浅谈长线路对交流控制回路的影响及应对措施

本文就长线路控制电缆电容电流和线路电压损失对交流控制回路的影响进行分析,并提出解决方法。交流控制回路中,电缆电容电流和线路电压损失的存在是不可避免的,应综合分析各方面的影响因素,采用合适的应对措施,从消除它们对交流控制回路的影响,确保控制回路的安全运行。     

交流控制回路中控制电缆允许长度的确定

对交流控制回路中决定控制电缆长度的主要原因进行了分析,并通过有关计算,得出控制电缆的最大允许长度,供设计时参考。     

电缆分布电容与继电器误动作

通过工程项目建设实例说明交流电对信号电缆所产生的分布电容会引起信号继电器误动作.分析分布电容计算的公式,阐述分布电容与电缆材料、长度等的关系,通过实例计算信号继电器的技术规格,给出抗分布电容的方案.最后介绍实际使用情况与思考意见.     

交流控制回路中控制电缆允许长度的确定

分析交流控制回路中决定控制电缆长度的主要因素,并计算出控制电缆的最大允许长度     

高压分压器分布电容补偿的研究

电阻分压器的测量精度和响应时间受自身分布电容的影响较大,尤其是高压分压器。由于高压分压器本身高压臂和低压臂的阻值均比较大,分布电感影响较小,分布电容对测量精度的影响将占据主导地位。为此,本文提出在高压臂两端串联均压环的方法均衡分压器高压侧电场分布,提高分压器性能。并且通过对分压器传递函数的相频和幅频特性进行理论分析,提出一种分布电容补偿的方法。试验证明这种补偿方法是正确的,满足工程要求。     

电缆电容电流过大引起的回路跳闸问题的研究

文章概述了莱变电站由于电缆过长引起的电容电流过大问题,造成变电站误跳闸,经过分析采用了有效的方案解决了存在的问题,提出对于有较长电缆连接跳闸回路的变电站必须注意选用合理的方案,注意避免类似问题再次发生.     

液位控制对聚丙烯装置长周期运行的影响

通过对聚丙烯装置近5年停车情况进行的分析，找出了聚丙烯装置停车的主要原因，通过对液相反应器液位测量方式的讨论，指出了现行液位测量与控制上的缺点及偏差；试图从液相反应釜搅拌电流来估算合适的液位区间，即最佳液位。并提出了改进液位测量与控制的技术措施和操作指导。     

自吸泵控制回路的改进

针对使用离心泵时产生的问题,基于离心泵自身的使用特点,结合使用环境,对其控制回路进行了改进,实现了离心泵的保护,延长了检修周期。     

电缆电容对模拟控制电路的影响

总结了滨化集团有限责任公司一次电流波动故障的发生经过及处理过程,探讨了电缆电容对模拟控制电路的影响.     

Robust distributed economic model predictive control based on differential dissipativity

In this article, a robust distributed economic model predictive control (DEMPC) approach is developed for plant-wide chemical processes. The proposed approach achieves arbitrary feasible setpoints that may vary frequently, attenuates the plant-wide effects of unknown disturbances and minimizes a plant-wide economic cost. In this approach, a plant-wide process is represented as a network of process units and each process unit is controlled by an individual controller which shares a plant-wide optimization economic objective and stability conditions through the network. To ensure the convergence of process variables to arbitrary setpoints, a contraction condition is developed for the DEMPC, based on the contraction theory. To deal with the effects of interactions among process units, the concept of dissipativity is adopted. Using sum-separable control contraction metrics, a reference-independent robust stability condition is developed to ensure the plant-wide disturbance effects (under interactions among process units) to be attenuated in terms of differential ℒ₂ gain and represented by a plant-wide differential dissipativity condition, which is converted into the differential dissipativity conditions that individual controllers need to satisfy. This approach facilitates the optimization of plant-wide economic costs with global constraints in a distributed way, allowing efficient implementation of alternating direction method of multipliers (ADMM). The proposed approach is illustrated using a reactor-separator process.     

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one‐directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi‐directional communication strategy, are evaluated in parallel and iterate to improve closed‐loop performance. In the design of the distributed model predictive controllers, Lyapunov‐based model predictive control techniques are used. To ensure the stability of the closed‐loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov‐based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed‐loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Dissipativity‐based distributed model predictive control with low rate communication

Distributed or networked model predictive control (MPC) can provide a computationally efficient approach that achieves high levels of performance for plantwide control, where the interactions between processes can be determined from the information exchanged among controllers. Distributed controllers may exchange information at a lower rate to reduce the communication burden. A dissipativity‐based analysis is developed to study the effects of low communication rates on plantwide control performance and stability. A distributed dissipativity‐based MPC design approach is also developed to guarantee the plantwide stability and minimum plantwide performance with low communication rates. These results are illustrated by a case study of a reactor‐distillation column network. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3288–3303, 2015     

Optimal temperature policies by distributed control for reactors with lhhw catalyst deactivation

An approximate procedure for solving the problem of temperature optimal control distributed in both space and time coordinates is presented. It is applied to chemical reactors suffering from catalyst decay whose model is described by a complex second order partial differential equation deduced from LHHW kinetics. The proposed algorithm uses a finite-difference approximation method to solve the state equation, and the control vector parametrization technique to obtain the optimal control. Numerical examples are computed and the results obtained show that distributed control reaches significantly higher production than lumped control.     

基于分布式偏差的加热炉支路温度一致控制

针对现有集中式温度支路平衡控制存在的问题,提出基于分布式偏差的支路温度一致性控制方案：仅利用相邻支路的温度信息,将各支路与相邻支路的温度偏差作为控制输入,使加热炉所有支路的温度达到一致的同时,确保调节过程中总进料流量不变。由于仅利用了相邻支路的温度信息,使得所提分布式偏差控制方案在大规模支路情形下更显优势。最后仿真实例表明了该分布式控制方案的有效性与可行性。     

Subsystem decomposition of process networks for simultaneous distributed state estimation and control

An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach. © 2018 American Institute of Chemical Engineers AIChE J, 65: 904–914, 2019     

熔体直纺生产过程中的新型分散控制系统

介绍了由PC和PLC构成的新型分散控制系统,叙述了它在熔体输送控制系统的应用实例,给出了控制系统的硬件组成及软件实现方法。新型分散控制系统,使熔体输送的自动化水平有了很大的提高。能够自动、安全、稳定运行,同时也大大降低了施工和安装费用。     

Machine learning-based distributed model predictive control of nonlinear processes

This work explores the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative DMPC systems are designed and analyzed with respect to closed-loop stability and performance properties. Extensive open-loop data within a desired operating region are used to develop long short-term memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov-based DMPC to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. Using a nonlinear chemical process network example, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed-loop performance is very close to the one of a centralized MPC system.     

阳极双电层电容对微生物燃料电池性能的影响

采用3种活性炭粉制备具有不同电容的阳极,研究了双电层电容阳极对单室空气阴极微生物燃料电池启动、运行、性能、阳极生物膜附着的影响。结果表明:当电极表面积相近的情况下,阳极双电层电容从0.0012 F增加到22.72 F时,微生物燃料电池启动时间缩短了68.0%,电池的最大功率密度增加了16.8倍,达到546.1 m W·m-2。扫描电子显微镜的结果表明高电容的阳极表面附着的微生物量比低电容电极的高1倍。因此,微生物燃料电池性能受阳极双电层电容的影响,而与阳极表面积的相关性小。