摘要
通过固定床石英管热解装置将稻壳、木薯秸秆及玉米秸秆在350、450、500、550、600℃进行充分热解制备生物炭,利用图像识别技术获得生物炭的RGB值(红、绿、蓝三个通道的颜色)及相应的灰度值,研究了生物炭灰度值与其水溶液中的pH值及阳离子(NH_4^+-N及K^+-K)吸附性能的关系.结果表明:3种生物炭的pH值随着灰度值的增加呈现"S"型增长趋势,并符合DoseResp模型,回归方程的决定系数(R^2)分别为0.9766、0.9592和0.9219,残差平方和(RSS)均小于0.01;除玉米秸秆炭的K^+-K吸附量与灰度值的关系为线性负相关外,3种生物炭的NH_4^+-N和K^+-K吸附量与灰度值之间满足一元高次非线性模型,R2范围在0.8595~0.9999.本研究为快速预测生物炭在水溶液中的pH值和阳离子吸附性能提供了理论基础.
According to statistics of the past 40years,average fertilizer use efficiency is only about 50%.The low fertilizer efficiency not only causes energy loss but also causes environmental pollution and ground and surface water contamination.A new fertilizer using biochar as carrier has a great potential to solve this issue.In this research,the biochars made from rice husk,cassava straw and corn stover were obtained by a horizontal fixed bed quartz tube pyrolysis reactor with varied temperatures(350,450,500,550,and 600℃).The RGB values and corresponding gray value of these biochars were analyzed by image recognition technology.The relationship between pH and gray value and the relationship between adsorption capacities of N and K cations(NH4+-N and K+-K,)and gray value were studied.In general,the pH value of biochar increased as gray value increased with R2of 0.9766,0.9592,and 0.9219,respectively,and the trend fitted well with the DoseResp model.NH4+-N and K+-K adsorption capacity of those three biochars followed the one-dimensional high-order nonlinear models with R2 ranged from 0.8595 to 0.9999,except the negative linear correlation between K+-K adsorption capacity and gray value for corn stover.This study provides theoretical basis for development of rapid method to predict pH and cation adsorption properties of biochar,and also has a potential to develop portable equipment for online testing.
作者
王明峰
杨璐菡
张争艳
吴西文
钟旋
蒋恩臣
WANG Ming-feng;YANG Lu-han;ZHANG Zheng-yan;WU Xi-wen;ZHONG Xuan;JIANG En-cheng(Key laboratory of Ministry of Agriculture Energy Plant Resources and the Use,College of Materials and Energy,South China Agricultural University,Guangzhou 510642,China)
出处
《中国环境科学》
EI
CAS
CSSCI
CSCD
北大核心
2018年第11期4141-4146,共6页
China Environmental Science
基金
国家自然科学基金资助项目(51706074)
广东省科技计划项目(2015B020237010,2016A020221028).