The current energy crisis could be alleviated by enhancing energy generation using the abundant biomass waste resources. Agricultural and forest wastes are the leading organic waste streams that can be transformed int...The current energy crisis could be alleviated by enhancing energy generation using the abundant biomass waste resources. Agricultural and forest wastes are the leading organic waste streams that can be transformed into useful alternative energy resources. Pyrolysis is one of the technologies for converting biomass into more valuable products, such as bio-oil, bio-char, and syngas. This work investigated the production of bio-oil through batch pyrolysis technology. A fixed bed pyrolyzer was designed and fabricated for bio-oil production. The major components of the system include a fixed bed reactor, a condenser, and a bio-oil collector. The reactor was heated using a cylindrical biomass external heater. The pyrolysis process was carried out in a reactor at a pressure of 1atm and a varying operating temperature of 150˚C, 250˚C, 350˚C to 450˚C for 120 minutes. The mass of 1kg of coconut fiber was used with particle sizes between 2.36 mm - 4.75 mm. The results show that the higher the temperature, the more volume of bio-oil produced, with the highest yield being 39.2%, at 450˚C with a heating rate of 10˚C/min. The Fourier transformation Infrared (FTIR) Spectroscopy analysis was used to analyze the bio-oil components. The obtained bio-oil has a pH of 2.4, a density of 1019.385 kg/m<sup>3</sup>, and a calorific value of 17.5 MJ/kg. The analysis also showed the presence of high-oxygenated compounds;carboxylic acids, phenols, alcohols, and branched oxygenated hydrocarbons as the main compounds present in the bio-oil. The results inferred that the liquid product could be bestowed as an alternative resource for polycarbonate material production.展开更多
In this paper, we present simultaneous multiple pollutant gases （CO2, CO, and NO） measurements by using the non-dispersive infrared （NDIR） technique. A cross-correlation correction method is proposed and used to c...In this paper, we present simultaneous multiple pollutant gases （CO2, CO, and NO） measurements by using the non-dispersive infrared （NDIR） technique. A cross-correlation correction method is proposed and used to correct the cross-interferences among the target gases. The calculation of calibration curves is based on least-square fittings with third-order polynomials, and the interference functions are approximated by linear curves. The pure absorbance of each gas is obtained by solving three simultaneous equations using the fitted interference functions. Through the interference correction, the signal created at each filter channel only depends on the absorption of the intended gas. Gas mixture samples with different concentrations of CO2, CO, and NO are pumped into the sample cell for analysis. The results show that the measurement error of each gas is less than 4.5%.展开更多
目的利用拉曼光谱与中红外光谱的数据融合技术实现对食用酒精乙醇浓度(酒精度)的快速定量检测。方法首先,分别采集不同浓度食用酒精水溶液的拉曼光谱与中红外光谱。其次,采用多元散射校正(multiplicative scatter correction,MSC)、卷...目的利用拉曼光谱与中红外光谱的数据融合技术实现对食用酒精乙醇浓度(酒精度)的快速定量检测。方法首先,分别采集不同浓度食用酒精水溶液的拉曼光谱与中红外光谱。其次,采用多元散射校正(multiplicative scatter correction,MSC)、卷积平滑(Savitzky-Golay,S-G)、一阶求导的方法对原始数据进行预处理。然后,基于自举软缩减法(bootstrapping soft shrinkage,BOSS)和无信息变量消除算法(uninformative variable elimination,UVE)分别对预处理后的光谱数据进行特征提取,并利用X-Y距离样本集划分法(sample set partitioning based on joint X-Y distance,SPXY)将光谱数据划分为校正集和预测集。最后,建立基于拉曼光谱-中红外光谱数据融合的偏最小二乘回归(partial least squares regression,PLSR)食用酒精乙醇浓度预测模型,并利用麻雀搜寻算法优化的混合核极限学习机算法(sparrow search algorithm-optimized hybrid kernel extreme learning machine,SSA-HKELM)提升预测性能,实现对不同浓度食用酒精的快速、准确定量检测。结果与拉曼光谱数据、中红外光谱数据以及中红外与拉曼光谱的数据层融合构建的预测模型相比,中红外光谱与拉曼光谱特征层融合数据构建的预测模型具有更好的预测性能。其中,最优模型的校正集均方根误差(root mean squared error of calibration set,RMSEC)为0.98314,校正集决定系数(R_(c)^(2))为0.99634,预测集均方根误差(root mean squared error of prediction set,RMSEP)为1.03256,预测集决定系数(R_(p)^(2))为0.99036。结论中红外光谱与拉曼光谱特征层融合预测模型可以实现对不同浓度食用酒精的高效定量检测,为食用酒精的质量检测提供了有效的理论支持与技术保障。展开更多
显示器电磁木马是通过控制计算机屏幕电磁辐射达到窃取信息目的的一种新型木马。当前的主流防护思想是用软件防护代替较为成熟但造价昂贵的硬件防护机制,然而目前软防护思想大多侧重于理论方法的探索,在实现机制上相对比较复杂。针对显...显示器电磁木马是通过控制计算机屏幕电磁辐射达到窃取信息目的的一种新型木马。当前的主流防护思想是用软件防护代替较为成熟但造价昂贵的硬件防护机制,然而目前软防护思想大多侧重于理论方法的探索,在实现机制上相对比较复杂。针对显示器电磁木马的工作特点提出了Soft-TEMPEST防护机制,设计了显示器电磁木马的ADFA(API Detection and Frequency Analysis)检测方法。该方法通过API函数序列的周期性挖掘分析,结合对屏幕像素信息的傅里叶变换及频谱分析,达到检测出木马进程的目的。测试结果表明,该方法能够成功检测出多种显示器电磁木马,而且原理简单,方便投入使用。展开更多
文摘The current energy crisis could be alleviated by enhancing energy generation using the abundant biomass waste resources. Agricultural and forest wastes are the leading organic waste streams that can be transformed into useful alternative energy resources. Pyrolysis is one of the technologies for converting biomass into more valuable products, such as bio-oil, bio-char, and syngas. This work investigated the production of bio-oil through batch pyrolysis technology. A fixed bed pyrolyzer was designed and fabricated for bio-oil production. The major components of the system include a fixed bed reactor, a condenser, and a bio-oil collector. The reactor was heated using a cylindrical biomass external heater. The pyrolysis process was carried out in a reactor at a pressure of 1atm and a varying operating temperature of 150˚C, 250˚C, 350˚C to 450˚C for 120 minutes. The mass of 1kg of coconut fiber was used with particle sizes between 2.36 mm - 4.75 mm. The results show that the higher the temperature, the more volume of bio-oil produced, with the highest yield being 39.2%, at 450˚C with a heating rate of 10˚C/min. The Fourier transformation Infrared (FTIR) Spectroscopy analysis was used to analyze the bio-oil components. The obtained bio-oil has a pH of 2.4, a density of 1019.385 kg/m<sup>3</sup>, and a calorific value of 17.5 MJ/kg. The analysis also showed the presence of high-oxygenated compounds;carboxylic acids, phenols, alcohols, and branched oxygenated hydrocarbons as the main compounds present in the bio-oil. The results inferred that the liquid product could be bestowed as an alternative resource for polycarbonate material production.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2009AA063006)the National Natural Science Foundation of China (Grant No. 40805015)the Excellent Youth Scientific Foundation of Anhui Province, China (Grant No. 10040606Y28)
文摘In this paper, we present simultaneous multiple pollutant gases （CO2, CO, and NO） measurements by using the non-dispersive infrared （NDIR） technique. A cross-correlation correction method is proposed and used to correct the cross-interferences among the target gases. The calculation of calibration curves is based on least-square fittings with third-order polynomials, and the interference functions are approximated by linear curves. The pure absorbance of each gas is obtained by solving three simultaneous equations using the fitted interference functions. Through the interference correction, the signal created at each filter channel only depends on the absorption of the intended gas. Gas mixture samples with different concentrations of CO2, CO, and NO are pumped into the sample cell for analysis. The results show that the measurement error of each gas is less than 4.5%.
文摘目的利用拉曼光谱与中红外光谱的数据融合技术实现对食用酒精乙醇浓度(酒精度)的快速定量检测。方法首先,分别采集不同浓度食用酒精水溶液的拉曼光谱与中红外光谱。其次,采用多元散射校正(multiplicative scatter correction,MSC)、卷积平滑(Savitzky-Golay,S-G)、一阶求导的方法对原始数据进行预处理。然后,基于自举软缩减法(bootstrapping soft shrinkage,BOSS)和无信息变量消除算法(uninformative variable elimination,UVE)分别对预处理后的光谱数据进行特征提取,并利用X-Y距离样本集划分法(sample set partitioning based on joint X-Y distance,SPXY)将光谱数据划分为校正集和预测集。最后,建立基于拉曼光谱-中红外光谱数据融合的偏最小二乘回归(partial least squares regression,PLSR)食用酒精乙醇浓度预测模型,并利用麻雀搜寻算法优化的混合核极限学习机算法(sparrow search algorithm-optimized hybrid kernel extreme learning machine,SSA-HKELM)提升预测性能,实现对不同浓度食用酒精的快速、准确定量检测。结果与拉曼光谱数据、中红外光谱数据以及中红外与拉曼光谱的数据层融合构建的预测模型相比,中红外光谱与拉曼光谱特征层融合数据构建的预测模型具有更好的预测性能。其中,最优模型的校正集均方根误差(root mean squared error of calibration set,RMSEC)为0.98314,校正集决定系数(R_(c)^(2))为0.99634,预测集均方根误差(root mean squared error of prediction set,RMSEP)为1.03256,预测集决定系数(R_(p)^(2))为0.99036。结论中红外光谱与拉曼光谱特征层融合预测模型可以实现对不同浓度食用酒精的高效定量检测,为食用酒精的质量检测提供了有效的理论支持与技术保障。
文摘显示器电磁木马是通过控制计算机屏幕电磁辐射达到窃取信息目的的一种新型木马。当前的主流防护思想是用软件防护代替较为成熟但造价昂贵的硬件防护机制,然而目前软防护思想大多侧重于理论方法的探索,在实现机制上相对比较复杂。针对显示器电磁木马的工作特点提出了Soft-TEMPEST防护机制,设计了显示器电磁木马的ADFA(API Detection and Frequency Analysis)检测方法。该方法通过API函数序列的周期性挖掘分析,结合对屏幕像素信息的傅里叶变换及频谱分析,达到检测出木马进程的目的。测试结果表明,该方法能够成功检测出多种显示器电磁木马,而且原理简单,方便投入使用。
