炼焦炉鼓风机调速系统的多模态智能控制

以鼓风机调速系统为核心的炼焦炉集气管压力控制系统是一个工况复杂多变、大滞后、强耦合、非线性且频发强干扰的工业系统.从炼焦工况复杂多变的角度设计一套基于切换的多模态智能控制策略：在正常工况下,采用RBFNN逆模型控制＋PI控制＋Smith预估的复合控制;在非正常工况（如推焦、装煤等强干扰操作）下,采用PI控制＋Smith预估的复合控制,并在线对PI控制器进行参数自整定.仿真结果表明：该智能控制策略提高了快速性、稳定性,增强了对工况复杂多变情况的适应性和对强干扰的抑制能力,保证了荒煤气的稳定传输.     

基于减法聚类的焦炉集气管压力操作模式提取及迁移重构

基于集气管压力控制特点和生产过程中产生的大量历史数据和以往操作经验的基础上，提出一种基于减法聚类的焦炉集气管压力控制的操作模式提取及迁移重构方法。基于减法聚类算法进行操作模式的提取，最终形成操作模式优化数据库，对压力设定值进行优化，采用基于模型迁移思想的模式重构策略实现操作模式的修正。工业应用试验验证了所提方法的有效性。     

焦炉集气管压力的优化控制

介绍了焦炉集气管压力的优化控制方案及控制算法。该方案既可稳定焦炉集气管的压力和延长焦炉寿命，又能达到节能降耗的目的。     

协调控制技术在焦炉集气管压力调节中的应用

在对影响焦炉集气管压力的工艺因素进行分析和研究的基础上,结合焦炉煤气的流体特性和焦炉工艺特点,开发出一套随动协调自动控制系统软件,保证焦炉集气管压力长期保持高精度稳定运行。     

焦炉集气管压力的复合控制

集气管压力的稳定影响焦炭的质量和焦炉的寿命 ,通过分析焦炉集气管压力影响因素及其耦合关系 ,采用PID控制、解耦控制、专家控制来进行集气管压力的控制。应用结果表明 ,集气管压力系统的抗干扰能力得以提高 ,其波动现象得到有效控制     

Explicit distributed model predictive control design for chemical processes under constraints and uncertainty

In multivariable industrial processes, the common distributed model predictive control strategy is usually unable to deal with complex large-scale systems efficiently, especially under system constraints and high control performance requirements. Based on this situation, we use the distributed idea to divide the large-scale system into multiple subsystems and transform them into the state space form. Combined with the output tracking error term, we build an extended non-minimal state space model that includes output error and measured output and input. When dealing with system constraints, the new constraint matrix is divided into range and kernel space by using the explicit model predictive control algorithm, which reduces the difficulty of solving constraints in the extended system and further improves the overall control performance of the system. Finally, taking the coke furnace pressure control system as an example, the proposed algorithm is compared with the conventional distributed model predictive control algorithm using non-minimal state space, and the simulation results show the feasibility and superiority of this method.     

焦炉加热过程模糊复合智能控制系统模型研究

采用"间歇加热控制"与加热煤气流量调节相结合的控制原理,利用模糊复合控制建立焦炉加热智能控制策略和模型.该控制策略采用前馈、反馈和模糊智能控制相结合,并应用结焦指数CI控制焦炉的炼焦过程.根据智能控制策略,设计了焦炉加热模糊智能控制的结构和模型.根据实际需要和人工经验,设计了焦炉加热的模糊控制器.仿真结果表明该系统能够稳定焦炉的炉温,具有很大的实用价值.     

基于解耦控制的焦炉集气管压力控制系统

针对焦炉集气管压力系统具有多回路、强耦合、非线性的特点 ,提出了一种运用相关性分析法的解耦控制算法 ,并将该算法与变积分PI控制有机结合 ,解决了具有相互耦合的多座焦炉的集气管压力稳定问题。实际运行结果表明该方法简单实用 ,可靠有效。     

多焦炉集气管压力平衡控制

介绍了贵阳煤气气源厂3#焦炉投产以后，在3座焦炉满负荷运行时，对集气管压力调节造成的影响，尤其详细介绍了通过连通平衡管的调节，解决集气管压力不稳定的问题及产生的效果。     

Modeling and nonlinear predictive functional control of liquid level in a coke fractionation tower

The level control of the fractionation tower in industrial coke unit is not very easy due to its complex characteristics and nonlinearity. Most are controlled by PID or linear predictive control methods without considering the complexity. This paper shows that a more comprehensive nonlinear-model-based predictive control method can further improve control performance. A verification of a nonlinear process model with plant data is first shown. Then the design of nonlinear predictive functional control is discussed and results are shown by way of simulations and application to demonstrate the effectiveness and feasibility of the proposed control strategy.     

基于神经网络的pH中和过程非线性预测控制

针对pH中和过程这一化工过程系统中的典型非线性对象特点，应用神经网络建模思想和模型预测控制方法，并结合Hammerstein模型特点，研究pH中和过程非线性系统的两种新型模型预测控制手段，分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略，并用MATLAB对其进行仿真。控制仿真结果表明，建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法，具有良好的设定值跟踪效果和抗干扰控制响应，说明这两种控制策略是非线性过程的有效控制方法。     

焦粉成型造气技术应用

介绍了焦粉成型技术及其在造气工艺生产中的应用情况,实践证明,将焦化厂的廉价焦粉成型,用于掺烧制气,其焦粉成型率为95.18%,半水煤气成分满足工艺指标,1.18t成型焦粉可替代1t焦粒,炉底及上行集尘器下灰量增多,且经济上年可降成本158万元。     

焦炉生产的智能控制与管理系统研究

为了提高焦炉的控制与管理水平,对焦炉生产的控管一体化进行了研究.首先建立了焦炉生产的控管一体化系统模型,然后研究了焦炉生产的加热控制和生产计划与调度.采用"间歇加热控制"与加热煤气流量调节相结合的控制方法,利用模糊复合控制建立焦炉加热智能控制策略和模型.根据智能控制策略,设计了焦炉加热模糊智能控制的结构和模型.应用动态规划和遗传算法对焦炉生产计划与调度进行了优化研究.最后将此系统应用于生产实际,实践运行表明:该系统能够实现焦炉生产的智能控制与管理,有效地提高了焦炭质量和降低了能耗.稳定了焦炉生产,具有很大的实用价值.     

50万吨／年延迟焦化装置

本装置以减压渣油为原料，生产气体，汽油，柴油，蜡油与焦炭。采用－炉两塔，无堵焦阀开工和预热工艺流程；引进全井架水力除焦控制系统及除焦控制 ；以计算机集散系统进行控制。     

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     

高纯度精馏过程的集成控制与在线优化策略

For high-purity distillation processes, it is difficult to achieve a good direct product quality control using traditional pro-portional-integral-differential (PID) control or multivariable predictive control technique due to some difficulties, such as long re-sponse time, many un-measurable disturbances, and the reliability and precision issues of product quality soft-sensors. In this paper, based on the first principle analysis and dynamic simulation of a distillation process, a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable. Correspondingly, a new strategy with integrated control and on-line optimization is developed, which consists of model predictive control of the split ratio, surrogate model based on radial basis function neural network for optimization, and modified differential evolution optimization algorithm. With the strategy, the process achieves its steady state quickly, so more profit can be obtained. The proposed strategy has been successfully applied to a gas separation plant for more than three years, which shows that the strategy is feasible and effective.     

焦炉鼓冷系统建模与GPC控制器设计

焦炉鼓冷系统由集气管、鼓风机、初冷器和鼓风机调速装置组成,主要功能是调节鼓风机转速,稳定初冷器前吸力和集气管压力,实现焦炉煤气稳定传输。建立2种工况模型,采用不同参数进行GPC控制。结果表明,该控制系统抑制扰动能力较强,鲁棒性好,对焦炉鼓冷系统的控制取得满意效果。     

焦炉煤气制甲醇工艺中影响甲醇产量的因素探析

分析了焦炉煤气制甲醇工艺中影响甲醇产量的多种因素,如反应原料气量、气体成分、合成系统压力及反应温度等。针对这些因素进行了技术改造,提出了提高煤气量及氧气量,保证气体净化效果防止催化剂中毒、防止煤气中的焦油等杂质带入催化剂槽中增加系统阻力、降低转化系统中CH4含量,以及适当提温,在确保合成催化剂低温活性的前提下提高CO合成率等有效控制措施。     

基于多属性性能评估的焦炉加热燃烧过程在线优化控制方法

针对焦炉加热燃烧过程中控制器参数难以适应由加热煤气热值和结焦时间变化等因素引起的火道温度波动的问题, 设计了一种基于多属性性能评估的焦炉加热燃烧过程优化控制方法。首先通过分析焦炉加热燃烧过程的工艺特点及生产需求, 针对过程参数周期差异较大的特点, 提出了基于信息熵的多属性性能评估模型, 实现控制系统的在线性能评估。针对控制系统性能评估不合格的情况, 建立了以火道温度偏差、偏差变化率和调节时间为目标的多目标优化模型, 并采用差分进化算法进行求解, 通过控制器参数的在线调节, 保证焦炉火道温度的稳定。仿真结果表明该优化控制方法在加热煤气热值和结焦时间变化时能较好地抑制火道温度的波动。     

Multistage nonlinear model predictive control for pumping treatment in hydraulic fracturing

Hydraulic fracturing has gained increasing attention as it allows the constrained natural gas and crude oil to flow out of low-permeability shale formations and significantly increase production. Perilous operating states of extremely high pressure also raise some safety concerns, requiring us to formulate an appropriate dynamic model, and provide a careful engineering control to ensure safe operating conditions. Moreover, uncertainties due to spatially varying rock properties increase the difficulties in control of the fracturing process. In this work, we formulate a first-principles model by considering the fracture evolution, mass transport of substances in the slurry, changing fluid properties, and the monitored operating pressure on the ground level. Next, we implement nonlinear model predictive control (NMPC) to control the process under a set of final requirements and process constraints. Our results show that the performance of standard NMPC degrades when the rock uncertainty causes the parameter mismatch between the process and the predictive model in the controller. With standard NMPC, designed with a nominal model, the process fails to meet the terminal requirements of fracture geometry, and pressure is violated in one of the parameter mismatch cases. Therefore, we resort to multistage NMPC, which considers uncertainty evolution in a scenario tree with separate control sequences to address constraint violations. We demonstrate that multistage NMPC presents good performance by showing constraint satisfaction whether the uncertain rock parameter realization is time-invariant or time-variant. We also simulate the process with multistage NMPC including different numbers of scenarios and compare their control performance. Our investigation demonstrates that multistage NMPC effectively manages parametric uncertainties attributed to non-homogeneous rock formation, and provides a promising control strategy for the hydraulic fracturing process.