|
|
|
|
|
|
| Predicting Gross Calorific Value of Agricultural Feedstock Based on Proximate/Ultimate Analysis and Visible-Near Infrared Spectroscopy |
| XIONG Xian-qing1, QIAN Shao-ping2, SHENG Kui-chuan2*, HE Yong2, FANG Lu1, WU Zhi-hui1 |
1. College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
|
|
Abstract Utilization of agricultural residues as compacted fuels (in pellet or briquette form) for both domestic furnace and industrial boiler is more and more promising. Gross calorific value (GCV) is an important performance for biomass as a solid fuel, indicating the useful energy content of biomass. Measurement of gross calorific value using oxygen bomb calorimeter is time consuming. Thus, it is necessary to develop a fast and accurate method to evaluate the GCV of raw biomass residues. This is conducive to control the quality of feedstock for biomass pellets production. In this study, different GCV predicting models for multiple agricultural residues were proposed and analyzed, and optimal modeling and statistical methods for the GCV prediction of 5 crop residues viz. rice straw, wheat straw, corn stalk, rape stalk and cotton stalk were developed. Multiple linear regression (MLR), stepwise regression analysis (SWR), back propagation artificial neural networks (BPNN) models were proposed to predict the GCV from proximate and/or ultimate analysis of 5 crop feedstock. The best coefficients of determination of R2, root mean square error of predict (RMSEP), and the ratio of standard error of prediction to standard deviation of the reference data (RPD) of 0.921 1, 0.135 1 and 3.49 were obtained, respectively, when corresponding variables were introduced to MLR models. Additionally, GCV models developed based on visible-near infrared spectroscopy (Vis-NIR) also showed the highest R2 and RMSEP values of 0.881 2 and 0.412 9, respectively, when partial least squares regression (PLR) was used. This study demonstrates that MLR model and PLR model can be used to estimate the GCV of agricultural feedstock from proximate analysis, ultimate analysis and Vis-NIR technology.
|
|
Received: 2016-04-29
Accepted: 2016-08-16
|
|
|
|
Corresponding Authors:
SHENG Kui-chuan
E-mail: kcsheng@zju.edu.cn
|
|
[1] Das O, Sarmah A K. Sci. Tot. Environ., 2015, 537: 323.
[2] Gillespie G D, Everard C D, Fagan C C, et al. Fuel, 2013, 111: 771.
[3] Acar S, Ayanoglu A. Energ. Edu. Sci. Technol. Part A, 2012, 28(2): 749.
[4] Niu W, Huang G, Liu X, et al. Energ. Fuel, 2014, 28(12): 7474.
[5] National Bureau of Statistics of China. China Statistical Yearbook. Beijing：China Statistical Publishing House, 2014.
[6] Gillespie GD, Everard CD, McDonnell KP. Energy, 2015, 80: 582.
[7] Nhuchhen D R, Salam P A. Fuel, 2012, 99: 55.
[8] Demirbas A. Energ. Sour., 2003, 25(7): 629.
[9] Kavsek D, Bednárová A, Biro M, et al. Open Chemistry, 2013, 11(9): 1481.
[10] Huang C, Han L, Yang Z, et al. Waste Manage., 2009, 29(6): 1793.
[11] Qian SP, Yan W, Yang GF, et al. Int. Agr. Eng. J., 2015, 24: 119.
[12] He C, Chen L, Yang Z, et al. Bioresour. Technol., 2012, 110: 314.
[13] Huang C, Han L, Yang Z, et al. Energy Convers. Manage., 2008, 49(12): 3433.
[14] Pandit YP, Badhe YP, Sharma B, et al. Fuel, 2011, 90(1): 339.
[15] Fagan C C, Everard C D, McDonnell K. Bioresour. Technol., 2011, 102(8): 5200. |
| [1] |
SHI Wen-qiang1, XU Xiu-ying1*, ZHANG Wei1, ZHANG Ping2, SUN Hai-tian1, 3, HU Jun1. Prediction Model of Soil Moisture Content in Northern Cold Region Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1704-1710. |
| [2] |
WANG Xue-pei1, 2, ZHANG Lu-wei1, 2, BAI Xue-bing3, MO Xian-bin1, ZHANG Xiao-shuan1, 2*. Infrared Spectral Characterization of Ultraviolet Ozone Treatment on Substrate Surface for Flexible Electronics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1867-1873. |
| [3] |
WANG Yue1, 3, 4, CHEN Nan1, 2, 3, 4, WANG Bo-yu1, 5, LIU Tao1, 3, 4*, XIA Yang1, 2, 3, 4*. Fourier Transform Near-Infrared Spectral System Based on Laser-Driven Plasma Light Source[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1666-1673. |
| [4] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
| [5] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
| [6] |
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3. Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1716-1720. |
| [7] |
PENG Yan-fang1, WANG Jun1, WU Zhi-sheng2*, LIU Xiao-na3, QIAO Yan-jiang2*. NIR Band Assignment of Tanshinone ⅡA and Cryptotanshinone by
2D-COS Technology and Model Application Tanshinone Extract[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1781-1785. |
| [8] |
WANG Li-qi1, YAO Jing1, WANG Rui-ying1, CHEN Ying-shu1, LUO Shu-nian2, WANG Wei-ning2, ZHANG Yan-rong1*. Research on Detection of Soybean Meal Quality by NIR Based on
PLS-GRNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1433-1438. |
| [9] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
| [10] |
LI Jia-yi1, YU Mei1, LI Mai-quan1, ZHENG Yu2*, LI Pao1, 3*. Nondestructive Identification of Different Chrysanthemum Varieties Based on Near-Infrared Spectroscopy and Pattern Recognition Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1129-1133. |
| [11] |
CHEN Chu-han1, ZHONG Yang-sheng2, WANG Xian-yan3, ZHAO Yi-kun1, DAI Fen1*. Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1173-1178. |
| [12] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
| [13] |
ZHANG Xiao-hong1, JIANG Xue-song1*, SHEN Fei2*, JIANG Hong-zhe1, ZHOU Hong-ping1, HE Xue-ming2, JIANG Dian-cheng1, ZHANG Yi3. Design of Portable Flour Quality Safety Detector Based on Diffuse
Transmission Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1235-1242. |
| [14] |
ZHENG Kai-yi1, ZHANG Wen1, DING Fu-yuan1, ZHOU Chen-guang1, SHI Ji-yong1, Yoshinori Marunaka2, ZOU Xiao-bo1*. Using Ensemble Refinement (ER) Method to OptimizeTransfer Set of Near-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1323-1328. |
| [15] |
CHENG Jie-hong1, CHEN Zheng-guang1, 2*, YI Shu-juan2. Wavelength Selection Algorithm Based on Minimum Correlation Coefficient for Multivariate Calibration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 719-725. |
|
|
|
|
