., Gaines. L. D.. Gaddy, J. L., AIChE J., 22 (4), 744 (1976). Hougen. 0. A.. Watson, K. M., Ragatz. R. R., "Chemical Process Principles," 2nd ed, pp 986,and 1048.Wiley, New York. N.Y., 1966. Kelahan. R. C., Gaddy, J. L., "Variable Elimination for Optimization with Difficult Inequality Constraints," submitted to AlChEJ., 1977. Komatsu, S., Ind. Eng. Chem., 60,2 (1968). Mehta, D. D., Ross, D. E., Hydrocarbon Process., 49 (1l), 183 (1970). Morse, P. L., Hydrocarbon Process., 52 (l), 113 (1973). Motard, R. L., Shacham, M., Rosen, E. M.. AlChEJ., 21 (3).417 (1975). Natta, G., "Synthesis of Methanol," "Catalysis," P. H. Emmett, Ed., Voi. 3,pp 394-411, Reinhold, New York. N.Y., 1955. Seader, J. D., Dallin, D. E., Chem. Eng. Computing, Vol. 1. AlChE Workshop Series (1972). Shah, M. J., Stillman, R. E., Ind. Eng. Chem., Proc. Des. Dev., 62 (12),59
(1 970). Shannon. P.T., Johnson, A . I., Crowe, C. M., Hoffman, T. W., Hamielec, A. E., Woods, D. R., Chem. Eng. Prog., 62 (6), 49 (1966). 5 5 (1970). Strelzoff, S., Chem. Eng. Prog. Symp. Ser., 66 (98), Umeda. T., Ichikawa, A.. Ind. Eng. Chem., Process Des. Dev., IO, 229 ( 1971).
Wellman, D., Katell. S.. Hydrocarbon Process Pet. Refiner, 42 (6),135 ( 1963).
Receit'ed f o r reciew J u n e 2 2 , 1976 Accepted M a r c h 25, 1977 Presented a t t h e 81st N a t i o n a l M e e t i n g of t h e A m e r i c a n I n s t i t u t e of C h e m i c a l Engineers, Kansas C i t y . M o . , April 1976. S u p p l e m e n t a r y M a t e r i a l A v a i l a b l e . M o d e l flowsheet s i m u l a t i o n
( l i pages, i n c l u d i n g 6 tables). O r d e r i n g i n f o r m a t i o n is g i v e n o n a n y c u r r e n t m a s t h e a d page.
The Crystallization and Drying of Polyethylene Terephthalate (PET) Brian D. Whitehead Zimmer Aktiengesellschaft, 6000 Frankfurt am Main 60, West Germany
Following a brief discussion of PET crystallization theory, the theory of PET drying is discussed in detail, together with a review of the required data. The application to practical processes and associated problems is then reviewed, before summarizing future developments.
Introduction This paper discusses the crystallization and drying stages necessary before the final processing of polyethylene terephthalate (PET) to finished products. The underlying theory of these two unit operations will be summarized, together with a review of the necessary physical property data. Then the problems encountered in drying processes will be discussed.
Finally, future developments in PET drying technology will be outlined.
The
for Drying PET T h e production and processing stages of PET are still mostly carried out at separate locations or even by separate companies. Even within one company, it is rare t o find that Ind. Eng. Chem., Process Des. Dev., Vol. 16,No. 3, 1977
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