CN111060477B - 厌氧共发酵原料生化甲烷势近红外光谱快速检测方法 - Google Patents
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Abstract
本发明的厌氧共发酵原料生化甲烷势近红外光谱快速检测方法是利用近红外光谱进行生化甲烷势检测的方法,其采集植物秸秆和农家肥,按固定比例和随机比例混合后作为样品备用,利用近红外光谱对其扫描,并对其生化甲烷势进行测定后,对样品集划分及光谱数据预处理,再基于竞争自适应重加权采样算法联合遗传模拟退火算法进行生化甲烷势特征波长优选,得到回归模型并对其精精度进行评测,建立检测模型,对需要检测生化甲烷势的秸秆与畜禽粪便按比例混合对其进行近红外光谱扫描,既可完成厌氧共发酵原料生化甲烷势的快速检测。本发明的检测方法,速度快、精度高的特点,实现了秸秆粪便混合厌氧共发酵原料生化甲烷势的快速检测,有效解决了传统生化甲烷势测试方法耗时长、工作强度大的问题。
Description
技术领域
本发明是利用近红外光谱进行生化甲烷势检测的方法,特别是涉及到一种厌氧共发酵原料生化甲烷势近红外光谱快速检测方法。
背景技术
随着社会的飞速发展,人类所面临的能源紧缺压力日益严峻;同时,农牧业的快速发展,产生大量农作物秸秆和畜禽粪便等有机废弃物,由此引发的环境污染问题日益突出。厌氧发酵产沼气作为一项清洁生物质能源技术,是实现农牧废弃物资源化利用、改善环境、解决能源问题的重要手段和发展方向。厌氧发酵原料的最大产甲烷潜力又称生化甲烷势,是评价原料是否适宜进行厌氧发酵生产沼气的关键参数。发酵原料生化甲烷势的测定是进行沼气工程给料、指导沼气装置设计、评估沼气工程运行状态、评价沼气生产经济可行性的重要依据。厌氧共发酵是解决单一原料厌氧发酵产沼气时因底物性质导致产甲烷效率和转化率偏低的有效途径,通过调节厌氧共发酵底物的不同配比,可以有效实现产甲烷条件的优化,在提高厌氧发酵效率的同时有效避免氨抑制和酸败现象的产生。在以秸秆和粪便为原料进行厌氧共发酵生产沼气时,为了优化原料配比,构建最佳产甲烷条件,需要对秸秆粪便混合发酵原料的生化甲烷势进行快速、准确测定。但传统生化甲烷势测试实验至少需要20天以上的时间,难以满足秸秆粪便混合发酵原料生化甲烷势快速测定的需求。
近红外光谱分析技术以其多组分同步快速检测的优势,在农产品及农牧业废弃物的定性分析和定量检测方面得到了广泛应用。近红外光谱基于-CH、-NH和-OH等含氢基团的倍频与组合频信息能够实现有机物中蛋白质、脂肪、淀粉、纤维素、半纤维素、还原糖、总糖、总碳、总氮含量的快速检测,而上述有机物组成成分与其厌氧发酵产沼气能力直接相关。因此,相关学者开始研究应用近红外光谱进行城市固体废弃物、草本植物等有机原料厌氧发酵过程生化甲烷势的快速检测,以解决传统发酵实验测试生化甲烷势耗时过长的问题。但现有厌氧发酵原料生化甲烷势近红外光谱检测技术主要以直接采集的有机物为原料进行厌氧发酵最大产甲烷能力的快速检测,尚未见以秸秆和粪便混合物为原料进行厌氧共发酵生化甲烷势的快速检测;而且现有生化甲烷势近红外光谱检测技术没有考虑全谱建模计算量大、波长冗余严重、不相干和非线性冗余波长点对建模性能影响较大的问题,导致检测精度和效率有待进一步提高。
发明内容
本发明旨在于克服现有技术的不足,提供了一种厌氧共发酵原料生化甲烷势近红外光谱快速检测方法。
本发明的厌氧共发酵原料生化甲烷势近红外光谱快速检测方法,是通过下列步骤实现的:
(1)样品采集与制备
采集植物秸秆和农家肥,将其烘干、粉碎过40目筛后装密封袋备用;按秸秆粪便干物质按固定比例混合制备玉米秸秆和猪粪、牛粪、羊粪、鸡粪混合物样品,制备水稻秸秆和猪粪、牛粪、羊粪、鸡粪混合物样品;再按随机比例制备玉米秸秆与猪粪、牛粪、羊粪、鸡粪混合物样品,按随机比例制备水稻秸秆与猪粪、牛粪、羊粪、鸡粪混合物样品;
(2)近红外光谱采集
采用德国Bruker TANGO型近红外光谱仪对采集与制备的秸秆粪便混合物厌氧共发酵原料样品进行积分球漫反射光谱扫描,光谱采集范围3946~11542cm-1,分辨率为8cm-1,样品扫描32次,装样方式为50mm样品杯,装样高度约10mm,采用旋转台进行旋转扫描,背景每小时扫描一次;在保持室内温湿度基本稳定的情况下,每个样品装样3次,取3~5次扫描平均值作为样品的原始光谱;
(3)生化甲烷势测定
对采集与制备的秸秆粪便混合物厌氧共发酵原料样品进行常规生化甲烷势测试实验,取常年驯化正常产气的牛粪厌氧发酵液为接种物,按接种比1:1调节发酵原料与接种物比例,使发酵系统的起始干物质浓度约为6%,采用中温批式厌氧发酵实验完成厌氧共发酵原料生化甲烷势的测定;
(4)样品集划分及光谱数据预处理
以按固定比例混合的秸秆粪便混合物样品和单一秸秆、粪便样品作为校正集,以随机混合的秸秆粪便混合物样品作为验证集,采用光谱平滑、多元散射校正、标准正则变换、导数处理及其多种方法相结合的方式对光谱数据进行预处理,建立不同预处理方法下的全谱偏最小二乘回归模型,并基于校正集的交叉验证均方根误差最小确定采用的光谱预处理方法;
(5)基于竞争自适应重加权采样算法联合遗传模拟退火算法进行生化甲烷势特征波长优选
a:基于竞争自适应重加权采样算法的近红外光谱特征波长初步优选:
竞争自适应重加权采样算法特征波长迭代搜索过程中引入蒙特卡洛采样和自适应加权采样两个随机因素,通过多次执行该算法,并选取多次重复选中的特征波长点作为优选结果的方式,能够进一步提高回归模型的性能;执行竞争自适应重加权采样算法多个轮次,并选定校正集交叉验证均方根误差最小时对应的重复选中波长点作为竞争自适应重加权采样算法得到的特征波长初步优选结果;
b:基于遗传模拟退火算法的近红外光谱特征波长二次优选:
采用遗传模拟退火算法对初步优选结果进行特征波长二次优选,遗传模拟退火算法以多次竞争自适应重加权采样算法优选的特征波长点数为码长,进行二进制编码和种群初始化,以校正集的交叉验证均方根误差为目标函数f(x),适应度函数fit(x)=exp[-(f(x)-fmin)/t],其中fmin为当前代种群的最小目标函数值,t为温度参数;在确定初始温度、降温操作,计算适应度函数值后,执行多个轮次的选择、交叉、变异和Metropolis选择复制进化操作,完成1次近红外光谱特征波长的优选。针对遗传模拟退火算法优化结果的随机性问题,执行多个轮次基于遗传模拟退火算法的特征波长二次优选,并基于验证集的预测均方根误差最小选取重复选中的波长点作为特征波长建立偏最小二乘回归模型,能够得到较高的回归模型性能;
(6)检测模型建立
将校正集光谱数据按竞争自适应重加权采样算法联合遗传模拟退火算法优选的生化甲烷势特征波长建立偏最小二乘回归模型,并使用验证集对回归模型的精度进行评测;当建立的偏最小二乘回归模型检测精度满足需求时,输出相应模型,完成厌氧共发酵原料生化甲烷势近红外光谱快速检测模型的构建;
(7)厌氧共发酵原料生化甲烷势的检测
对需要检测生化甲烷势的玉米秸秆、水稻秸秆与猪粪、牛粪、羊粪、鸡粪按任意比例混合制备的厌氧共发酵原料,经烘干、粉碎处理后,进行积分球漫反射近红外光谱扫描,对光谱数据进行预处理后再按优选的特征波长输入检测模型,既可完成厌氧共发酵原料生化甲烷势的快速检测。
本发明的厌氧共发酵原料生化甲烷势近红外光谱快速检测方法,基于竞争自适应重加权采样算法联合遗传模拟退火算法进行生化甲烷势特征波长优选先采用多次执行竞争自适应重加权采样算法的方式进行特征波长初步优选,既解决了谱区优选算法优选特征谱区内部存在大量冗余波长点的问题,又避免了竞争自适应重加权采样算法优选结果的不确定性。在特征波长初步优选的基础上,再使用遗传模拟退火算法对初选结果进行二次优选,进一步剔除相关性较弱的波长点,在兼顾波长优选性能的同时有效减少搜索时间,解决了直接以遗传模拟退火算法优选近红外光谱特征波长时以全谱波长点个数为码长直接编码容易引起解空间发散的问题。基于该波长优选方法建立的厌氧共发酵原料生化甲烷势偏最小二乘回归模型具有检测速度快、精度高的特点,实现了秸秆粪便混合厌氧共发酵原料生化甲烷势的快速检测,有效解决了传统生化甲烷势测试方法耗时长、工作强度大的问题。
附图说明
图1是厌氧共发酵原料生化甲烷势近红外光谱快速检测方法流程示意图;
图2是基于竞争自适应重加权采样算法联合遗传模拟退火算法进行生化甲烷势特征波长优选流程示意图。
具体实施方式
本发明的厌氧共发酵原料生化甲烷势近红外光谱快速检测方法,是适应秸秆和粪便混合厌氧发酵过程中生化甲烷势的快速检测的需求,其具体步骤如下:
(1)样品采集与制备
采集实验用玉米秸秆、水稻秸秆、猪粪、牛粪、羊粪、鸡粪样品,烘干、粉碎过40目筛后装密封袋备用。按秸秆粪便干物质比9:1、8:2、7:3、6:4、5:5、4:6、3:7、2:8和1:9的比例制备玉米秸秆和猪粪、牛粪、羊粪、鸡粪混合物样品各9个,制备水稻秸秆和猪粪、牛粪、羊粪、鸡粪混合物样品各9个,再按随机比例制备玉米秸秆与猪粪、牛粪、羊粪、鸡粪混合物样品各3个,按随机比例制备水稻秸秆与猪粪、牛粪、羊粪、鸡粪混合物样品各3个,每个样品重10g,连同6个单一秸秆、粪便样品,共计采集与制秸秆粪便厌氧共发酵原料样品102个。
(2)近红外光谱采集
采用德国Bruker TANGO型近红外光谱仪对采集与制备的厌氧共发酵原料样品进行积分球漫反射光谱扫描,光谱采集范围3946~11542cm-1,分辨率为8cm-1,样品扫描32次,装样方式为50mm样品杯,装样高度约10mm,采用旋转台进行旋转扫描,背景每小时扫描一次。在保持室内温湿度基本稳定的情况下,每个样品装样3次,取3次扫描平均值作为样品的原始光谱。
(3)生化甲烷势测定
对采集与制备的102个样品进行常规生化甲烷势测试实验,取常年驯化正常产气的牛粪厌氧发酵液为接种物,进行中温批式厌氧发酵实验完成厌氧共发酵原料生化甲烷势的测定。按接种比1:1(干物质比)调节发酵原料与接种物比例,使发酵系统的起始干物质浓度约为6%,在中温36±1℃恒温水浴槽中采用500mL玻璃三角瓶(有效发酵容积350mL)作为反应器进行厌氧发酵,发酵周期为30天,设置3个空白接种物作为对照。实验过程中每天定时对厌氧发酵反应器进行手摇搅拌2次,混匀料液的同时避免浮渣结壳。发酵过程中,采用集气袋进行气体收集,每天测量产气量和甲烷含量。用排水法测量发酵过程中产生气体的体积,用安捷伦GC-6890N型气相色谱仪测定气体组分。
(4)样品集划分及光谱数据预处理
以按固定比例混合的72个秸秆粪便混合物样品和6个单一秸秆、粪便样品作为校正集,以随机混合的24个秸秆粪便混合物样品作为验证集,采用光谱平滑、多元散射校正、标准正则变换、导数处理及其多种方法相结合的方式对光谱数据进行预处理,建立不同预处理方法下的全谱偏最小二乘回归模型,并基于校正集的交叉验证均方根误差最小确定采用的光谱预处理方法。通过计算比较后确定采用多元散射校正联合卷积平滑的方法对光谱数据进行预处理。
(5)基于竞争自适应重加权采样算法联合遗传模拟退火算法进行生化甲烷势特征波长优选
a:基于竞争自适应重加权采样算法的近红外光谱特征波长初步优选:
竞争自适应重加权采样算法在特征波长迭代搜索过程中引入蒙特卡洛采样和自适应加权采样两个随机因素,难以保证每次优选特征波长的一致性。为了解决竞争自适应重加权采样算法优选结果不一致的问题,通过多次执行该算法,并选取多次重复选中的特征波长点作为优选结果的方式,能够进一步提高回归模型的性能。执行竞争自适应重加权采样算法500轮次,并选定校正集交叉验证均方根误差最小时对应的重复选中波长点作为竞争自适应重加权采样算法得到的特征波长初步优选结果。
b:基于遗传模拟退火算法的近红外光谱特征波长二次优选:
针对基于竞争自适应重加权采样算法的近红外光谱特征波长初步优选结果中存在少量相关性较弱波长点的问题,采用遗传模拟退火算法对初步优选结果进行特征波长二次优选。遗传模拟退火算法以多次竞争自适应重加权采样算法优选的特征波长点数为码长,进行二进制编码和种群初始化,以校正集的交叉验证均方根误差为目标函数f(x),适应度函数fit(x)=exp[-(f(x)-fmin)/t],其中fmin为当前代种群的最小目标函数值,t为温度参数。在确定初始温度、降温操作,计算适应度函数值后,执行多个轮次的选择、交叉、变异和Metropolis选择复制进化操作,完成1次近红外光谱特征波长的优选。针对遗传模拟退火算法优化结果的随机性问题,执行100轮次基于遗传模拟退火算法的特征波长二次优选,并基于验证集的预测均方根误差最小选取重复选中的波长点作为特征波长建立偏最小二乘回归模型,能够得到较高的回归模型性能。
(6)检测模型建立
将校正集光谱数据按竞争自适应重加权采样算法联合遗传模拟退火算法优选的生化甲烷势特征波长建立偏最小二乘回归模型,并使用验证集对回归模型的精度进行评测。若验证集的评测结果不满足实际检测精度要求,重新执行(5),进行特征波长优选和回归模型建立;当建立的偏最小二乘回归模型检测精度满足需求时,输出相应模型,完成厌氧共发酵原料生化甲烷势近红外光谱快速检测模型的构建。
(7)厌氧共发酵原料生化甲烷势的检测
对需要检测生化甲烷势的玉米秸秆、水稻秸秆与猪粪、牛粪、羊粪、鸡粪按任意比例混合制备的厌氧共发酵原料,经烘干、粉碎处理后,进行积分球漫反射近红外光谱扫描,对光谱数据进行预处理后再按优选的特征波长输入检测模型,既可完成厌氧共发酵原料生化甲烷势的快速检测。
Claims (1)
1.厌氧共发酵原料生化甲烷势近红外光谱快速检测方法,所述方法是通过下列步骤实现的:
(1)样品采集与制备
采集农作物秸秆和畜禽粪便样品,将其烘干、粉碎过40目筛后装密封袋备用;将秸秆与粪便按干物质固定比例混合制备玉米秸秆和猪粪、牛粪、羊粪、鸡粪混合物样品,制备水稻秸秆和猪粪、牛粪、羊粪、鸡粪混合物样品;再按随机比例制备玉米秸秆与猪粪、牛粪、羊粪、鸡粪混合物样品,按随机比例制备水稻秸秆与猪粪、牛粪、羊粪、鸡粪混合物样品;
(2)近红外光谱采集
采用德国Bruker TANGO型近红外光谱仪对采集与制备的秸秆粪便混合物厌氧共发酵原料样品进行积分球漫反射光谱扫描,光谱采集范围3946~11542 cm-1,分辨率为8 cm-1,样品扫描32次,装样方式为50 mm样品杯,装样高度为10 mm,采用旋转台进行旋转扫描,背景每小时扫描一次;在保持室内温湿度基本稳定的情况下,每个样品装样3次,取3~5次扫描平均值作为样品的原始光谱;
(3)生化甲烷势测定
对采集与制备的秸秆粪便混合物厌氧共发酵原料样品进行常规生化甲烷势测试实验,取常年驯化正常产气的牛粪厌氧发酵液为接种物,按接种比1:1调节发酵原料与接种物比例,使发酵系统的起始干物质浓度为6%,采用中温批式厌氧发酵实验完成厌氧共发酵原料生化甲烷势的测定;
(4)样品集划分及光谱数据预处理
以按固定比例混合的秸秆粪便混合物样品和单一秸秆、粪便样品作为校正集,以随机混合的秸秆粪便混合物样品作为验证集,采用光谱平滑、多元散射校正、标准正则变换、导数处理及其多种方法相结合的方式对光谱数据进行预处理,建立不同预处理方法下的全谱偏最小二乘回归模型,并基于校正集的交叉验证均方根误差最小确定采用的光谱预处理方法;
其特征在于:
(5)基于竞争自适应重加权采样算法联合遗传模拟退火算法进行生化甲烷势特征波长优选
a:基于竞争自适应重加权采样算法的近红外光谱特征波长初步优选:
竞争自适应重加权采样算法特征波长迭代搜索过程中引入蒙特卡洛采样和自适应加权采样两个随机因素,通过多次执行该算法,并选取多次重复选中的特征波长点作为优选结果的方式,能够进一步提高回归模型的性能;执行竞争自适应重加权采样算法多个轮次,并选定校正集交叉验证均方根误差最小时对应的重复选中波长点作为竞争自适应重加权采样算法得到的特征波长初步优选结果;
b:基于遗传模拟退火算法的近红外光谱特征波长二次优选:
采用遗传模拟退火算法对初步优选结果进行特征波长二次优选,遗传模拟退火算法以多次竞争自适应重加权采样算法优选的特征波长点数为码长,进行二进制编码和种群初始化,以校正集的交叉验证均方根误差为目标函数,适应度函数,其中为当前代种群的最小目标函数值,为温度参数;在确定初始温度、降温操作,计算适应度函数值后,执行多个轮次的选择、交叉、变异和Metropolis选择复制进化操作,完成1次近红外光谱特征波长的优选;针对遗传模拟退火算法优化结果的随机性问题,执行多个轮次基于遗传模拟退火算法的特征波长二次优选,并基于验证集的预测均方根误差最小选取重复选中的波长点作为特征波长建立偏最小二乘回归模型,能够得到较高的回归模型性能;
(6)检测模型建立
将校正集光谱数据按竞争自适应重加权采样算法联合遗传模拟退火算法优选的生化甲烷势特征波长建立偏最小二乘回归模型,并使用验证集对回归模型的精度进行评测;当建立的偏最小二乘回归模型检测精度满足需求时,输出相应模型,完成厌氧共发酵原料生化甲烷势近红外光谱快速检测模型的构建;
(7)厌氧共发酵原料生化甲烷势的检测
对需要检测生化甲烷势的玉米秸秆、水稻秸秆与猪粪、牛粪、羊粪、鸡粪按任意比例混合制备的厌氧共发酵原料,经烘干、粉碎处理后,进行积分球漫反射近红外光谱扫描,对光谱数据进行预处理后再按优选的特征波长输入检测模型,既可完成厌氧共发酵原料生化甲烷势的快速检测。
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