The catalytic performance of a series of La-Fe/AC catalysts was studied for the selective catalytic reduction (SCR) of NO by CO. With the increase in La content, the Fe2+/Fe3+ ratio and amount of surface oxygen vacancies (SOV) in the catalysts increased; thus the catalytic activity improved. Incorporating the promoters to La3-Fe1/active carbon (AC) catalyst could affect the catalyst activity by changing the electronic structure. The increase in Fe2+/Fe3+ ratio after the promoter addition is possibly due to the extra synergistic interaction of M (Mn and Ce) and Fe through the redox equilibrium of M3+ + Fe3+ ↔ M4+ + Fe2+. This phenomenon could have improved the redox cycle, enhanced the SOV formation, facilitated NO decomposition, and accelerated the CO-SCR process. The presence of O2 enhanced the formation of the C(O) complex and improved the activation of the metal site. Mn@La3-Fe1/AC catalyst revealed an excellent NO conversion of 93.8% at 400 °C in the presence of 10% oxygen. The high catalytic performance of MnOx and double exchange behavior of Mn3+ and Mn4+ can increase the number of SOV and improve the catalytic redox properties.

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Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (M RW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

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Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart patterns (CCPs). The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for finding of optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.