基于高速公路梁板预制全过程施工控制点分析

预制梁板是高速公路桥梁上部结构常见形式,具有技术工艺成熟、施工质量稳定、流水施工方便等优点,在中国大中小型桥梁施工中获得了广泛运用,并随着国家新建、改扩建桥梁项目的日益增加,有力地促进了桥梁预制行业的发展。文章基于芜湖至黄山高速公路工程项目,研究了高速公路上部结构梁板预制施工管理与技术方面的主要施工控制点,以期该研究成果对类似项目快速部署实施具有一定指导和借鉴意义。

预制箱梁施工全过程中的信息化技术应用

随着社会的发展,高速铁路建设的实施,旨在为人们的出行提供方便,在一定程度上解决解决很多交通运输问题,具有输送能力强、安全性高、速度快等特点,在社会上得到了很大的关注,而作为高铁施工中的重要环节,预制箱梁发挥的作用极为关键,合理利用该技术,可在促进箱梁质量提高的同时,缩短施工工期。

谈T梁预制的心得与体会

主要介绍了T梁在建设过程中的施工技术,总结了钢筋的加工与安装、模板的安装与拆除、养生、张拉、压浆、封锚等一系列工艺的注意事项,以期为T梁的预制积累经验。

浅析桥梁建设中预应力混凝土T梁施工技术

随着我国科学技术的发展,一些高新技术逐渐被应用各种领域中。尤其在桥梁建设阶段应用了大量的新材料、新技术,预应力混凝土施工技术就是其中一种。预制T梁为目前最常见的模型,对于一些跨度较大的桥梁预制过程相对比较复杂,对预制采用原料、预制地点、模型的施工混凝土施工技术等方面要求相对严格,因此混凝土T桥梁施工技术受到了人们广泛关注。本文首先介绍了T梁施工的常见方法,然后深入分析了施工过程中各个步骤,对每个工序的要点进行了概括总结。希望本文的工作能为从事相关工作的人员提供一定的指导和帮助。

预应力混凝土梁施工中的应力损失分析及控制

分析预应力混凝土梁在预制过程中引起预应力损失的原因 。

客运专线64m双线节段拼装预应力混凝土简支箱梁拼装工艺与线形控制技术

新建铁路西安至成都铁路客运专线为设计时速250km/h的高速铁路,其对桥梁的线形、平顺性有极高的要求,而对位于半径8000m圆曲线上的64m节段拼装简支梁,其相对于传统简支箱梁具有大弦比的特点,且箱梁施工工序繁多,工艺复杂,则对梁体精准的线性控制成为箱梁施工的重难点之一,对此特以西成客专汉中汉江特大桥64m双线节段拼装预应力混凝土简支箱梁箱梁预制架设施工过程进行研究,总结箱梁在梁段预制、梁段架设、预应力施工等过程中的线形控制技术,为今后客运专线大跨度节段拼装简支梁施工线形控制提供技术支持。

预应力混凝土T梁裂缝产生的机理分析与控制

预应力混凝土T梁在我国高速公路桥梁中使用广泛,T梁结构出现裂缝将影响整体梁桥的使用功能。结合两个典型的工程实例,分析了T梁在预制过程中和在运营过程中发生裂缝的原因和控制措施。

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.

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.

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.

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.

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.

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%.

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.

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.

Study on the evaluation system for the coal safety management based on risk pre-control

The new type of risk management is process management. First, the hazardsources are identified before coal mine accidents occur, and then the pre-control measureand information monitoring method based on classifying the hidden hazard sources aregiven. Lastly, the risk pre-alarm and risk control method are confirmed, the managementstandard and management measure are used to eliminate the hidden hazard sources. Inthis study, an evaluation system is built to evaluate the result of risk management.

Prediction of preheating conditions for inclined laser assisted machining

For laser assisted machining,shape of preheating laser heat source is changed irregularly because of complexity of material shape.So,the preheating temperature should be controlled by adjusting the feed rate or the laser power.Thermal analyses of the laser assisted machining process for inclination planes were performed.By analyzing the obtained temperature profile,a proper feed rate control method was proposed according to the inclination angles.In addition,the temperature distribution of the cross section after feed rate control was predicted.The correlation equation between inclination angles and adjusted proper feed rate was proposed.The results of this analysis can be used to predict the preheating effect on workpiece and can be applied as a preheating temperature control method in laser assisted machining 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.

Decentralized closed-loop identification and controller design for a kind of cascade processes

A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm.

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.