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Journal articles on the topic 'Pretreatment optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Chen, Xiao Hong, Guang Wei Sun, Jing Hui Zhou, and Ying Han. "Pretreatment Optimization of Poplar Green Liquor Pretreatment-Kraft Pulping." Advanced Materials Research 781-784 (September 2013): 2654–57. http://dx.doi.org/10.4028/www.scientific.net/amr.781-784.2654.

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Green liquor has lower hydroxide concentration and higher sulfidity, which makes it a better choice for modified alkali cooking. This paper studied process parameters of pretreatment stage on pulping properties of poplar green liquor pretreatment-Kraft pulping process. Conditional experiments with green liquor charge of 0.50L/kg, 0.75L/kg, 1.00L/kg and 1.25L/kg, maximum pretreatment temperature of 95°C, 105°C, 115°C and 135°C, and holding time of 30min, 60min and 90min, were carried out. The results shows that optimal pretreatment conditions are as follows: green liquor charge, 1.00L/kg; maximum pretreatment temperature, 115°C; holding time, 60min; and wood to liquor ratio, 1:4.

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2

Zaafouri, Kaouther, Manel Ziadi, Aida Ben Hassen-Trabelsi, Sabrine Mekni, Balkiss Aïssi, Marwen Alaya, Latifa Bergaoui, and Moktar Hamdi. "Optimization of Hydrothermal and Diluted Acid Pretreatments of Tunisian Luffa cylindrica (L.) Fibers for 2G Bioethanol Production through the Cubic Central Composite Experimental Design CCD: Response Surface Methodology." BioMed Research International 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/9524521.

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This paper opens up a new issue dealing with Luffa cylindrica (LC) lignocellulosic biomass recovery in order to produce 2G bioethanol. LC fibers are composed of three principal fractions, namely, α-cellulose (45.80% ± 1.3), hemicelluloses (20.76% ± 0.3), and lignins (13.15% ± 0.6). The optimization of LC fibers hydrothermal and diluted acid pretreatments duration and temperature were achieved through the cubic central composite experimental design CCD. The pretreatments optimization was monitored via the determination of reducing sugars. Then, the 2G bioethanol process feasibility was tested by means of three successive steps, namely, LC fibers hydrothermal pretreatment performed at 96°C during 54 minutes, enzymatic saccharification carried out by means of a commercial enzyme AP2, and the alcoholic fermentation fulfilled with Saccharomyces cerevisiae. LC fibers hydrothermal pretreatment liberated 33.55 g/kg of reducing sugars. Enzymatic hydrolysis allowed achieving 59.4 g/kg of reducing sugars. The conversion yield of reducing sugar to ethanol was 88.66%. After the distillation step, concentration of ethanol was 1.58% with a volumetric yield about 70%.

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3

Pedraza-Segura, Lorena, Hector Toribio-Cuaya, and Antonio Flores-Tlacuahuac. "Multiobjective Optimization Approach for Cellulosic Biomass Pretreatment." Industrial & Engineering Chemistry Research 52, no. 15 (April 5, 2013): 5357–64. http://dx.doi.org/10.1021/ie3032058.

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4

Jaisamut, K., L. Paulová, P. Patáková, M. Rychtera, and K. Melzoch. "Optimization of alkali pretreatment of wheat straw to be used as substrate for biofuels production ." Plant, Soil and Environment 59, No. 12 (December 1, 2013): 537–42. http://dx.doi.org/10.17221/7129-pse.

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Alkali pretreatment of wheat straw was optimized by response surface methodology to maximize yields of fermentable sugars in subsequent enzymatic hydrolysis and to remove maximum lignin in order to improve rheological attributes of the media. The effects of pretreatment conditions on biomass properties were studied using the Expert Designer software. Concentration of sodium hydroxide and temperature were the factors most affecting pretreatment efficiency. At the optimum (80°C, 39 min, 0.18 g NaOH and 0.06 g lime per g of raw biomass), 93.1 ± 1.0% conversion of cellulose to glucose after enzymatic hydrolysis and 80.3 ± 1.2% yield of monosaccharides (glucose plus xylose and arabinose) from cellulose and hemicellulose of wheat straw were achieved.

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5

Motevali, Ali, and Saeid Minaei. "Effects of microwave pretreatment on the energy and exergy utilization in thin-layer drying of sour pomegranate arils." Chemical Industry and Chemical Engineering Quarterly 18, no. 1 (2012): 63–72. http://dx.doi.org/10.2298/ciceq110702047m.

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Energy and exergy analyses may be considered as important tools for design, analysis and optimization of thermal systems. This paper reports on energy and exergy analyses of thin-layer drying of sour pomegranate arils with microwave pretreatment. There were two microwave pretreatments (100W for 20 min and 200 W for 10 min) along with a control treatment (convection drying with no microwave pretreatment). Experiments were carried out at three air temperatures (50, 60 and 70?C) and three air velocities (0.5, 1 and 1.5 m/s). Results showed that energy utilization and energy utilization ratio increased with time, while exergy efficiency decreased. Energy utilization and drying time decreased considerably with microwave pretreatment of pomegranate arils. The minimum values of exergy loss and exergy efficiency were associated with the 200W microwave pretreatment, while they were maximum for control treatment.

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6

Kocher, Gurvinder Singh, and Krishan Lal Kalra Krishan Lal Kalra. "Optimization of Pretreatment, Enzymatic Saccharification and Fermentation Conditions for Bioethanol Production from Rice Straw." Indian Journal of Applied Research 3, no. 5 (October 1, 2011): 62–64. http://dx.doi.org/10.15373/2249555x/may2013/165.

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7

Schmidt, Anette Skammelsen, and Anne Belinda Thomsen. "Optimization of wet oxidation pretreatment of wheat straw." Bioresource Technology 64, no. 2 (May 1998): 139–51. http://dx.doi.org/10.1016/s0960-8524(97)00164-8.

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8

Kushwaha, Deepika, Ishita Mishra, Neha Srivastava, and Pradeep Kumar Mishra. "Optimization of pretreatment conditions for enhanced sugar release." International Journal of Green Energy 14, no. 13 (August 8, 2017): 1110–18. http://dx.doi.org/10.1080/15435075.2017.1359784.

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9

Bazargan, Alireza, Majid Bazargan, and Gordon McKay. "Optimization of rice husk pretreatment for energy production." Renewable Energy 77 (May 2015): 512–20. http://dx.doi.org/10.1016/j.renene.2014.11.072.

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10

Dahunsi, S. O. "Liquefaction of pineapple peel: Pretreatment and process optimization." Energy 185 (October 2019): 1017–31. http://dx.doi.org/10.1016/j.energy.2019.07.123.

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11

Brahim, M., N. Boussetta, N. Grimi, E. Vorobiev, I. Zieger-Devin, and N. Brosse. "Pretreatment optimization from rapeseed straw and lignin characterization." Industrial Crops and Products 95 (January 2017): 643–50. http://dx.doi.org/10.1016/j.indcrop.2016.11.033.

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12

Salapa, Ioanna, Evangelos Topakas, and Dimitrios Sidiras. "Simulation and optimization of barley straw organosolv pretreatment." Industrial Crops and Products 113 (March 2018): 80–88. http://dx.doi.org/10.1016/j.indcrop.2018.01.018.

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13

He, Zhong-bing, Yun-guo Liu, and Yu Xiao. "Optimization of Fenton pretreatment for 2-chlorophenol solution." Journal of Central South University 20, no. 10 (October 2013): 2791–95. http://dx.doi.org/10.1007/s11771-013-1798-y.

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14

Barisik, Gizem, Asli Isci, Naciye Kutlu, Simel Bagder Elmaci, and Bulent Akay. "Optimization of organic acid pretreatment of wheat straw." Biotechnology Progress 32, no. 6 (September 4, 2016): 1487–93. http://dx.doi.org/10.1002/btpr.2347.

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15

Muharja, Maktum, Rizki Fitria Darmayanti, Bekti Palupi, Istiqomah Rahmawati, Boy Arief Fachri, Felix Arie Setiawan, Helda Wika Amini, et al. "Optimization of Microwave-Assisted Alkali Pretreatment for Enhancement of Delignification Process of Cocoa Pod Husk." Bulletin of Chemical Reaction Engineering & Catalysis 16, no. 1 (January 16, 2021): 31–43. http://dx.doi.org/10.9767/bcrec.16.1.8872.31-43.

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In this study, the optimization of microwave-assisted alkaline (MAA) pretreatment is performed to attain the optimal operating parameters for the delignification of cocoa pod husk (CPH). The MAA performance was examined by heating the CPH solid with different particle sizes (60–120 mesh) and NaOH solution with a different sample to a solvent (SS) ratio (0.02–0.05 g/L), for short irradiation time (1–4 min). Box-Behnken Design (BBD) was utilized to optimize the percentage of lignocellulose composition changes. The results show that by enlarging particle size, the content of lignin and cellulose decreased while hemicellulose increased. By prolong irradiation time, the content of lignin and hemicellulose decreased while cellulose elevated. On the other hand, increasing the SS ratio was not significant for hemicellulose content changes. From FTIR and SEM characterization, the MAA drove the removal of lignin and hemicellulose of CPH and increased cellulose slightly. Supported by kinetic study which conducted in this work, it was exhibited that MAA pretreatment technology is an effective delignification method of CPH which can tackle the bottleneck of its commercial biofuel production. Copyright © 2021 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

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16

Xu, Wanxiu, Guanyu Zhu, Chunfang Song, Shaogang Hu, and Zhenfeng Li. "Optimization of Microwave Vacuum Drying and Pretreatment Methods for Polygonum cuspidatum." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/4967356.

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This study was conducted to optimize the drying process of Polygonum cuspidatum slices using an orthogonal experimental design. The combined effects of pretreatment methods, vacuum pressure and temperature of inner material, drying kinetics, color value, and retention of the indicator compounds were investigated. Seven mathematical models on thin-layer drying were used to study and analyze the drying kinetics. Pretreatment method with blanching for 30 s at 100°C increased the intensity of the red color of P. cuspidatum slices compared with other pretreatment methods and fresh P. cuspidatum slices. P. cuspidatum slices dried at 60°C retained more indicator compounds. Furthermore, microwave pretreatment methods, followed by microwave vacuum for 200 mbar at 50°C, resulted in high concentration of indicator compounds, with short drying time and less energy. This optimized condition for microwave vacuum drying and pretreatment methods would be useful for processing P. cuspidatum. The Newton, Page, and Wang and Singh models slightly fitted the microwave vacuum drying system. The logarithmic, Henderson and Pabis, two-term, and Midilli et al. models can be used to scale up the microwave vacuum drying system to a commercial scale. The two-term and Midilli et al. models were the best fitting mathematical models for the no-pretreatment case at 600 mbar and 60°C.

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17

Wu, Jianguo, Yuanyuan Wang, Zhao Zhang, Xuanxian Luo, Chi Wang, Zhiqing Zhong, Dong Qian, and Tong Zhang. "Optimization of pretreating poplar wood shavings with cellulase to produce binderless fiberboard using response surface methodology." BioResources 15, no. 3 (July 6, 2020): 6431–41. http://dx.doi.org/10.15376/biores.15.3.6431-6441.

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Wood processing waste, poplar wood shavings, were used for fiberboard production, and the pretreatment conditions using cellulase were studied using response surface methodology (RSM). After single factors optimization, central level of temperature, dosage, and liquid/solid ratio (LSR) of cellulase pretreatment conditions were obtained. Further optimization to study the influence of the factors was carried out using Box-Behnken design of experiments. A second-order polynomial equation was obtained, and the low p-value (<0.007) implied that the model was highly significant by analysis of variance (ANOVA). The optimized cellulase pretreatment conditions for maximum bending strength (BS) of the fiberboard were determined by ridge analysis as 44.4 °C of temperature, 1.23 U/g of dosage, 4.2 of LSR, and 5 h of pretreatment time. Under the optimized conditions, the BS of the fiberboard reached 25.12 ± 0.35 MPa by validation experiment, which was twice that of the fiberboard without pretreatment. Thus, the cellulase pretreatment should be a good choice to produce high-strength binderless fiberboard.

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18

Prisciandaro, M., A. Del Borghi, and F. Vegliò. "Factorial Experiments in the Optimization of Alkaline Wastewater Pretreatment." Industrial & Engineering Chemistry Research 41, no. 20 (October 2002): 5034–41. http://dx.doi.org/10.1021/ie0202827.

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19

Herrero, Andrew M., and Kris A. Bertness. "Optimization of surface pretreatment for single GaN nanowire devices." Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena 30, no. 6 (November 2012): 062201. http://dx.doi.org/10.1116/1.4754701.

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20

Nandy, Tapas, R. D. Vyas, Sunita Shastry, and S. N. Kaul. "Optimization of Coagulants for Pretreatment of Printing Ink Wastewater." Environmental Engineering Science 19, no. 1 (January 2002): 1–7. http://dx.doi.org/10.1089/109287502753590197.

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21

Ramos, L. B., R. J. Sánchez, A. K. De Figueiredo, S. M. Nolasco, and M. B. Fernández. "Optimization of Microwave Pretreatment Variables for Canola Oil Extraction." Journal of Food Process Engineering 40, no. 3 (June 30, 2016): e12431. http://dx.doi.org/10.1111/jfpe.12431.

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22

Song, C. F., S. G. Wang, J. Yang, Z. W. Cui, and Y. H. Gu. "Optimization of Vacuum-Microwave Radiation Pretreatment on Extraction ofGanodermaPolysaccharides." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/792832.

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A new process of vacuum-microwave (VM) radiation pretreatment for extracting polysaccharides from theGanoderma lucidumwas proposed, and the parameters were optimized by response surface methodology (RSM). The orthogonal-central composite design scheme was used and the responsive surfaces methodology of three factors and five levels was adopted, and the factors influencing the technological parameters and its interaction terms were analyzed and regressed. The optimal parameters were obtained as follows: the infiltration time of 70 min, microwave power density of 11.2 W/g, and VM irradiation time of 180 s. In consequence, the extraction yield was up to 1.775% when VM radiation was conducted in advance. Compared to the traditional hot-water extraction method, VM pretreatment can shorten the extraction time by more than a half, and the polysaccharide extraction yield was increased by 48.1%. It holds significant potential for further investigation, development, and application.

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23

Davaritouchaee, Maryam, and Shulin Chen. "Persulfate oxidizing system for biomass pretreatment and process optimization." Biomass and Bioenergy 116 (September 2018): 249–58. http://dx.doi.org/10.1016/j.biombioe.2018.06.021.

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24

Chen, Huazhou, Qiqing Song, Guoqiang Tang, Quanxi Feng, and Liang Lin. "The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models." ISRN Spectroscopy 2013 (January 17, 2013): 1–9. http://dx.doi.org/10.1155/2013/642190.

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The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.

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25

Flandrois, C., C. Lahet, D. Feldmann, J. M. Gabastou, A. Gonnon, and I. Maire. "Urinary alanine aminopeptidase assay improved as result of multivariate response-surface analysis." Clinical Chemistry 34, no. 5 (May 1, 1988): 954–57. http://dx.doi.org/10.1093/clinchem/34.5.954.

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Abstract Optimization of determination of alanine aminopeptidase in urine by univariate study led to a method involving pretreatment of urine with Sephadex G50. Re-examination of the optimization by multivariate study led us to recommend higher optimal concentrations: 5.8 mmol/L for the substrate and 300 mmol/L for the Tris buffer. Under these new conditions, pretreatment of urine was no longer necessary and the assay could be completely automated.

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26

Li, Xinchao, Xin Jin, Shan Lu, Zhe Li, Yue Wang, and Jiangtao Cao. "Carbon-Efficient Production Scheduling of a Bioethanol Plant Considering Diversified Feedstock Pelletization Density: A Case Study." Processes 8, no. 9 (September 18, 2020): 1189. http://dx.doi.org/10.3390/pr8091189.

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This paper presents a dual-objective optimization model for production scheduling of bioethanol plant with carbon-efficient strategies. The model is developed throughout the bioethanol production process. Firstly, the production planning and scheduling of the bioethanol plant’s transportation, storage, pretreatment, and ethanol manufacturing are fully considered. Secondly, the carbon emissions in the ethanol manufacturing process are integrated into the model to form a dual-objective optimization model that simultaneously optimizes the production plan and carbon emissions. The effects of different biomass raw materials with optional pelletization density and pretreatment methods on production scheduling are analyzed. The influence of demand and pretreatment cost on selecting a pretreatment method and total profit is considered. A membership weighted method is developed to solve the dual-objective model. The carbon emission model and economic model are integrated into one model for analysis. An example is given to verify the effectiveness of the optimization model. At the end of the paper, the limitation of this study is discussed to provide directions for future research.

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27

Gundupalli, Marttin Paulraj, Nishanth Senthilkumar, and Debraj Bhattacharyya. "Process Optimization for Recovery of Reducing Sugar from Coconut Pith Using Sequential Hydrothermal Pretreatment and Enzymatic Saccharification." International Journal of Chemical Engineering and Applications 9, no. 6 (December 2018): 94–199. http://dx.doi.org/10.18178/ijcea.2018.9.6.726.

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28

Feng, RuiZhe, Asad A. Zaidi, Kun Zhang, and Yue Shi. "Optimisation of Microwave Pretreatment for Biogas Enhancement through Anaerobic Digestion of Microalgal Biomass." Periodica Polytechnica Chemical Engineering 63, no. 1 (June 15, 2018): 65–72. http://dx.doi.org/10.3311/ppch.12334.

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In this study, optimization of microwave (MW) pretreatment conditions for anaerobic digestion of green microalgae (Enteromorpha) is carried out by using response surface methodology (RSM). MW power, pretreatment time and liquid-solid ratio were selected as independent variables for optimization. The optimum conditions were achieved at MW power, pretreatment time and liquid-solid ratio of 656.92 W, 5.10 min and 33.63:1, respectively. From these optimum conditions, it was found that MW pretreatment power of about 600 W had better effect. An anaerobic digestion was carried out batch-wise with working volume, operating temperature and mixing rate as 250 ml, 37 °C and 150 rpm, respectively. Optimum conditions provide highest amount of COD and reducing sugar increase of 10,420 mg/L and 0.77-0.79 g/L respectively. The increase in COD and reducing sugar showed that the pretreatment has improved anaerobic digestion of microalgae. The peak biogas production amount of MW pretreated 20:1, 6 min group reached 244 mL whereas the control group only reached 188 mL in total.

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29

Gohi, Bi, Jinze Du, Hong-Yan Zeng, Xiao-ju Cao, and Kai Zou. "Microwave Pretreatment and Enzymolysis Optimization of the Lotus Seed Protein." Bioengineering 6, no. 2 (March 27, 2019): 28. http://dx.doi.org/10.3390/bioengineering6020028.

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Pretreatment with a microwave was conducted before enzymolysis and shown to enhance the enzymolysis, which changed the secondary structure of the lotus seed protein. Under high-power microwave irradiation, sub bonds of the protein were broken, causing disaggregation and unfolding of the secondary structure, namely a decrease in the intermolecular aggregate structure and increase in the random coil structure, making the protein bonds susceptible to papain in the enzymolysis. On the other hand, a response surface methodology (RSM) was launched to investigate the influence of the enzymolysis process variables on the DH (degree of hydrolysis). The statistical analysis revealed that the optimized conditions were a protein substrate concentration of 15 g/L, pH of 5.5, enzymolysis temperature of 57 °C, papain amount of 0.5 g/L, and enzymolysis time of 45 min, for which the predicted value of the DH was 35.64%. The results indicated that a microwave also had better potential for applications in the enzymolysis of foods.

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30

Pongsuwan, Phubet, and Sininart Chongkhong. "Optimization of Microwave Pretreatment without a Reagent of Pineapple Peel." Advanced Materials Research 941-944 (June 2014): 1056–59. http://dx.doi.org/10.4028/www.scientific.net/amr.941-944.1056.

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The microwave pretreatment of pineapple peel for ethanol production was optimized by RSM (Response surface methodology). Three variables, which were studied, were peel to water ratio (100-160 g/L), microwave power (90-900 watt) and time (5-20 min). The optimal condition for reducing sugar production was 132 g/L peel to water ratio, 90 watt for 12 min that provide the 10.4 g/L of highest content. For total sugar production was 160 g/L ratio, 90 watt for 20 min that gave the optimum content of 579.9 g/L. This microwave pretreatment without a reagent could achieve the satisfied sugar content for ethanol fermentation that led to save time and energy consumption.

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31

Alansari, Amir, Meric Selbes, Tanju Karanfil, and James Amburgey. "Optimization of Coagulation Pretreatment Conditions in a Ceramic Membrane System." Journal - American Water Works Association 107, no. 12 (December 2015): E693—E701. http://dx.doi.org/10.5942/jawwa.2015.107.0151.

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32

Romaní, Aloia, Héctor A. Ruiz, Francisco B. Pereira, Lucília Domingues, and José A. Teixeira. "Fractionation ofEucalyptus globulusWood by Glycerol–Water Pretreatment: Optimization and Modeling." Industrial & Engineering Chemistry Research 52, no. 40 (September 25, 2013): 14342–52. http://dx.doi.org/10.1021/ie402177f.

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33

Xie, Danni, Dan Huo, Chuanling Si, and Qiulin Yang. "Optimization of eucalyptus pretreatment by NH4Cl using response surface methodology." Nordic Pulp & Paper Research Journal 32, no. 3 (August 1, 2017): 459–65. http://dx.doi.org/10.3183/npprj-2017-32-03-p459-465.

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34

Chen, S., X. Li, W. C. Xia, and C. Liang. "Optimization of grinding pretreatment for effective flotation of oxidized coal." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 39, no. 8 (March 16, 2017): 794–99. http://dx.doi.org/10.1080/15567036.2016.1263258.

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35

Kootstra, A. Maarten J., Hendrik H. Beeftink, Elinor L. Scott, and Johan PM Sanders. "Optimization of the dilute maleic acid pretreatment of wheat straw." Biotechnology for Biofuels 2, no. 1 (2009): 31. http://dx.doi.org/10.1186/1754-6834-2-31.

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36

Choi, Won-Il, Ji-Yeon Park, Joon-Pyo Lee, You-Kwan Oh, Yong Park, Jun Kim, Jang Park, Chul Kim, and Jin-Suk Lee. "Optimization of NaOH-catalyzed steam pretreatment of empty fruit bunch." Biotechnology for Biofuels 6, no. 1 (2013): 170. http://dx.doi.org/10.1186/1754-6834-6-170.

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37

Martín, Carlos, Jürgen Puls, Andreas Schreiber, and Bodo Saake. "Optimization of sulfuric acid-assisted glycerol pretreatment of sugarcane bagasse." Holzforschung 67, no. 5 (July 1, 2013): 523–30. http://dx.doi.org/10.1515/hf-2012-0179.

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Abstract A combined pretreatment of sugarcane bagasse with glycerol and sulfuric acid was investigated based on a central composite rotatable experimental design. The following factors were varied: temperature (150–199°C), time (0.69–2.3 h), H2SO4 concentration (0.0–1.1%), and glycerol concentration (55.4–79.6%). Xylans and lignin were considerably solubilized during pretreatment. Xylan solubilization, ranging between 6% and 94%, increased significantly with the increase of temperature, time, and H2SO4 concentration and dropped with the increase of glycerol amount. Glycerol restricted the solubilization and full hydrolysis of xylans and the degradation of xylose. Lignin solubilization (20.6–49.4%) increased with the increase of all the experimental factors. Cellulose recovery, which was generally high, increased with the increasing of glycerol concentration and declined at high levels of the other factors. Recoveries above 97% were achieved at low H2SO4 concentration and high glycerol load, whereas the lowest value (83.4%) was achieved in the longest-lasting experiment. The models based on the experimental results predicted the maximal lignin solubilization at 187.7°C, 2.3 h, 79.6% glycerol, and 0.64% H2SO4, whereas the highest yield of enzymatic hydrolysis can be expected at 194.1°C, 1.67 h, 79.6% glycerol, and 1.1% H2SO4. The optimal conditions were confirmed in control experiments. The synergistic effect of sulfuric acid and glycerol on the enzymatic hydrolysis of cellulose was demonstrated.

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38

Ling Hii, Kiew, Swee Pin Yeap, and Mat Don Mashitah. "Utilization of palm pressed pericarp fiber: Pretreatment, optimization and characterization." Environmental Progress & Sustainable Energy 33, no. 1 (February 26, 2013): 238–49. http://dx.doi.org/10.1002/ep.11757.

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39

Li, Yanjun, Ute Merrettig-Bruns, Sabine Strauch, Stephan Kabasci, and Hongzhang Chen. "Optimization of ammonia pretreatment of wheat straw for biogas production." Journal of Chemical Technology & Biotechnology 90, no. 1 (January 31, 2014): 130–38. http://dx.doi.org/10.1002/jctb.4297.

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40

Lee, Hyunsu, and Young Mo Kim. "Optimization of Organosolv Pretreatment of Waste Wood for Lignin Extraction." Journal of Korean Society of Environmental Engineers 39, no. 10 (October 31, 2017): 568–74. http://dx.doi.org/10.4491/ksee.2017.39.10.568.

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41

Chen, Wu. "Optimization of Sludge Dewatering Through Pretreatment, Equipment Selection, and Testing." Drying Technology 31, no. 2 (January 25, 2013): 193–201. http://dx.doi.org/10.1080/07373937.2012.723658.

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42

Hosseini, Seyed Ali, and Nilay Shah. "Multiscale modelling of hydrothermal biomass pretreatment for chip size optimization." Bioresource Technology 100, no. 9 (May 2009): 2621–28. http://dx.doi.org/10.1016/j.biortech.2008.11.030.

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Li, Hongqiang, and Jian Xu. "Optimization of microwave-assisted calcium chloride pretreatment of corn stover." Bioresource Technology 127 (January 2013): 112–18. http://dx.doi.org/10.1016/j.biortech.2012.09.114.

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Ma, Lijuan, Youzhi Cui, Rui Cai, Xueqiang Liu, Cuiying Zhang, and Dongguang Xiao. "Optimization and evaluation of alkaline potassium permanganate pretreatment of corncob." Bioresource Technology 180 (March 2015): 1–6. http://dx.doi.org/10.1016/j.biortech.2014.12.078.

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Al-Rqobah, H., M. Abdel-Jawad, Z. Qamhiyah, and G. Aly. "Optimization of chemical pretreatment for reverse osmosis (RO) seawater desalination." Desalination 66 (December 1987): 423–30. http://dx.doi.org/10.1016/0011-9164(87)90222-0.

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Theodoridou, E., A. D. Jannakoudakis, J. O. Besenhard, and R. F. Sauter. "Carbon fibre-supported noble metal catalysts: Optimization of fibre pretreatment." Synthetic Metals 14, no. 1-2 (March 1986): 125–35. http://dx.doi.org/10.1016/0379-6779(86)90134-7.

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Wan Omar, Wan Nor Nadyaini, and Nor Aishah Saidina Amin. "Multi response optimization of oil palm frond pretreatment by ozonolysis." Industrial Crops and Products 85 (July 2016): 389–402. http://dx.doi.org/10.1016/j.indcrop.2016.01.027.

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Xu, Jian, Hongzhang Chen, Zsófia Kádár, Anne Belinda Thomsen, Jens Ejbye Schmidt, and Huadong Peng. "Optimization of microwave pretreatment on wheat straw for ethanol production." Biomass and Bioenergy 35, no. 9 (October 2011): 3859–64. http://dx.doi.org/10.1016/j.biombioe.2011.04.054.

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Tang, Yuanjun, Jun Dong, Yong Chi, Zhaozhi Zhou, and Mingjiang Ni. "Energy and exergy optimization of food waste pretreatment and incineration." Environmental Science and Pollution Research 24, no. 22 (June 22, 2017): 18434–43. http://dx.doi.org/10.1007/s11356-017-9396-4.

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Ahn, Hong-Joo, Myung-Ho Lee, Se-Chul Sohn, Kwang Yong Jee, and Kyuseok Song. "Optimization study on sample pretreatment of spent fuel storage rack." Journal of Radioanalytical and Nuclear Chemistry 285, no. 2 (April 11, 2010): 199–205. http://dx.doi.org/10.1007/s10967-010-0548-y.

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