Biochar yield prediction using response surface methodology: effect of fixed carbon and pyrolysis operating conditions

被引:34
|
作者
Mariyam, Sabah [1 ]
Alherbawi, Mohammad [1 ]
Pradhan, Snigdhendubala [1 ]
Al-Ansari, Tareq [1 ]
McKay, Gordon [1 ]
机构
[1] Hamad Bin Khalifa Univ, Qatar Fdn, Coll Sci & Engn, Div Sustainable Dev, Doha, Qatar
关键词
Pyrolysis; Biochar; Response surface methodology; Yield; Fixed carbon; Prediction; MICROWAVE-ASSISTED PYROLYSIS; BOX-BEHNKEN DESIGN; BIOMASS; OPTIMIZATION; WASTE;
D O I
10.1007/s13399-023-03825-6
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Generating value from wastes via pyrolysis has been increasingly researched in recent times. Biochar is a versatile pyrolysis product with yields based on many process parameters, including feedstock type and particle size, and operating conditions such as pyrolysis reactor, heating rate, residence time, and reaction temperature. The heterogeneous nature of waste biomass creates challenges in controlling the pyrolysis' product selectivity. Intensive and time-consuming experimental studies are often required to determine product distribution for the pyrolysis of each unique feedstock. Alternatively, prediction models that learn from a wide range of existing experimental data may provide insight into potential yields for different biomass sources. Several advanced models exist in the literature which can predict the yield of biochar and subsequent products based on operating temperature. However, these models do not consider the combined effect of biomass characteristics and operating conditions on biochar yield, which is considered a decisive factor for biochar formation. As such, the objective of this study is to develop a prediction model based on the biomass' fixed carbon content (14-22%), reaction temperature (350-750 degrees C), and heating rate (5-10 degrees C/min) using the response surface methodology. Biomasses, date stones, spent coffee grounds, and cow manure have been used to design a Box-Behnken experiment based on the three factors for the biochar yield response. An empirical equation is developed based on a statistically significant quadratic model to produce optimized biochar yield with high prediction accuracy. The study discussed the 3D response and diagnostic plots and conducted validation experiments to confirm the applicability of the developed model. The biochar yields are significantly affected by the fixed carbon content of the feedstock and the reaction temperature, and the experimental validation confirms the accuracy of biochar yield quantification. The model can be easily applied for further process flow modeling of biomass pyrolysis, only relying on proximate feed analysis, operating temperature, and heating rate.
引用
收藏
页码:28879 / 28892
页数:14
相关论文
共 50 条
  • [1] Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions
    Zhu, Xinzhe
    Li, Yinan
    Wang, Xiaonan
    BIORESOURCE TECHNOLOGY, 2019, 288
  • [2] Microwave-assisted pyrolysis of food waste: optimization of fixed carbon content using response surface methodology
    Kadlimatti, H. M.
    Mohan, B. Raj
    Saidutta, M. B.
    BIOFUELS-UK, 2021, 12 (09): : 1051 - 1058
  • [3] Prediction of biochar characteristics and optimization of pyrolysis process by response surface methodology combined with artificial neural network
    Xie, Haiwei
    Zhou, Xuan
    Zhang, Yan
    Yan, Wentao
    BIOMASS CONVERSION AND BIOREFINERY, 2025, 15 (03) : 4745 - 4757
  • [4] Optimization of Euphorbia rigida fast pyrolysis conditions by using response surface methodology
    Kilic, Murat
    Putun, Ersan
    Putun, Ayse E.
    JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS, 2014, 110 : 163 - 171
  • [5] Optimization of batch operating conditions for the decolourization of vinasses using surface response methodology
    Vecino, X.
    Devesa-Rey, R.
    Moldes, A. B.
    Cruz, J. M.
    MICROCHEMICAL JOURNAL, 2012, 102 : 83 - 90
  • [6] Prediction of biodiesel yield during transesterification process using response surface methodology
    Mohamad, M.
    Ngadi, N.
    Wong, S. L.
    Jusoh, M.
    Yahya, N. Y.
    FUEL, 2017, 190 : 104 - 112
  • [7] Prediction of liquid yields from the pyrolysis of waste mixtures using response surface methodology
    Pinto, Filomena
    Paradela, Filipe
    Gulyurtlu, Ibrahim
    Ramos, Ana Maria
    FUEL PROCESSING TECHNOLOGY, 2013, 116 : 271 - 283
  • [8] OPTIMISATION OF SPRAY DRYING OPERATING CONDITIONS OF TOMATO SLURRY USING RESPONSE SURFACE METHODOLOGY
    Anisuzzaman, S. M.
    Joseph, Collin G.
    Endu, Olivia Mayang
    JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2023, 18 (01): : 112 - 135
  • [9] Effect of Water on the Supported Ziegler–Natta Catalysts: Optimization of the Operating Conditions by Response Surface Methodology
    Ameneh Rahbar
    Mehdi Nekoomanesh-Haghighi
    Naeimeh Bahri-Laleh
    Hossein Abedini
    Catalysis Letters, 2015, 145 : 1186 - 1195
  • [10] Thermogravimetric analysis and the optimisation of bio-oil yield from fixed-bed pyrolysis of rice husk using response surface methodology (RSM)
    Isa, Khairuddin Md
    Daud, Suhardy
    Hamidin, Nasrul
    Ismail, Khudzir
    Saad, Saiful Azhar
    Kasim, Farizul Hafiz
    INDUSTRIAL CROPS AND PRODUCTS, 2011, 33 (02) : 481 - 487