“全过程预控”在安全管理中的应用

安全管理工作必须做到事前预测、事中控制、事后完善提升。其中事中控制要求我们不断创新安全管理体制和安全管理模式,构建以安全为重点的全员全过程管理新体制,针对生产实际情况,坚持"安全第一,预防为主"的思想不动摇,时刻遵循"安全管理超前工作,安全工作超前管理"的安全工作理念,坚持超前管理超前预控,保持持续安全的态势。

小批量生产过程的质量控制

在小批量生产过程中,由于样本量少,无法采用常规的休哈特控制图进行控制。在分析现实中小批量生产过程质量控制思路的基础上,提出一种新型的控制方法——单值回归预控法,这是基于预控思想的加严控制。通过MATLAB仿真分析,发现该方法既实现了小批量生产过程的质量控制,又降低了误判的概率。

电力可靠性预控管理系统的开发与应用

介绍金华电业局电力可靠性预控管理系统的开发与应用情况。该局电力生产工作以电力可靠性为中心,可靠性管理工作以目标管理和停电全过程预控为核心,将电力可靠性的事后统计分析管理转变为事前制定、事中控制、事后统计分析的信息化网络管理,提升了电力可靠性管理工作的管理水平和工作效率,提高了企业的经济效益和社会效益。

基于风险预控的矿山安全监控系统研究

通过建立基于风险预控的矿山安全监控系统,可以有效的指导矿山安全管理工作、使矿山安全管理由静态转化为动态、化被动为主动,实现安全管控的超前性、有效性。本文在风险预控管理理论的基础上,阐述了矿山安全风险预控的技术过程,以计算机为载体分析了构建软件系统的硬件要求及技术支持,研究了系统的构架,针对瓦屋磷矿进行实证研究,取得了一定成效,对其他矿山安全预控管理具有借鉴意义。

安全风险预控管理与标准化作业融合研究

在借鉴国内能源企业风险预控管理先进经验的基础上,结合准能集团近十年风险预控管理系统建设的实践,面对准能集团实际,需要在铁路、电厂、化工及辅助生产单位实施岗位标准化作业的需求,对风险预控管理及岗位标准化作业流程在可行性、融合思路、系统建设等方面的阐述。

BIM技术在群塔作业管控中的应用探索

在大体量、大规模工程项目的群塔施工过程中,爬升吊装、回转碰撞、群塔拆装等一系列具体操作将引发诸多难以预测的情况。本篇就大连恒隆广场项目现场12台塔吊进行的计算机建模预控管理成功经验,对BIM技术在群塔工程中的作业管控应用进行了深入的探讨与总结。

做好建设工程项目监理的体会

作者结合建设工程项目现场施工阶段的监理实践,就如何做好建设工程项目监理工作,从思想认识、目标措施到团队建设几个方面做了有益的交流。

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

Process control technology of low NO_x sintering based on coke pretreatment

Process control is an effective approach to reduce the NOx emission from sintering flue gas.The effects of different materials adhered on coke breeze on NOx emission characteristics and sintering performance were studied.Results showed that the coke breeze adhered with the mixture of CaO and Fe2O3 or calcium ferrite significantly lowers the NOx emission concentration and conversion ratio of fuel-N to NOx.Pretreating the coke with the mixture of lime slurry and iron ore fines helped to improve the granulation effect,and optimize the carbon distribution in granules.When the mass ratio of coke breeze,quick lime,water and iron ore fines was 2:1:1:1,the average NOx emission concentration was decreased from 220 mg/m3 to 166 mg/m3,and the conversion ratio of fuel-N was reduced from 54.2%to 40.9%.

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.

Assessment of Model Predictive Control Performance Criteria

The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.

Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.

Stable MIMO Constrained Predictive Control with Steadystate Objective Optimization

A two-stage multi-objective optimization model-predictive control algorithms(MPC) strategy is presented. A domain MPC controller with input constraints is used to increase freedom for steady-state objective and enhance stabilization of the controller. A steady-state objective optimization algorithm oriented to transient process is adopted to realize optimization of objectives else than dynamic control. It is proved that the stabilization for both dynamic control and steady-state objective optimization can be guaranteed. The theoretical results are demonstrated and discussed using a distillation tower as the model. Theoretical analysis and simulation results show that this control strategy is efficient and provides a good strategic solution to practical process control.

Analysis and Control of Surface Delamination Defects During Milling of Orthogonal Aramid Fiber‑Reinforced Composites Laminates

The aramid fiber-reinforced composites(AFRC)can increase the durability of corresponding applications such as aerospace,automobile and other large structural parts,due to the improvement in hardness,heat build-up,wear properties and green environmental protection.However,because of its complex multiphase structure and unique heterogeneity and anisotropy,the poor compression fatigue resistance and the incident surface fibrillation are inevitable.To improve the assembly precision of AFRC,mechanical processing is necessary to meet the dimensional accuracy.This paper focuses on the influence of contour milling parameters on delamination defects during milling of AFRC laminates.A series of milling experiments are conducted and two different kinds of delamination defects including tearing delamination and uncut-off delamination are investigated.A computing method and model based on brittle fracture for the two different types of delamination are established.The results can be used for explaining the mechanism and regularity of delamination defects.The control strategy of delamination defects and evaluation method of finished surface integrity are further discussed.The results are meaningful to optimize cutting parameters,and provide a clear understanding of surface defects control.

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.

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

Conventional and Predictive Control Algorithms for Controlling Nonlinear Processes Using Multiple-Model Approach

The objective of this work is to formulate and demonstrate the methodology of multi-models for improving the performance of existing advanced control strategies. Multiple models are used to capture the nonlinear process dynamics relating to gain and time constant variations. The multi-model strategy was implemented on several controllers such as Smith-Predictor using PI （Proportional-lntegral） and GPC （Generalized Predictive Control）. Computer simulations and experiments were conducted on several nonlinear systems and compared to the original form of these controllers. The enhanced approach was tested on controlling the screw speed of an injection molding machine and temperature of a steel cylinder.