Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS

Pythagorean fuzzy sets (PFSs) were proposed by Yager in 2013 to treat imprecise and vague information in daily life more rigorously and efficiently with higher precision than intuitionistic fuzzy sets. In this paper, we construct new distance and similarity measures of PFSs based on the Hausdorff metric. We first develop a method to calculate a distance between PFSs based on the Hasudorff metric, along with proving several properties and theorems. We then consider a generalization of other distance measures, such as the Hamming distance, the Euclidean distance, and their normalized versions. On the basis of the proposed distances for PFSs, we give new similarity measures to compute the similarity degree of PFSs. Some examples related to pattern recognition and linguistic variables are used to validate the proposed distance and similarity measures. Finally, we apply the proposed methods to multicriteria decision-making by constructing a Pythagorean fuzzy Technique for Order Preference by Similarity to an Ideal Solution and then present a practical example to address an important issue related to social sector. Numerical results indicate that the proposed methods are reasonable and applicable and also that they are well suited in pattern recognition, linguistic variables, and multicriteria decision-making with PFSs.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

冠状动脉系统的微分积分终端滑模混沌抑制

冠状动脉系统的混沌现象会导致严重的健康问题。以非线性冠状动脉系统为研究对象，建立了不确定性冠状动脉系统动力学模型，提出了不确定性冠状动脉系统的微分积分终端滑模混沌抑制方法，针对该模型的不确定性设计了扰动观测器，根据Lyapunov理论证明了所设计控制系统的稳定性，通过仿真实验验证了所提出的混沌抑制方法的有效性和可行性。     

高效液相色谱法测定维生素K胶囊中维生素K2含量不确定度评估

目的评估高效液相色谱法测定维生素K胶囊中的维生素K_2含量的不确定度。方法采用高效液相色谱法测定维生素K胶囊中的维生素K_2含量,建立数学模型,对测定过程的主要不确定度分量进行计算,得出合成不确定度和扩展不确定度。结果维生素K胶囊中维生素K_2的含量为181.2μg/g时,在置信区间95%下,其扩展不确定度为7.5μg/g(k=2)。结论不确定度的主要来源为标准曲线配制的过程,该评估模型为维生素K胶囊中维生素K_2的含量测定的准确性提供了参考依据。     

电感耦合等离子体质谱法测定辣椒镉含量中不确定度的评估

摘要：目的 采用电感耦合等离子体质谱法(ICP-MS)测定辣椒中镉（Cd）含量，及评估测定过程产生的不确定度，以提高定量分析结果的可靠性。方法 建立数学模型，分析识别试样称量、试样定容、标准物质、标准溶液的配制、标准曲线的拟合、重复测量、回收试验等不确定度来源，量化各不确定度分量，计算合成标准不确定度。结果 辣椒中镉含量不确定度测定结果为：。结论 测定过程不确定度主要来源于标准溶液配制和标准曲线拟合。电感耦合等离子体质谱法测定辣椒中镉含量准确度高，重复性好，建立的不确定度评估能保证分析结果的质量。     

液相色谱-电感耦合等离子体质谱法测定大米中无机砷的不确定度评估

目的评估液相色谱-电感耦合等离子体质谱法(liquid chromatography inductively coupled plasma mass spectrometry, LC-ICPMS)测定大米中无机砷的不确定度。方法采用LC-ICPMS测定大米中As~Ⅲ和As~Ⅴ含量,建立数学模型,并从测量重复性、样品称量、提取液移取体积、标准溶液配制、标准曲线拟合等方面进行测量不确定度的计算。结果大米样品中As~Ⅲ、As~Ⅴ、i-As的含量分别为0.180、0.014、0.194 mg/kg,在95%的置信区间下,其扩展不确定度为0.003、0.002、0.028 mg/kg, k=2。结论测量过程中的不确定度主要来源于测量的重复性、标准溶液的配制和曲线拟合,需要在测量过程中加以注意。     

白酒中氰化物的测定及不确定度的评定

氰化物是白酒检测卫生指标的重要一项,按照GB 5009.36-2016《食品安全国家标准食品中氰化物的测定》[1]第一法测定白酒中的氰化物的含量。利用分光光度法测定白酒中氰化物的含量,并对整个检测过程的不确定度来源进行分析,评估不确定度。经过分析,不确定度的来源主要包括样品测定过程中随机效应产生的不确定度、试样量取、定容产生的不确定度、标准曲线拟合、仪器的量化误差产生的不确定度等。经评定,白酒中氰化物测定相对扩展不确定度为U95r(el x)=4.58%,包含概率为95%,包含因子k=2。