Ind. Eng. Chem. Res. 1992,31, 1998-2003
1998
Nomenclature G ( X . . )= information gain obtained when splitting at Xij Inf(4= amount of information needed to generate a decision tree from a data set S Ni = number of instances whose class is wi in the training set S
Ns = number of instances in the training set S NsL= number of instances in the subset SL where Xi IXi, NSR= number of instances in the subset SRwhere Xi > Xij S = training set Sb= between-class covariance matrix SL= a subset in the left-hand node SR = a subset in the right-hand node S, = within-class covariance matrix Pi = probability of class wi, Le., N i / N s PsL= probability of subinterval X i IX i j , i.e., NsJNs PSR= probability of subinterval X i > Xij, i.e., NsR/Ns X i = any attribute in the instances (records) Xi, = a value of the attribute X = attribute vector of records Ti = observed output Yi = prediction of Yi p i = mean vector in class wi p = mean vector of population wi = class in the instances
Literature Cited Bernstein, I. H.; Garbin, C. P.; Teng, G. K. Applied Multivariate Analysis; Springer-Verlag: New York, 1988; pp 11-13. Breiman, L.; Friedman, J. H.; Olshen, R. A,; Stone, C. J. Classification and Regression Trees; Wadsworth, Inc.: Belmont, CA, 1984. Draper, N. R.;Smith, H. Applied Regression Analysis, 2nd ed.; Wiley: New York, 1981;pp 327-332. Geladi, P.;Kowaleki, B. R.Partial Least-Square Regression: A tutorial. Anal. Chim. Acta 1986,185, 1-17.
Hand, D. J. Discrimination and Classification;Wiley: New York, 1981. Hunt, E. B.; Marin, J.; Stone, P. J. Experiments in Induction; Academic Press: New York, 1966. James, M. Classification Algorithms; Wiley: New York, 1985. Joseph, B.; Wang, F.; Shieh, D. Knowledge Acquisition from Routine Data: A Comparison of Statistical Methods with Artiicial Neural Networks. Comput. Chem. Eng. 1992, in press. Mantaras, R. A Distance-Based Attribute Selection Measure for Decision Tree Induction. Mach. Learn. 1991, 6, 81-92. Mingers, J. An Empirical Comparison of Selection Measures for Decision-Tree Induction. Mach. Learn. 1989, 3, 319-342. Quinlan, J. R.Learning Efficient Classification Procedures and Their Application to Chess End Games. In Machine Learning; Michalski, R. S., et al., Eds.; Morgan Kaufmann: Los Altos, 1983; Vol. 1, Chapter 15,pp 463-482. Quinlan, J. R. Induction of Decision Trees. Mach. Learn. 1986,1, 81-106. Quinlan, J. R. Simplifying Decision Trees. Znt. J. Man-Mach. Stud. 1987,27, 221-234. SAS Institute. SASISTAT User's Guide: for Personal Computers, release 6.03 edition; SAS Institute Inc.: Cary, NC, 1988 pp 909-922. Shannon, C. E.; Weaver, W. The ikfathematical Theory of Communication; The University of Illinois Press: Urbana, 1964. Weiss, S. M.; Kulikowski, C. A. Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems; Morgan Kaufmann: San Mateo, CA, 1991. Wong, J. H.; Wong, S. K. M. An Inductive Learning System-ILS. In Methodologies for Intelligent System; Ras, 2.W., Zemankova, M., Eds.; North-Holland: New York, 1987;pp 370-378. Wu, H. T. Knowledge Based Control of Composite Material Manufacturing Processes. D.Sc. Dissertation, Department of Chemical Engineering, Washington University at St. Louis, 1990. Young, T. Y.; Calvert,T. W. Classification, Estimation and Pattern Recognition; American Elsevier: New York, 1974;pp 109-158.
Received for review August 21, 1991 Revised manuscript received April 21, 1992 Accepted May 26, 1992
Kinetics of Dilute Acid Hydrolysis of Cellulose Originating from Municipal Solid Wastes Ilan A. Malester,*Michal Green, and Gedaliah Shelef Environmental and Water Resources Engineering Department, Technion-Israel Institute of Technology, Haifa 32000, Israel
Kinetics of dilute acid hydrolysis of cellulose originating from municipal solid wastes (MSW) were determined on the basis of experimental results from a hydrolysis batch reactor. pH values were used as a measure for the acidity instead of the usual sulfuric acid concentration (weight percent), which was found to be unsuitable due to the significant effect of the neutralizing capacity of lig nocellulosic materials. The hydrolysis experiment results were used to determine the rate constants of cellulose and glucose hydrolysis (kland k,) together with the glucose yield (Y). The kinetic parameters of the process (El, n,klo and E2,n,k20)were then calculated. These results led to the calculation of the maximal glucose yields (Ymm) and the corresponding reaction times (tOpt),which were found to be directly dependent on the experimental conditions (temperature and pH) and could be expressed by a new set of equations. It was found that within the range of hydrolysis conditions studied (pH 0.34-0.85 and temperature 2 W 2 4 0 "C),the best conditions for MSW hydrolysis were the most severe ones: pH 0.34 and temperature 240 O C . Under these conditions, a glucose yield of 54.30% was achieved at a reaction time of 4.6 s. Introduction A process for treating municipal solid wastes (MSW) producing a valuable liquid fuel-ethanol-has been developed at the Technion during the past few years (Shelef *To whom correspondence should be addressed. Present address: Ministry of the Environment, Marine and Coastal Environment Division, POB 6234,Jerusalem 91061,Israel.
et al., 1987; Green et al., 1988; Malester, 1989). This process is based on high-temperature dilute acid hydrolysis of MSW converting cellulose to glucose, which is then fermented to ethanol. All the by-products are recovered. This paper deals with the determination of the kinetic parameters of cellulose hydrolysis originating from MSW. The basic kinetic study on cellulose hydrolysis has been established by Saeman (19451,who found that the high-
0888-588519212631-1998$03.00/0@ 1992 American Chemical Society
Ind. Eng. Chem. Res., Vol. 31, No. 8,1992 1999 /I
I
h e
($4
Figure 1. Calculated glucose yield from the kinetic parameters in
the literature.
temperature dilute acid hydrolysis of cellulose can be described by pseudo-homogeneous consecutive fit-order reactions, as shown by the following reaction: cellulose (A)
4 ' & H
k, .,o .
glucose (B) decomposition products (C) (1)
The rate constants k , and k2 can be expressed by an Arrhenius equation:
kl = k,.(Ac)" exp(-E,/RTj
(2)
kz = k,(Ac)* exp(-Ez/RT) (3) where k,. and kz. are the frequency factors (independent of temperature and concentrations); Ac is the acidity, which is usually expressed by the acid concentration (weight percent); m and n are constants, expressing the effect of the acid; E, and Ez are the activation energies (in calories); R is the ideal gas constant; and T is the temperature in kelvin. Results from previous kinetic studies (Saeman, 1945; Mc Kibbins, 1958; Mc Kibbins et al., 1962; Fagan et al., 1971; Santini and Vaux, 1976; Grethlein, 1978; Thompson and Grethlein, 1979;Alpay et al., 1979;Churcb and Wooldridge, 1981;Titchener and Guha, 1981; Franzidis, 1982; Bhandari et al., 1983; Ullal et al., 1984; Harris et al., 1985) on the acid hydrolysis of MSW were difficult to compare (Malester et al., 1988; Malester, 1989) with one another. The main reason for these discrepancies was the use of sulfuric acid concentrations (weight percent) as a measure of the acidity, while disregarding the influence of the neutralizing capacity of the materials, as well as the nonlinearity of the resultant H30+concentrations. The neutralizing capacity of the MSW was found, in a previous work (Malester et al., 1988), to be very significant. Glucose yields were calculated using the kinetic model equation (eq 5) and the kinetic parameters published by the above authors. A wide range of glucose yields was observed (Figure 1)for the same experimental conditions (temperature 230 "C and 2.0 wt % acid concentration). In this research, pH was used as a measure of acidity, whose measurement takes into account the neutralizing capacity of the materials, thus allowing for more accurate kinetic calculations. Material and Methods The hydrolysis experiments were conducted in a 2-L steel batch reactor ( F i i e 2) which was designed and built at the Tecbnion. The acid was injected into the reactor only after the substrate (MSW and water) was heated to the desired temperature (about 230 "C), hence avoiding
1
~
~
~
~
~
~ lank ]
f
l
o
s
h
Figure 2. Scheme of the hydrolysis reactor system.
prehydrolysis. After a predetermined reaction time (a few seconds to 1min), the reactor content was discbarged into to a flash tank, with the expansion immediately cooling the substrate to a temperature of about 80 "C, thereby quenching the hydrolysis reaction. The MSW used in the experiments were collected from two sources: a Haifa suburb collecting system (Israel) and a Florida MSW wet pulping treatment plant (USA). T w o pretreatments were used medium grinding (particle diameter less than 2 mm) or wet pulping (particle diameter of a few centimeters). The experiments were conducted under the following conditions: temperature range of 200-240 "C, pH values of 0.34-0.85, and solids concentrations of 512%. The acidification was achieved with concentrated sulfuric acid (Frutarom). The acid concentration was taken as the H,O+ concentration determined fmm the pH values. The pH was measured with a pH meter, El Hama 720 (f0.01 pH), and a glass electrode. Temperature was controlled by two copperConstantan thermocoudes (reference and measure): a fiied countervoltage w& applied in order to get &mal sensitivity: f0.3 "C a t 230 O C . The glucose concentrations were determined by enzy. matic analysis (Sigma). The cellulose concentrations (as glucme) were determined by quantitative saccharification (Saeman et al., 1945). ~
Results and Discussion The kinetic results were based on 200 hydrolysis experiments. For each experiment, the initial cellulose concentration,the residual cellulose concentration, and the glucose yield were determined as well as physicochemical characteristics. The experiments were assembled in 27 groups of uniform experimental conditions (temperature and pH).
2000 Ind. Eng. Chem. Res., Vol. 31, No. 8, 1992 3.50
-
3.00
-
I\
kc
(Cellulose)
h
8 2.50-
\
8 2.001.50 1 .oo 0.50 -
0
v
-I
+
pH=0.42: Temp=225'C
0.00 -I
0.85
-d
90.00-
8 -
60.00-
h
pH=0.42; Temp. 2 2 5 ' C
bp
0
\
80.00-
A
kz ( Q l u ~ )
I
=\:
50.004
"I
4 -s E
I
Figure 6. Effect of pH and temperature on cellulose hydrolysis rate constant kl.
i j
70.00-
204'C
40.00
0
8
E
30.00
20.00 10.00
0.00 0.0
5.0
I
10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 time (sec)
Figure 4. Degradation of cellulose: residual cellulose at 225 "C and pH 0.42;(kl= 0.0747 s-l; 13 experiments). 70.00 60.00
I
I k1
-
= 0.0773
0.85
Figure 7. Effect of pH and temperature on glucoae hydrolysis rate constant kl.
/I
0
".Y"V
F 50.00 -
I
-0.500 h
W
40.00
-
30.00
-
20.00
1
10.00
-
j:
3
204.C
-1.500
Exparim. Yields
1 -3.000
w
pH=0.42; Temp.=225'C
''
-3.500
IO
Figure 8. Determination of the kinetic parameter m (acid dependence) at 230 "C (from transposed rate constants kl at 230 "C).
perature and lower pH (Figures 6 and 7). The rate constants of the glucose degradation were usually smaller than those of cellulose hydrolysis. Rate constants and glucose yields for the two types of MSW (Israeli and American) were found to be similar. The technique used for pretreatment (grinding or wet pulping) and the particle size (smaller than 2 mm or a few centimeters) had no effect on the rate constants. Kinetic Parameters: Activation Energy ( E l ,E2), Acid Dependence (m, n),and Frequency Factor (klo, k%). The experimental rate constants of cellulw (k,)and glucose (k,) hydrolysis, at different temperatures and pH values, were used to determine the acid dependence parameters rn and n,the activation energies Eland E2,and the frequency factors klo and kzo. The acid dependence parameters m and n were calculated by classical methods from the slopes of the linear regressions of In (k,) for cellulose (eq 6) and In (k,) for
Ind. Eng. Chem. Res., Vol. 31, No. 8, 1992 2001 0.000
Table I. Kinetic Parameters of MSW Cellulose and Glucow Hvdrolmis glucose hydrolysis cellulose hydrolysis k l = 0.17458-l rate const at 235 "C k2 = 0.0874 8-l and pH 0.42 kzo = 6.89 X 10138-l k l , = 1.94 X 10" s-I freq factor acid dependence n = 213 m = 1.00 (Ac = 10TH) E2 = 34000 cal El = 41000 cal activation energy
(6)
In (k2) = In (kZo)- E2/RT - 2.303n(pH)
(7)
I
3
6 -1.000
c
!-1.500
8
-2.000
c
e -2.500 { -3.000
v
glucose (eq 7) versus pH, at fixed temperatures: an example for the m determination is given in Figure 8.
In (k,) = In (klo) - E1/RT - 2.303m(pH)
,
-0.500
k 1 8 9 exp. in 7 groups
-4.500 -5.000 194.00
206.00
198.00
202.00 Inverse of temp.: 1 / T * l O O
210.00
000 (K-' )
Figure 9. Determination of the kinetic parameter El, the activation energy at pH 0.42(from transposed rate constants kl at pH 0.42).
The activation energiea El and E2were determined from the slopes of the linear regressions of In (k,)for cellulose (eq 8), and In (k,) for glucose (eq 9) versus the inverse of
-"
I
/I-
200'C 220'C
In (k,)= In (lO-(pH)"klo) - (El/R)(l/T)
(8)
In (k,) = In (10-(pH)"kz0) - (E2/R)(l/T)
(9)
225'C 230'C 235'C
the temperature 1/T, at fixed pH values: an example for Eldetermination is given in Figure 9. The frequency factors kl, and k% were calculated from eqs 10 and 11,based on the previous determination of the
0.2
0.3
0.4
0.5
0.6
0.7 0.8 0.9
1
1.1
1.2
PH
klo = 10(pH)"k1exp(E1/RT)
(10)
kzo = 10(pH)"k2 exp(E2/RT)
(11)
Figure 10. Glucose maximal yield w pH and temperature ("C) based on kinetic data from this work.
eters m and n were in total accordance with the previous results. muations 14 and 15 present an example of transpition of the rate constants kl and k2 at pH 0.42, using the previously calculated parameters m and n.
parameters El, m, E2,and n,and the calculated values of the rate constants kl and k2 at 230 O C and pH 0.42. Verification and Conformity of the Parameters to the Kinetic Model. The experimental hydrolysis rate constants and k2(T,pH) were transposed to different temperatures and/or pH values in order to test the accordance of the obtained parameter values ta the kinetic model. The transposed values of the rate constants were in close agreement with results observed under the same experimental conditions. F,quations 12 and 13 present an example of transposition of the rate constants kl and k2 at 230 "C,using the previously calculated parameters El and E2. kl(M3.16)
= kl(nexp[(E1/N1/T - 1/503.16)1 (12)
kZ(503.16)
= ~ z ( T , exp[(EZ/R)(l/T
- 1/503.16)1
kl(0.42) = 10pH4'.42mkl(pH)
(14)
These transpositions of the rate constants at pH 0.42 provided a wide base (204 O C < temperature C 238 O C ) for a determination of the activation energies El and E2 (Figure 9). The obtained new values of the parameters El and E2were also in total accordance with the previous results. The internal accordance of the calculated kinetic parameters with the experimental results and the kinetic model proved the validity of the model and the results. These results were also used to calculate the frequency factors kl, and kZobased on eqs 10 and 11. The calculated values of the kinetic parameters are presented in Table I.
(13)
These transpositions of the rate constants at the temperature of 230 O C provided a wide base (0.34 < pH C 0.85) for a determination of the acid-dependent parameters m and n (Figure 8). The obtained new values of the param-
Table 11. Calculated Rate Constants (k,and k2,8-l) of Cellulose and Glucose Hydrolysis at Different pH's and Temperatures ( O C ) , Bared on Kinetic Parameters Obtained in This Work 0.34 0.42 0.55 0.85 PH h, mol/L 0.457 0.380 0.282 0.141 Ac, % w/v 4.372 3.620 2.658 1.288 temp, O C 200 210 220 225 230 235 240
kl
k2
kl
k2
kl
k2
kl
k2
0.01043 0.02570 0.061 06 0.09291 0.14020 0.20985 0.31163
0.00820 0.01732 0.03551 0.05030 0.07075 0.09884 0.13720
0.00867 0.02137 0.050 79 0.07728 0.11661 0.17454 0.25920
0.00725 0.01532 0.03141 0.04448 0.06257 0.08742 0.121 35
0.006 43 0.01584
0.005 94 0.012 55 0.025 73 0.03644 0.05125 0.071 61 0.09940
0.003 22 0.00794 0.01887 0.02871 0.04333 0.064 85 0.096 30
0.00375 0.00792 0.01623 0.02299 0.03234 0.045 18 0.06271
0.03765 0.05729 0.08645 0.12939 0.19215
2002 Ind. Eng. Chem. Res., Vol. 31, No. 8, 1992
e= 4 0 -I
210
del Technion Fellowship, and Ralph Levitz Fellowship,is gratefully acknowledged.
f / / i/
::
$50-
02
03
04
05
06
07
08
09
1
I '
PH
Figure 11. Optimal reaction time vs pH and temperature ("C) based on kinetic data from this work.
Maximal Glucose Yield and Optimal Reaction Time. The kinetic parameters obtained (Table I) were used to calculate the rate constants (k,and k2)of cellulose and glucose hydrolysis (Table 11) at different pH's and temperatures ("C). These resulta were used to calculate the maximal glucose yields Y,, and optimal reaction times top,,under different hydrolysis conditions (Figures 10 and ll),based on the following equations:
where kland k2 were obtained with the Arrhenius equations (eqs 2 and 3; Table 11); A = l + Yo(kl- k 2 ) / k 1 ,Yo = initial glucose yield (here 3.5%), and K = k , / k 2 (selectivity of the reactions). A set of equations (18 and 19) was derived which enable the calculation of the maximal glucose yield and the optimal reaction time for any set of hydrolysis experimental conditions (pH and temperature): Yrn,(pH,6') = 0.2888 - 15.265pH - 9.44 topt(pH,6')= exp(4.07786'
+ 1.936pH + 19.51)
(18)
(19)
where the temperature 6' is in "C. These equations are valid under the conditions studied: temperature from 200 to 240 O C and pH from 0.34 to 1.00.
Conclusion The high-temperature dilute acid hydrolysis of cellulose from MSW could be described by pseudohomogeneous consecutivefirsborder reactions. The use of pH values in the kinetic study proved to be much more suitable than applied acid concentration values. As a consequence of m / n and E,/E, ratios being found to be higher than 1,it could be concluded that the maximal yield of glucose can be increased by using more severe hydrolysis conditions (lower pH and higher temperature). The maximal glucose yield and optimal reaction time were found to be directly related to the experimental conditions. Acknowledgment This research was carried out due to the dedication of the technical staff of the Department of Environmental and Water h u r c e a Engineering at the Techxiion-Israel Institute of Technology, Haifa, Israel. This research was partially supported by the government of Israel and Paz Oil Company. The generous financial help of the Belle Bernstein Fellowship,Association Venezolana de Amigos
Nomenclature Ac: acid concentration, usually measured in wt %, here measured as h = 10-pH CA: residual cellulose concentration (as glucose), % CAo: initial cellulose concentration (as glucose), % CB: residual glucose concentration, % CBo: initial glucose concentration, 70 E,: activation energy of cellulose hydrolysis, cal E2:activation energy of glucose hydrolysis, cal h: lo-@; [H,O+] concentration, mol/L k,: rate constant of cellulose hydrolysis, s-l k,: rate constant of glucose hydrolysis, s-l k,,: frequency factor of cellulose hydrolysis, s-l k,: frequency factor of glucose hydrolysis, s-l K k l / k z = selectivity of the hydrolysis reactions m: constant expressing the acid dependence of the cellulose hydrolysis n: constant expressing the acid dependence of the glucose hydrolysis Y: yield of glucose, % Yo: initial yield of glucose, % Ym,: maximal yield of glucose, % t: time of reaction, s to t: optimal time of reaction, S d' temperature, K 8: temperature, O C Registry No. Cellulose, 9004-34-6; glucose, 50-99-7. Literature Cited Alpay, H. E.; Naushahi, M. K.; Saeistowski, H. Conversion of cellulosic part of municiDal waste into industrial ethanol. J. Sci. Res. 1979,-31 (1-2), 67-?3. Bhandari, N.; Mc Donnald, D. G.; Bakhshi, N. N. Kinetic studies of corn stover saccharification using- sulfuric acid. Biotechnol. Bioeng. 1983,26, 320-327. Church, J. A.; Wooldridge, D. Continuous high-solida acid hydrolysis of biomass in a ll/Jn. plug flow reactor. Znd. Eng. Chem. Prod. Res. Deu. 1981, 20 (2), 371-378. Fagan, R. D.; Converse, 0.; Grethlein, H. E.; Porteous, A. Kinetics of the acid hydrolysis of cellulose found in paper refuse. Enuiron. Sci. Technol. 1971, 5 (6),545-547. Franzidis, J. P. The development of a continuous reactor for the acid hydrolysis of cellulose and its application to refuse disposal. PLD. Dissertation, Faculty of Technology, Open University, Milton Keynes, UK, 1982. Green, M.; Kimchie, S.; Malester, I. A.; Rugg, B.; Shelef, G. Utilization of municipal solid wastes (M.S.W.) for alcohol production. Biol. Wastes 1988, 26, 285-295. Grethlein, H. E. Chemical breakdown of cellulosic materials. J. Appl. Chem. Biotechnol. 1978,28,296-308. Guha, B. K.; Fowlder, G. E.; Titchener, A. L. Engineering evaluation of chemical conversion of wood to liquid fuel alcohol. Proceedings of the Alcohol Fuels Conference; Inst. Chem. Eng., N.S.W. Group: Sydney, Australia, 1978; pp 8/64/11. Harris, J. F.; Baker, F.; Andrew, J.; Conner, A. H.; Jeffries, T. W.; Minor, J. L.; Petterson, R. C.; Ralph, W.; Sringer, E. L.; Wegner, T. H.; Zerbe, J. I. "Two-stage, dilute sulfuric acid hydrolysis of wood: an investigation of fundamentals". Gen. Tech. Rep. FPL-45; USDA, Forest Service, Forest Products Laboratory &port: Madison, WI, 1985; 73p. Malester, I. A. Kinetics of acid hydrolysis of cellulose in municipal solid wastes. Ph.D. Dissertation, Technion-Israel Institute of Technology, Haifa, Israel, 1989. Malester, I. A,; Green, M.; Kimchie, S.; Shelef, G. The effect of the neutralizing capacity of cellulosic materials on the kinetics of cellulose dilute acid hydrolysis. Biol. Wastes 1988,26,115-124. Mc Kibbins, S. W. Kinetica of the acid catalyzed conversion of glucose to 5-hydroxymethyl-2-furaldehyde and levulinic acid. Ph.D. Dissertation, Department of Chemical Engineering, University of Wisconsin, 1958. Mc Kibbins, S. W.; Harria, J. F.; Saeman, J. F.; Neill, W.K. Kinetics of the acid catalyzed conversion of glucose to 5-hydroxymethyl2-furaldehyde and levulinic acid. For. Prod. J. 1962,12 (l),17-23.
Ind. Eng. Chem. Res. 1992,31, 2003-2010 Saeman, J. F. Hydrolysis of cellulose and decomposition of sugars in dilute acid at high temperature. Znd. Eng. Chem. 1945,37(l), 43-52. Saeman,J. F.; Bubl, J. L.; Harris,E. E. Quantitative saccharification of wood and cellulm. Znd. Eng. Chem., Anal. Ed. 1946,17,35-37. Santini, G. S.;Vaux, W. G.Biochemical conversion of refuse to ethyl-alcohol. AZChE Symp. Ser. 1976,72 (No. 158),9s103. Shelef, G.; Green, M.; Kimchie, S.; Maleeter, I. A. "Exploitation of organic wastes in Israel to produce ethanol and by-products"; Final report (in Hebrew); The Technion R&D Foundation La., Haifa, Israel, 1987. Thompson, D. R.;Grethlein, H. E. Design and evaluation of a plug
2003
flow reactor for acid hydrolysis of cellulose. Znd. Eng. Chem. Prod. Res. Dev.1979, 18 (3), 166-169. Titchener, A. L.; Guha, B. K. 'Acid hydrolysis of wood"; Report No. 56,New Zealand Engineering Research and Development Committee, University of Auckland, New Zealand, 1981. Ullal, V. G.;Mutharaaan, R.; Grossmann, E. D. New insights into high solids acid hydrolysis of biomass. Biotech. Bioeng. Symp. 1984,No. 14,69-93. Received for review September 6,1991 Revised manuscript received March 23, 1992 Accepted April 13, 1992
Partial Least Squares for Mass Spectral Analysis of H2and D2Plasma Scrambling Products Sourav K. Sengupta,+Scott C. Cheeseman,' Steven D. Brown,$and Henry C. Foley*JJ Center for Catalytic Science and Technology, Department of Chemical Engineering, and Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716
The application of the partial least squares (PLS) method to resolve the mass spectrum of hydrogen and deuterium scrambling in a fast flow plasma reactor is reported. Calibration was performed by using a mixture of hydrogen, deuterium, and deuterium hydride, and the model predictions were validated against the experimental results. The calibration model was then extended to actual plasma scrambling experiments, and the effects of power, residence time, pressure, and inlet mole fraction of H2and D2were investigated. While residence time and applied power were found to have no significant effect on the deuterium hydride concentration within the operating regime tested here, changes in pressure and inlet mole fraction affected the conversion of H2and Dzto HD, as expected. The results from this study can be applied toward a more rational design of fast flow plasma or downstream etching reactors used in advanced materials fabrication.
Introduction Due to the increasing importance of plasma technology for materials fabrication and processing, the development of new methods for the quantitation of atoms and free radicals has become increasingly important. QpicaUy, this quantitation has been performed by one or more of the following techniques: calorimetry, electron spin resonance (ESR),vibrational spectroscopy, laser fluorometry, and mass spectrometry. Mass spectrometry is quite versatile, is readily available, and can be used to determine the concentration of atoms and the molecules that result from the plasma reactions. Yet the direct measurement of free radicals by mass spectrometry requires that the analyses be carried out at ultrahigh vacuum in order to avoid recombination within the plasma reactor or in the mass spectrometer. Since the pressure regime in which plasma reactors operate for materials fabrication is orders of magnitude higher than that maintained in ultrahigh vacuum experiments, there is strong motivation to develop new methods of mass spectrometry to analyze and quantitate the products from plasma scrambling processes. In this paper we have utilized partial least squares (PLS) analysis to accomplish this. Background Isotopic labeling and scrambling experiments are used for kinetic measurement of rate constants and to elucidate mechanisms of various reactions. Recently, catalytic scrambling of hydrogen and deuterium has been reported *Author to whom correspondence should be addressed. 'Center for Catalytic Science and Technology,Department of Chemical Engineering. t Department of Chemistry and Biochemistry.
by Nakamura et al. (1987)and Yamada et al. (1989)on Ni(100)single crystals with and without modification by sulfur. In these studies, maw spectrometry has been used to quantify the hydrogen, deuterium, and deuterium hydride concentration. The authors used mass fragments 2, 3, and 4 amu and a "suitable calibration factor" to quantify H2,HD, and D2 However, determination of the calibration factor was difficult due to double scrambling that takes place, first, within the reactor in the plasma zone and then, again, in the maw spectrometer due to electron impact for ionization. This results in a doubly mixed spectrum of mass fragment ions due to H2,D2,and HD, the resolution of which requires a sophisticated mathematical model. Several methods have been used in the past to convert a set of overlapped spectroscopic data into the concentrations of the components of the mixtures giving rise to such overlapped spectra; this process is known as multivariate calibration. Mathematical methods for calibration include multiple linear regression, principal component regression, ridge regression methods such as the Kalman filter, and partial least squares regression. A number of review articles and books are available in the literature (McClure, 1987). Most of these mathematical methods for multivariate calibration have also been compared in detail by a number of researchers in the past few years (Wold, 1982; Manne, 1987; Hoskuldsson, 1988; Sanchez and Kowalski, 1988). New methods for multivariate calibration have been applied successfully to mixtures of components with very similar spectra in ultraviolet, infrared, and X-ray spectroscopies (Lindberg et al., 1986;Frank et al., 1983; Haaland and Thomas, 1988;Thomas and Haaland, 1990; Karstang and Eastgate, 1987). The goals of this project were 2-fold: first, to establish a multivariate calibration method based on partial least
0888-5885/92/2631-2003$03.00/00 1992 American Chemical Society
