Enhanced delignification of pearl millet straw for biorefinery applications: Statistical Optimization of hydrogen peroxide acetic acid pretreatment and ANN-based predictive modelling

IF 4.1 4区 工程技术 Q3 ENERGY & FUELS
Aayush Mathur, Piyush Pachauri, Shireesh Shrivastava, Vinod K. Aswal, Muralidhar Nayak Bhukya, Jitendra Kumar Saini
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Abstract

Efficient bioconversion of lignocellulosic biomass, such as pearl millet straw (PMS), requires effective delignification using suitable pretreatment. In this study, lignin removal from PMS was improved using hydrogen peroxide-acetic acid pretreatment with low-dose H2SO4 as acid catalyst, which facilitates release of peracetic acid enhancing delignification with minimal carbohydrate loss. Pretreatment was optimized using central composite design of response surface methodology (RSM), resulting in maximum 77% delignification (1.76-fold enhancement) at optimized process parameters: solid loading 15 g, H2SO4 concentration 300 mM and temperature 90 °C. Although RSM effectively establishes relationship between variables, its assumption of polynomial model may not perfectly capture complex nonlinear processes. Hence, to improve accuracy in predicting process parameters, artificial neural network (ANN) modeling of delignification process was performed, and results compared with RSM. Higher coefficient of determination (R2 = 0.97) of trained ANN model indicated high accuracy over RSM (R2 = 0.83). Furthermore, hold-out cross-validation and low testing Root Mean Squared Error (RMSE = 8.55) confirmed robust predictive accuracy of ANN model on unseen data. Pretreatment-induced changes in structural, morphological, thermal and crystalline properties of PMS were comprehensively evaluated using biophysical techniques. Enzymatic hydrolysis of pretreated PMS resulted in maximum saccharification of ~ 67%, with a reducing sugar yield of ~ 75 mg/mL after 48 h. Ultimately, these findings demonstrate that integrating advanced pretreatment with machine learning-based optimization successfully enhances efficiency and predictability of PMS conversion. The resulting improvements in saccharification yields underline the practical viability of this pretreatment for producing fermentable sugars for downstream production of biofuels (e.g. bioethanol) and platform biochemicals for advancing circular bioeconomy.

Abstract Image

用于生物炼制的珍珠粟秸秆脱木质素增强:过氧化氢乙酸预处理的统计优化和基于神经网络的预测模型
木质纤维素生物质的高效生物转化,如珍珠粟秸秆(PMS),需要使用合适的预处理进行有效的脱木质素。在本研究中,采用过氧化氢-乙酸预处理,以低剂量H2SO4作为酸催化剂,改善了PMS中木质素的脱除,促进了过氧乙酸的释放,以最小的碳水化合物损失促进了脱木质素。采用响应面法(RSM)的中心复合设计对预处理进行了优化,在固体负荷15 g、H2SO4浓度300 mM、温度90°C的优化工艺参数下,最大脱木质素率为77%(提高1.76倍)。虽然RSM有效地建立了变量之间的关系,但其多项式模型的假设可能不能很好地捕捉复杂的非线性过程。因此,为了提高工艺参数预测的准确性,对脱木质素过程进行了人工神经网络(ANN)建模,并将结果与RSM进行了比较。神经网络模型的决定系数越高(R2 = 0.97),表明其准确率高于RSM (R2 = 0.83)。此外,交叉验证和低检验均方根误差(RMSE = 8.55)证实了人工神经网络模型对未知数据的预测准确性。利用生物物理技术综合评价预处理引起的PMS结构、形态、热和晶体性质的变化。酶解预处理PMS的最大糖化率为~ 67%,48 h后还原糖产率为~ 75 mg/mL。最终,这些研究结果表明,将先进的预处理与基于机器学习的优化相结合,成功地提高了PMS转化的效率和可预测性。由此产生的糖化产率的提高强调了这种预处理在生产下游生物燃料(例如生物乙醇)的可发酵糖和平台生物化学品以推进循环生物经济方面的实际可行性。
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来源期刊
Biomass Conversion and Biorefinery
Biomass Conversion and Biorefinery Energy-Renewable Energy, Sustainability and the Environment
CiteScore
7.00
自引率
15.00%
发文量
1358
期刊介绍: Biomass Conversion and Biorefinery presents articles and information on research, development and applications in thermo-chemical conversion; physico-chemical conversion and bio-chemical conversion, including all necessary steps for the provision and preparation of the biomass as well as all possible downstream processing steps for the environmentally sound and economically viable provision of energy and chemical products.
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