棉秆在NaOH中水解过程的神经网络模拟与优化
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摘要
生物质糖化是生物质高值化利用的重要途径。以棉花秸秆为原料,研究NaOH浓度、反应温度、时间及固含量等因素对棉秆水解后还原糖含量和木质素含量的影响,并采用神经网络对棉秆在NaOH中的水解过程进行模拟与优化,建立棉秆在NaOH中水解过程的神经网络模型,得到棉秆在NaOH中水解的最佳条件为:10%NaOH、60℃、24h和5%固含量,预处理后棉秆的还原糖含量为74.80%,木质素含量为23.21%。通过试验及神经网络模型的预测和优化,提高了棉秆在NaOH中水解后的还原糖含量,为棉秆水解发酵生产燃料乙醇技术的研究奠定了基础。
The saccharification of biomass is an important approach to the high value utilization of biomass.In this study,cotton stalk was selected as raw material,the effects of NaOH concentration,reaction temperature,time and solid content on the hydrolysis yield of the cotton stalk were investigated.Artificial neural network(ANN)was used to predict and optimize the process of hydrolysis of cotton stalk in NaOH.After training,testing and validation,the ANN model was established.The optimized condition of hydrolysis of cotton stalk in NaOH was obtained as:10% NaOH,60℃,24 hand 10% solid content,and 74.80% of reducing sugar content in the pretreated cotton stalk was achieved.The corresponding lignin content was23.21%.After simulation and optimization of hydrolysis of cotton stalk in NaOH solution by ANN model,the reducing sugar content was enhanced,which provided an important application basis for the effective utilization of cotton stalk.
The saccharification of biomass is an important approach to the high value utilization of biomass.In this study,cotton stalk was selected as raw material,the effects of NaOH concentration,reaction temperature,time and solid content on the hydrolysis yield of the cotton stalk were investigated.Artificial neural network(ANN)was used to predict and optimize the process of hydrolysis of cotton stalk in NaOH.After training,testing and validation,the ANN model was established.The optimized condition of hydrolysis of cotton stalk in NaOH was obtained as:10% NaOH,60℃,24 hand 10% solid content,and 74.80% of reducing sugar content in the pretreated cotton stalk was achieved.The corresponding lignin content was23.21%.After simulation and optimization of hydrolysis of cotton stalk in NaOH solution by ANN model,the reducing sugar content was enhanced,which provided an important application basis for the effective utilization of cotton stalk.
引文
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[16]汪雪,周国模,周健,等.基于贝叶斯改进的人工神经网络毛竹林碳通量估算[J].西北林学院学报,2017,32(1):203-209.WANG X,ZHOU G M,ZHOU J,et al.Estimation of Phyllostachys heterocycla cv.pubescens carbon flux based on artificial neural networks improved by Bayesian[J].Journal of Northwest Forestry University,2017,32(1):203-209.(in Chinese)
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[18] VANI S,SUKUMARAN R K,SAVITHRI S.Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling[J].Bioresource Technology,2015,188:128-135.
[19] WANG M,ZHOU D,WANG Y,et al.Bioethanol production from cotton stalk:a comparative study of various pretreatments[J].Fuel,2016,184:527-532.
[2] KARIMI K,KHERADMANDINIA S,TAHERZADEH M J.Conversion of rice straw to sugars by dilute-acid hydrolysis[J].Biomass and Bioenergy,2005,30(3):247-253.
[3]丁绍兰,张敏娜,黄振侠,等.尿素氨化预处理对稻秆厌氧发酵产气特性的影响[J].生态环境学报,2018,27(1):18-23.DING S L,ZHANG M N,HUANG Z X,et al.Effect of urea ammonization pretreatment on anaerobic fermentation characteristics of rice straw[J].Ecology and Environmental Sciences,2018,27(1):18-23.(in Chinese)
[4] AMNUAYCHEEWA P,HENGAROONPARSAN R,RATTANAPORN K,et al.Enhancing enzymatic hydrolysis and biogas production from rice straw by pretreatment with organic acids[J].Industrial Crops&Products,2016,87:247-254.
[5] SHETTY D J,KSHIRSAGAR P,TAPADIA-MAHESHWARI S,et al.Alkali pretreatment at ambient temperature:apromising method to enhance biomethanation of rice straw[J].Bioresource Technology,2017,226:80-88.
[6] SINGH A,BAJAR S,SISHNOI N R.Physico-chemical pretreatment and enzymatic hydrolysis of cotton stalk for ethanol production by Saccharomyces cerevisiae[J].Bioresource Technology,2017,244:71-77.
[7] CHANG C W,YU W C,CHEN W J,et al.A study on the enzymatic hydrolysis of steam exploded napiergrass with alkaline treatment using artificial neural networks and regression analysis[J].Journal of the Taiwan Institute of Chemical Engineers,2011,42(6):889-894.
[8]朱文远,姜波,寇佳文,等.酸性亚硫酸盐预处理对棉杆糖化效率的影响[J].纤维素科学与技术,2015,23(4):23-29.ZHU W Y,JIANG B,KOU J W,et al.Effects of bisulfite pretreatment on enzymatic saccharification of cotton stalk[J].Journal of Cellulose Science and Technology,2015,23(4):23-29.(in Chinese)
[9] SILVERSTEIN R A,CHEN Y,SHARMA-SHIVAPPA R R,et al.A comparison of chemical pretreatment methods for improving saccharification of cotton stalks[J].Bioresource Technology,2006,98(16):3000-3011.
[10] ANTWI P,LI J Z,BOADI P O,et al.Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network[J].Bioresource Technology,2017,228:106-115.
[11] ASTRAY G,GULLON B,LABIDI J,et al.Comparison between developed models using response surface methodology(RSM)and artificial neural networks(ANNs)with the purpose to optimize oligosaccharide mixtures production from sugar beet pulp[J].Industrial Crops&Products,2016,92:290-299.
[12] DAS S,BHATTACHARYA A,HALDAR S,et al.Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol:Comparison between artificial neural network and response surface methodology[J].Sustainable Materials and Technologies,2015,3:17-28.
[13]李勇,龚晓武,王珂,等.稀盐酸水解棉秆产糖工艺的神经网络模拟与优化[J].石河子大学学报:自然科学版,2017,35(4):407-413.
[14]李志霞,聂继云,闫震,等.响应面法对3,5-二硝基水杨酸比色法测定水果中还原糖含量条件的优化[J].分析测试学报,2016,35(10):1283-1288.LI Z X,NIE J Y,YAN Z,et al.Optimization of 3,5-dinitrosalicylic acid colorimetry determination conditions of reducing sugar in fruits by response surface method[J].Journal of Instrumental Analysis,2016,35(10):1283-1288.(in Chinese)
[15]杨博文,王晓晓,李曦,等.5种当归类药材多糖量的比较[J].中草药,2016,47(21):3896-3900.
[16]汪雪,周国模,周健,等.基于贝叶斯改进的人工神经网络毛竹林碳通量估算[J].西北林学院学报,2017,32(1):203-209.WANG X,ZHOU G M,ZHOU J,et al.Estimation of Phyllostachys heterocycla cv.pubescens carbon flux based on artificial neural networks improved by Bayesian[J].Journal of Northwest Forestry University,2017,32(1):203-209.(in Chinese)
[17]张义林,董华,周恩泽.基于BP神经网络的热管式真空管集热器热性能预测校正模型[J].太阳能学报,2008,29(6):690-693.
[18] VANI S,SUKUMARAN R K,SAVITHRI S.Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling[J].Bioresource Technology,2015,188:128-135.
[19] WANG M,ZHOU D,WANG Y,et al.Bioethanol production from cotton stalk:a comparative study of various pretreatments[J].Fuel,2016,184:527-532.
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