Climate Change Data Portal
| DOI | 10.1007/s13399-024-05371-1 |
| Paddy straw as a biomass feedstock for the manufacturing of bioethanol using acid hydrolysis and parametric optimization through response surface methodology and an artificial neural network | |
| 发表日期 | 2024 |
| ISSN | 2190-6815 |
| EISSN | 2190-6823 |
| 英文摘要 | Paddy straw (PS)-based lignocellulosic biomaterials (LBM) generate biofuel, which minimizes climate change, global warming, CO2, and nursery gas emissions. The current work is primarily focused on generation of bioethanol from PS to reduce energy costs and supplement non-renewable energy sources with alternative energy sources from specific agricultural biowaste sources. Due to its simplicity and effectiveness, the bioethanol PS was produced by employing the process of dilute acid hydrolysis fermentation (PDAHF). In this research, at constant temperature and hydrolysis time, the impacts of acid concentration and fermentation time on bioethanol yield were examined. Experiments revealed that the greatest production of ethanol under the best operating circumstances was 38.64%. Furthermore, to determine the impact of fermentation time, temperature, and pH on the bioethanol yield from PS biomass, the process variables were optimized using RSM, and its interaction effect on maximizing ethanol yield was investigated. From ANOVA analysis, a highly significant adjusted and R-squared correlation factor of 0.9963 and 0.9790, respectively, with low p-value of less than 0.0001 and F-value of 299.1 regression quadratic model equation was obtained. The maximum predicted and experimental bioethanol yields are 38.497 and 38.517%, respectively. Additionally, two types of artificial neural networks (ANN) were developed in this study: a feedforward artificial neural network (FANN) and a nonlinear autoregressive network with exogenous inputs (NARX). The Levenberg-Marquardt (LM) training algorithm for the FANN model provided best prediction of bioethanol yield with regression (R) of 0.966 and 0.515, 0.441, 0.394, and 0.011 for root mean squared error (RMSE), mean absolute error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), respectively, while gradient descent backpropagation (GD) for the NARX model generated R of 0.869 and RMSE, MSE, MAE, and MAPE of 0.515, 0.441, 0.394, and 0.011, respectively. In comparison to NARX, FANN has higher R-value and lower RMSE, MSE, MAE, and MAPE, making it a better choice for predicting the output of bioethanol. |
| 英文关键词 | Acid hydrolysis; Biomass; Bioethanol; Saccharomyces cerevisiae; RSM; ANN |
| 语种 | 英语 |
| WOS研究方向 | Energy & Fuels ; Engineering |
| WOS类目 | Energy & Fuels ; Engineering, Chemical |
| WOS记录号 | WOS:001159713400001 |
| 来源期刊 | BIOMASS CONVERSION AND BIOREFINERY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/305111 |
| 作者单位 | Haramaya University; Karpagam Academy of Higher Education (KAHE); University of Nottingham Malaysia |
| 推荐引用方式 GB/T 7714 | . Paddy straw as a biomass feedstock for the manufacturing of bioethanol using acid hydrolysis and parametric optimization through response surface methodology and an artificial neural network[J],2024. |
| APA | (2024).Paddy straw as a biomass feedstock for the manufacturing of bioethanol using acid hydrolysis and parametric optimization through response surface methodology and an artificial neural network.BIOMASS CONVERSION AND BIOREFINERY. |
| MLA | "Paddy straw as a biomass feedstock for the manufacturing of bioethanol using acid hydrolysis and parametric optimization through response surface methodology and an artificial neural network".BIOMASS CONVERSION AND BIOREFINERY (2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
