基于二分程控法消失调电压的微电流测量方法

针对微电流测量存在需要进行细小分辨的问题,提出了一种快速消除失调电压的检测方法,从硬件设计和软件算法方面详细描述了测量系统。选择偏置电流较小的运算放大器,设计了一种消失调电压的微电流检测电路;结合STM32单片机技术,采用二分法利用程序控制数模转换(DAC)模块自动快速调压,在0输入状态,使测量电路尽快达到0输出的稳定状态,消除运放失调电压的影响,提高微电流的测量精度与测量速度。实验结果表明:该方法能够快速有效地消除失调电压的影响,完成0.1μA级精度的电流测量,达到微电流测量要求。

程控升温燃烧法快速测定煤中硫的形态和数量

根据煤中不同结构的硫在一定燃烧条件下的析出规律的差别 ,采用差热天平和定硫仪结合的方法 ,程序控制温度升高确定了煤中硫的组成和数量 .同时通过与一些标准的含硫化合物的热分析结果进行对比 ,定性分析了部分复杂的硫分组成 .结果表明 。

用程控电压抵消法提高A/D转换的分辨率

本文提出了一种提高Ａ／Ｄ转换分辨率的新方法，即程控电压抵消法。它的基本原理是：在加法放大器的输入端引进若干个标定电压，此电压能抵消一部分输入电压，只要将标定电压按照一定的规律加以组合引入，就能在计算机程序控制下很方便地用较低分辨率的Ａ／Ｄ转换器获得较高的分辨率。接着，较详细地介绍了一个用８位Ａ／Ｄ转换器获得１０位分辨的应用实例，并给出了电路图和控制程序流程图。最后进行了误差分析。实践证明，在计算机的配合下，适当提高Ａ／Ｄ转换的分辨率。

瞬变电磁仪中几种干扰的消除方法

通过对瞬变电磁勘探法中常见的几种干扰因素的分析 ,提出了相应的解决方法 ,用程控天线法和随机双极性采样法抑制干扰具有良好的去噪效果 .在实际中 ,只要根据具体情况选择适当方法 ,就能有效地抑制电磁噪声的影响 .无论采用哪种方法 ,最后采用提取特征不变量的方法进行处理后 ,均可以得到更好的效果 .实验结果表明 ,测量曲线的质量较以前有很大提高 .

燃料电池核壳型催化剂NiCo@Pt/C的制备及表征

以乙二醇、C、CoCl2·6H2O和Ni(Ac)2、HPtCl6·4H2O为原料,用NaOH调节pH,采用多元醇还原法、微波震荡法、程控煅烧法制备选择性高、催化性能稳定的高活性超低Pt催化剂NiCo@Pt/C.通过红外光谱和热失重对产品进行结构表征,结果表明:实验制备出Pt为壳Co和Ni为核的核壳型催化剂.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints. In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality （LMI） constraints online. Compared with the algorithm with a nonsaturated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality. This model predictive control （MPC） algorithm is applied to an industrial continuous stirred tank reactor （CSTR） with explicit input constraint. The simulation results demonstrate that the presented algorithm is effective.

Multi-domain modeling and simulation of proportional solenoid valve

A multi-domain nonlinear dynamic model of a proportional solenoid valve was presented.The electro-magnetic,mechanical and fluid subsystems of the valve were investigated,including their interactions.Governing equations of the valve were derived in the form of nonlinear state equations.By comparing the simulated and measured data,the simulation model is validated with a deviation less than 15%,which can be used for the structural design and control algorithm optimization of proportional solenoid valves.

Virtual Reality Based Process Integrated Simulation Platform in Refinery:Virtual Refinery and Its Application

With the combination between system simulation and virtual reality,we have established an integrated virtual refinery simulation platform,and analyzed the overall design and principal architecture.This paper introduces a simulation algorithm about a refinery based on virtual reality,and explains how the algorithm can be applied to the virtual refinery integrated simulation platform in detail.The virtual refinery simulation platform,which consists of a three-dimensional scene system,an integrated database system and a dynamic-static simulation system,has many applications,such as dynamic-static simulation of key process unit used as process control and oil tank blending simulation for scheduling.With the visualization and human-computer interaction for acquiring production and process data,this platform can provide effective supports on staff training related with monitoring,control and operation in refinery.Virtual refinery can also be web published through the internet and it is helpful for the distance training and education.

Dynamic failure in coal seams Implications of coal composition for bump susceptibility

As a contributing factor in the dynamic failure(bumping) of coal pillars,a bump-prone coal seam has been described as one that is ‘‘uncleated or poorly cleated,strong...that sustains high stresses."Despite extensive research regarding engineering controls to help reduce the risk for coal bumps,there is a paucity of research related to the properties of coal itself and how those properties might contribute to the mechanics of failures. Geographic distribution of reportable dynamic failure events reveals a highly localized clustering of incidents despite widespread mining activities. This suggests that unique,contributing geologic characteristics exist within these regions that are less prevalent elsewhere. To investigate a new approach for identifying coal characteristics that might lead to bumping,a principal component analysis(PCA) was performed on 306 coal records from the Pennsylvania State Coal Sample database to determine which characteristics were most closely linked with a positive history of reportable bumping. Selected material properties from the data records for coal samples were chosen as variables for the PCA and included petrographic,elemental,and molecular properties. Results of the PCA suggest a clear correlation between low organic sulfur content and the occurrence of dynamic failure,and a secondary correlation between volatile matter and dynamic failure phenomena. The ratio of volatile matter to sulfur in the samples shows strong correlation with bump-prone regions,with a minimum threshold value of approximately 20,while correlations determined for other petrographic and elemental variables were more ambiguous. Results suggest that the composition of the coal itself is directly linked to how likely a coal is to have experienced a reportable dynamic failure event. These compositional controls are distinct from other previously established engineering and geologic criteria and represent a missing piece to the bump prediction puzzle.

Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

Quality of service estimation based on maximum bottleneck algorithm for domain aggregation in backbone networks

This paper investigates the routing among autonomous systems (ASs) with quality of service (QoS) requirements. To avoid the intractability of the problem, abstract QoS capability must be informed among ASs, because the routhing which constrained QoS has been proved to be nondeterministic polynomial-time (NP) hard even inside an AS. This paper employs the modified Dijkstra algorithm to compute the maximum bottleneck bandwidth inside an AS. This approach lays a basis for the AS-level switching capability on which interdomain advertisement can be performed. Furthermore, the paper models the aggregated traffic in backbone network with fractional Brownian motion (FBM), and by integrating along the time axis in short intervals, a good estimation of the distribution of queue length in the next short intervals can be obtained. The proposed advertisement mechanism can be easily implemented with the current interdomain routing protocols. Numerical study indicates that the presented scheme is effective and feasible.

Effects of Temperature Gradient and Cooling Rate on the Formation of Methane Hydrates in Coarse Sand

Temperature gradient and cooling rate have an obvious effect on formation of methane hydrate. The process for formation of methane hydrate in coarse sand is monitored to tmderstand the relationship between temperature gradient and cooling rate and nucleation, growth and distribution of methane hydrate by using the electrical resistivity method. The results show that the change of resistivity can better reflect the nucleation and growth and distribution of methane hydrate. Temperature gradient promotes the nucleation, formation, and formation rate of methane hydrate. At a temperature gradient of 0.11℃/cm, the rate of methane hydrate formation and saturation reaches a maximum. Cooling rate has little effect on the methane hydrate formation process. Judging from the outcome of final spatial distribution of methane hydrate, the cooling rate has an obvious but irregular effect in coarse sand. The effect of tempera^re gradient on distribution of methane hydrate in coarse sand is less than that of cooling rate. At a temperature gradient of 0.07℃/cm, methane hydrate is distributed uniformly in the sample. If the temperature gradient is higher or lower than this value, the hydrate is enriched in the upper layer of sample.

A practical predictive control algorithm for integral processes and a case study in boiler level system

The Dynamic Matrix Control （DMC） algorithm tor integral processes is investigated in this paper. The reason why the original DMC algorithm cannot be applied to these processes is analyzed. The shifting matrix is transformed into another form and the corresponding theorem is proved, then its applicable range is extended. Compared with other algorithms on the integral processes, this algorithm is more practical and simple to implement. Simulation results also prove its validity. Applying this algorithm, we succeed in the control of the boiler level system in power units.

Geospacial information utilized under the access control strategy

This paper introduces a solution to the secure requirement for digital rights management （DRM） by the way of geospacial access control named geospacial access control （GeoAC） in geospacial field. The issues of authorization for geospacial DRM are concentrated on. To geospacial DRM, one aspect is the declaration and enforcement of access rights, based on geographic aspects. To the approbation of digital geographic content, it is important to adopt online access to geodata through a special data infrastructure （SDI）. This results in the interoperability requirements on three different levels： data model level, service level and access control level. The interaction between the data model and service level can be obtained by criterions of the open geospacial consortium （OGC）, and the interaction of the access control level may be reached by declaring and enforcing access restrictions in GeoAC. Then an archetype enforcement based on GeoAC is elucidated. As one aspect of performing usage rights, the execution of access restrictions as an extension to a regular SDI is illuminated.