J. Phys. Chem. C 2009, 113, 3181–3188
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Hydrolysis of Cellulose by a Solid Acid Catalyst under Optimal Reaction Conditions Daizo Yamaguchi,† Masaaki Kitano,† Satoshi Suganuma,‡ Kiyotaka Nakajima,‡ Hideki Kato,‡ and Michikazu Hara*,†,‡ Kanagawa Academy of Science and Technology, Sakado 3-2-1, Takatsu-ku, Kawasaki 213-0012, Japan, and Materials and Structures Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan ReceiVed: October 1, 2008; ReVised Manuscript ReceiVed: December 1, 2008
The hydrolysis of cellulose with a highly active solid acid catalyst, a carbon material bearing SO3H, COOH, and OH groups, was investigated at 323-393 K using an artificial neural network (ANN) and a response surface methodology (RSM). The ANN models developed for experimental design accurately reflect the novel solid-solid interface catalysis. The ANN models and RSM revealed that the amount of water dominates the hydrolysis reaction as well as cellulose saccharification by concentrated sulfuric acid, a conventional saccharification method. The correlations of the reaction and each parameter are discussed on the basis of the reaction mechanism, ANN, and RSM. Introduction Cellulose saccharification, the hydrolysis of cellulose into water-soluble saccharides, is a key technology to obtain useful sugars from vegetation such as grasses and agricultural and wood waste, without consumption of the provision.1 Sugars can be converted into a range of industrially important chemicals, including ethanol, hydrocarbons, and starting materials for the production of polymers.2,3 Cellulose is a water-insoluble aggregate of long-chain β-1,4 glucan composed of glucose monomers linked by β-1,4 glycosidic bonds and is converted into water-soluble saccharides by the hydrolysis of the β-1,4 glycosidic bonds and decomposition of hydrogen bonds linking β-1,4 glucan chains; therefore, substantial effort has been devoted to the development of appropriate hydrolysis schemes, including catalysis using mineral acids,4-6 enzyme-driven reactions,7 the use of subcritical and supercritical water,8-10 and solid catalysts for hydrogenolysis.11 The hydrolysis of cellulose using sulfuric acid as a mineral acid catalyst has been extensively developed since the 1940s and has been implemented on relatively large scales.4-6 Although sulfuric acid is inexpensive and acts as a highly active catalyst for this reaction, its use is wasteful and energy-inefficient, requiring separation, recycling, and treatment of the waste sulfuric acid. Recently, we reported that an amorphous carbon material with SO3H, COOH, and OH groups can function as an efficient solid catalyst for the hydrolysis of cellulose at 343-373 K.11 The carbon material can be readily prepared by sulfonation of partially carbonized cellulose and is a novel solid Brønsted acid composed of SO3H-, COOH-, and phenolic OH-bearing nanographene sheets in a considerably random fashion. The carbon catalyst can hydrolyze not only pure crystalline cellulose but also natural lingo-cellulosic reactants, such as eucalyptus or straw tips, into water-soluble β-1,4 glucan, followed by the hydrolysis of water-soluble β-1,4 glucan into glucose, just as well as mineral acid catalysts. The high catalytic performance of the carbon material for the reaction is attributable to the * To whom correspondence should be addressed. Tel.: +81-45-924-5381. Fax: +81-45-924-5381. E-mail:
[email protected]. † Kanagawa Academy of Science and Technology. ‡ Tokyo Institute of Technology.
adsorption capability for β-1,4 glucan, including cellulose. The surface of the carbon material readily attaches to cellulose and water-soluble β-1,4 glucan as the hydrolytic products of cellulose through hydrogen bonds between OH groups in the catalyst and cellulose or water-soluble β-1,4 glucan. Strongly acidic SO3H groups bonded to the carbon material function as effective active sites for decomposing strong hydrogen bonds and hydrolyzing β-1,4 glycosidic bonds in cellulose or watersoluble β-1,4 glucan, resulting in the efficient conversion of cellulose into glucose. The reaction mechanism resembles enzymatic hydrolysis of cellulose; the amorphous carbon bearing SO3H, COOH, and OH groups is not so much a solid acid as an inorganic enzyme with highly active sites. After reaction, the particulate carbon catalyst can be readily separated from the water-soluble saccharides, allowing for repeated reuse of the catalyst without reduction in activity. Hydrolysis of cellulose using a solid acid, which does not require energy for separation, recovery, and rejection of the catalyst, would provide much lower energy consumption than that using liquid mineral acid catalysts. In contrast to the carbon material, all conventional solid acid catalysts, including inorganic solid acids (zeolite, inorganic oxides) and SO3H-bearing polymer-based solid acids (strongly acidic cation-exchangeable resins), do not function as effective catalysts for the reaction, because the surfaces of these solids cannot bond to β-1,4 glucan or do not have strong acid sites. Although the hydrolysis of cellulose by the carbon material has been demonstrated to be a new type of reaction based on solid Brønsted acid catalysts, the details of the reaction are yet to be clarified. Acid-catalyzed reactions in the presence of a large quantity of water, such as hydrolysis and hydration, are conflicting reactions for acid catalysts. A large quantity of water is favorable for the promotion of the reactions in kinetics and equilibrium but decreases the acid strength and catalytic activity of acid catalysts. Because the catalytic hydrolysis of cellulose by the carbon material is a reaction between solid interfaces, it is expected that not only chemical phenomena but also physical phenomena such as contact among particles participate in the reaction. As a result, many parameters would influence the hydrolysis of cellulose. It is therefore difficult to optimize such
10.1021/jp808676d CCC: $40.75 2009 American Chemical Society Published on Web 01/30/2009
3182 J. Phys. Chem. C, Vol. 113, No. 8, 2009 a complicated reaction system. In the present study, the relationship between the rate of reaction and several parameters (the amount of water and cellulosic reactant, stirring rate, and reaction temperature) in the hydrolysis of cellulose using the carbon material bearing SO3H, COOH, and OH groups has been investigated through an artificial neural network (ANN) and a response surface methodology (RSM). ANNs are being widely used in many scientific areas, providing functions that represent variety responses.12-15 This facilitates optimizing complicated reaction systems in which many parameters participate. Although ANN is a powerful method that is expected to reveal functions representing phenomenon, it cannot clarify both interaction among variables and the significance of each variable. To clarify this, RSM has been applied to the present study. RSM is an established method in statistics,16-21 revealing the significance of each variable and the interaction among variables. Experimental Section Cellulose Saccharification. Carbon material with SO3H, COOH, and OH groups was prepared by sulfonation of partially carbonized crystalline cellulose.11,22,23 The details of the preparation and structural information of the carbon material have been described elsewhere (see the Supporting Information).11,22,23 The hydrolysis of pure crystalline cellulose (Avicel; particle size, 20-100 µm; crystallinity, 80%; degree of polymerization, 200-300) in the presence of the carbon material was carried out in a poly(tetrafluoroethylene) (PTFE) cylindrical reactor (90 cm3) with a cap with a silicone O-ring. The cap is equipped with a PTFE stirring seal with PTFE O-rings. The reaction mixture of cellulose reactant, water, and the carbon material was stirred by a PTFE-coated stirring device attached to a PTFEcoated rod through the stirring seal. The hydrolysis reaction was started by placing the reactor in an oil bath. Because the cap at the top of the reactor is cooled to 283 K, water is refluxed in the reaction system. After the reaction, a part of the supernatant solution readily obtained by centrifugation was analyzed using a high-performance liquid chromatograph (HPLC) equipped with an Aminex HPX-87H column (operating temperature, 303 K; mobile phase, 5 mM sulfuric acid (0.6 mL min-1), Biorad Laboratories). For the hydrolysis using sulfuric acid, after the removal of sulfuric acid from the reaction solution as BaSO4 precipitate by the addition of aqueous BaCO3, the remaining supernatant solution was analyzed by HPLC. The amount of water-soluble β-1,4 glucan (linear polysaccharides composed of glucose monomers linked by β-1,4 glycosidic bonds), produced by the hydrolysis of cellulose, were estimated by analyzing the glucose produced by the hydrolysis of water-soluble β-1,4 glucan using a dilute sulfuric acid solution.24 A suitable amount of sulfuric acid solution was added to part of the supernatant solution collected after the hydrolysis reaction with the carbon material; the sulfuric acid concentration in the resulting solution was adjusted to 4 wt % and was then warmed to 394 K for 1 h to hydrolyze the polysaccharides into glucose.24 The amount of glucose in the solution after sulfuric acid-catalyzed hydrolysis was estimated using the HPLC. Cellulose conversion, glucose, and β-1,4 glucan yields were obtained using the following equations.11
cellulose conversion (%) ) 100(B + C)/A glucose yield (%) ) 100B/A total β-1, 4 glucan yield (%) ) 100C/A A: total amount (mol) of glucose monomer in cellulose. B: amount (mol) of glucose produced by acid-catalyzed hydrolysis.
Yamaguchi et al.
Figure 1. ANN architecture with three hidden neurons in a single hidden layer.
C: total amount (mol) of glucose monomer in water-soluble β-1,4 glucan produced by acid-catalyzed hydrolysis. Experimental Design and Performance Estimation. The hydrolysis of cellulose by the carbon material was planned using the Box-Behnken design18-21 of the JMP package (SAS Institute Inc.). In the experimental design, the reaction temperature, reaction time, and the amount of the carbon material are 373 K, 3 h, and 3.0 g, respectively, and are constant. The rates of glucose and water-soluble β-1,4 glucan formation from cellulose in the presence of the carbon material were measured by varying the stirring rate and the amounts of cellulose and distilled water on the basis of the Box-Behnken design. Performance maps, three-dimensional (3D) maps representing the relationships between the formation rates of water-soluble saccharides (glucose and water-soluble β-1,4 glucan), and the amounts of cellulose and distilled water at each stirring rate were estimated using the ANN models of the JMP package. The experimental results obtained were also analyzed using RSM of the JMP package. Artificial Neural Network. ANN is a mathematical model imitating biological neural networks in the human brain, consisting of an interconnected group of artificial neurons that processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase: ANN can be used to model complex relationships between inputs and outputs or to find patterns in data. The ANN architecture used in this study is illustrated in Figure 1. Generally, ANN is composed of three fundamental layers, input, hidden, and output layers, as shown in Figure 1. The input layer consists of the amounts of water and cellulose and stirring rate. Information in the input layer is converted into the output vector in the output layer through the hiddenlayer section. The outputs in this study are the rates of glucose and water-soluble β-1,4 glucan formation. Each node has an activation function and can receive signals from nodes in the previous layer. Modeling real-world problems using ANN can be achieved through the algorithm called “learning”, which adjusts the binding force (weight) among the neurons’ linking nodes. Learning achieves an advisable output through a discretional algorithm to update the “weights” between the neurons. There are two major learning paradigms, supervised learning and unsupervised learning. The former and latter proceed on the basis of supervised and unsupervised data, respectively. In the present study, the following procedure was executed: (1) database collection, (2) analysis and preprocessing of the data, (3) training of the neural network (estimation of the
Heterogeneous Catalytic Hydrolysis of Cellulose
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network model in this study), (4) testing the network model, and (5) using the ANN for simulation and prediction.25 A one-hidden-layer first-stage-logistic and second-stageidentity model in the unsupervised learning type feed-forward neural network was adopted for ANN modeling in this study.26 A logistic sigmoid transfer-function, expressed by eq 1, has been used as an activating function and inputs, and outputs are normalized within the range of (0, 1).26
S(x) )
1 (1 + e-x)
(1)
Nx
∑ (aijXi))
(2)
i)1
Nx: number of variables x. SH(x): logistic function. xi: normalized input variables. The output variable (Yˆk) is calculated by the following equation.26 NH
Yˆk ) SY(dk +
∑ (bjkHj))
(3)
j)1
NH: number of hidden nodes. SY(x): identity function. Yˆk: normalized output variables. Coefficients a, b, and c are estimated values. In the model estimation step, the weight-decaystabilized Gauss-Newton method26 was used to prevent overfitting, and three neurons were used in the hidden layer (Figure 1). Using the results produced by the network, statistical methods were used to make comparisons. At the model estimating and testing stages, the sum-squared error (SSE), the root-meansquared error (RMSE), and the coefficient of determination (R2) were estimated. These are defined as follows:26 N
SSE )
∑ (Yi - kYi)2
(4)
i)1
RMSE ) R2 ) 1 -
SSE DFe
(5)
SSE
N
∑ (Yi - Yj )
∑
(6)
2
i)1
Yi: experimental value. k Yi: estimated value. N: number of j : average of experiment. DFe: degrees of freedom of error. Y experimental values. These values represent the fitness of the ANN model for the actual phenomenon. The minimum values of SSE and RMSE in the model were selected during model estimation using the JMP package. In regression, R2 (0 e R2 e 1) shows compatibility between the regression line and actual data.26 R2 ) 1.0 indicates that the regression line perfectly fits the data. Response Surface Methodology. RSM was used for factorial analysis and for experimental design.16-19 RSM not only explores the relationships between several explanatory variables and one or more response variables but is also used for optimization in analytical chemistry.27 A three-level-three-factor Box-Behnken design was employed in this study, requiring 15 experiments using the JMP package.18 Factorial analysis was also carried out using this software. The contribution of each variable to the hydrolysis of cellulose (represented as the regression coefficients) was estimated by factorial analysis given by the following quadratic equation.18
3
3
βixi +
i)1
∑
βiixi2 +
i)1
3
∑ ∑ βijxixj
(7)
i ) 1 j)1 i
