Active gas/odor sensing system using automatically controlled gas blender and numerical optimization technique
Active gas/odor sensing system using automatically controlled gas blender and numerical optimization technique
复制标题
采用自动控制气体混合器和数值优化技术的主动气体/气味传感系统
DOI：
10.1016/0925-4005(93)01193-8
复制
发表时间：
1994
影响因子：
8.4
通讯作者：
Y. Sonoda
中科院分区：
文献类型：
--
作者：
T. Nakamoto;S. Ustumi;N. Yamashita;T. Moriizumi;Y. Sonoda
由于蒸汽混合物组成的测量是一项困难的技术，尽管许多研究人员付出了巨大的努力，但还没有建立起使用传感系统的方法。本文提出了一种新的气体/气味传感系统，该系统采用气体混合器和一种非线性数值优化算法，通过该系统可以量化未知蒸气中各组分的浓度。组分蒸汽在内部混合，并由系统修改混合比，以使混合蒸汽的传感器阵列输出方向图与未知蒸汽的传感器阵列输出方向图相等。经过几次迭代，得到了收敛，并根据混合蒸汽的混合物组成确定了每个组分的蒸汽浓度。虽然传统的系统是被动的，但这个系统被认为是主动的，因为它在识别之前执行探索性行为。这里，汽油蒸汽浓度是在一个或两个干扰蒸汽同时存在的情况下测量的。汽油蒸气已经被用作汽车乘客舱内气味的一个例子，因为它有时会闻起来不舒服。测量对于设计一辆汽车以保持乘客的舒适度是必不可少的。这里使用的传感器是三个半导体气体传感器和两个电化学传感器，它们是为了获得对汽油的高灵敏度而选择的。采用的非线性数值优化技术有单纯形法和梯度下降法，并对这两种方法进行了比较。结果表明，对于两组分和三组分的蒸气，其定量误差均在10ppm以内。
As measurement of a vapor mixture composition is a difficult technique, no method using a sensing system has yet been established in spite of great effort by many researchers. In this paper, the authors propose a new gas/odor sensing system using a gas blender and a nonlinear numerical optimization algorithm by which the concentration of each component in an unknown vapor can be quantified. The component vapors are internally blended and the mixture ratio is modified by the system so that the sensor array output pattern of the blended vapor can be made equal to that of the unknown one. After several iterations, convergence is obtained and the vapor concentration of each component is determined from the mixture composition of the blended vapor. Although the conventional system is passive, this system is considered as an active one as it performs exploratory behavior prior to recognition. Here, gasoline vapor concentration is measured under the condition that one or two interference vapors exist together. Gasoline vapor has been adopted as an example of odors in the passenger compartment of a car, since it sometimes smells unpleasant. The measurement is essential for designing a car in order to keep it comfortable for passengers. The sensors used here are three semiconductor gas sensors and two electrochemical sensors, which are chosen in order to obtain high sensitivity to gasoline. The nonlinear numerical optimization techniques used are the simplex method and the gradient descent method and these two methods are compared here. It is found that the quantification error is within ten ppm for two- or three-component vapors.