脱丁烷/脱异丁烷

应 用 用脱丁烷塔从天然汽油中分出丁烷,用脱异丁烷塔生产异丁烷(CiC_4)和正丁烷产品。采用非线性多变量控制和最佳化系统,既可生产出合格产品,又可降低公用工程消耗。控制策略 用多变量控制系统调节脱丁烷塔再沸器的蒸汽(或热油)流速和回流流速,以及调节脱异丁烷的进料流速和回流流速。控制的变量是脱丁烷塔底部汽油的雷特蒸汽压(Rvp)值、脱异丁烷塔底部最佳化的异丁烷(CiC_4)和顶部正丁烷的组成。当异丁烷销售价值高于正丁烷时,为了获得操作的最佳经济性,脱丁烷塔底部产物的雷特蒸汽压(Rvp)需维持在最大允许值,脱异丁烷塔底部异丁烷含量要最小,而顶部正丁烷含量要最大。脱丁烷塔顶部蒸汽用脱异丁烷塔底部产物     

茂名乙烯装置脱丁烷塔堵塞原因分析及对策

茂名2号乙烯装置脱丁烷塔长时间运行,进料中的不饱和烯烃组分发生聚合结垢堵塞塔盘,降低了脱丁烷塔的分离效果,导致脱丁烷塔塔釜中碳四损失升高.针对脱丁烷塔碳四损失严重的问题,利用闲置的2号裂解汽油加氢装置后脱戊烷塔对其进行了流程改造,将脱丁烷塔与后脱戊烷塔串联操作,脱丁烷塔塔釜的物料采出作为脱戊烷塔进料,进一步分离回收碳四...     

多变量预测控制技术在重芳烃分离塔控制中的应用

针对扬子石化公司某二甲苯分离装置中重芳烃分离塔进料波动大、操作变量多、关联性强、容量滞后大等特点,使用多变量预测控制软件APC-Hiecon,实现对重芳烃分离塔的平稳控制并提高分离效果,取得了很好的控制效果和显著的经济效益.     

PTA溶剂脱水塔先进控制与在线优化

从PTA溶剂脱水塔的工艺操作特点出发，运用RMPCT多变量预测控制软件，建立以塔顶与塔釜组分为被控变量，以塔釜温度回路设定值和回流量为操纵变量的溶剂脱水塔先进控制与在线优化系统．稳定溶剂脱水塔的操作，综合降低塔的酸耗和蒸汽消耗，取得满意的控制效果。     

氯乙烯精馏过程的先进控制系统

针对氯乙烯精馏过程存在的控制难点，使用多变量预测控制软件包APC-Hiecon设计了两个多变量预测控制器，并在低沸塔和高沸塔上应用，大大提高了生产操作平稳度和氯乙烯单体的纯度，取得良好的控制效果和显的经济效益。     

重整脱丁烷塔异常操作分析与处理

长岭分公司重整装置脱丁烷塔在首次三年一修的运行后期,操作弹性变小,操作参数难于控制,产品经常出现不合格现象,分析原因为氯化铵盐在塔内的累积增加,堵塞塔盘所致,通过对脱丁烷塔水洗后,提高了脱丁烷塔的分离效率,改善了操作情况,保证了产品质量。     

脱丁烷塔聚合发生的原因及应对措施

总结了镇海乙烯裂解装置脱丁烷塔的运行状况。通过对脱丁烷塔塔底聚合物进行实验室分析,确认了脱丁烷塔塔内聚合物的主要来源为戊二烯、苯乙烯等烯烃的聚合。通过采取优化工艺操作、提高检修质量、控制进料水含量、在脱丁烷塔加注阻聚剂等措施后,脱丁烷塔塔底聚合问题得到了缓解,从而保证了装置的长周期运行。     

Multi-variable Predictive Control Technology for Aromatics Benzene Tower Control System

介绍了多变量预测控制技术在兰州石化公司芳烃抽提先进控制系统中的应用,对利用多变量预测控制技术控制精馏单元苯塔灵敏板温度、塔顶温差、回流罐液位的策略和方案做了研究,评估了多变量预测控制技术在先进控制系统中的应用效果和产生的经济效益.     

多变量预测控制技术在芳烃苯塔先进控制中的应用

介绍了多变量预测控制技术在兰州石化公司芳烃抽提先进控制系统中的应用,对利用多变量预测控制技术控制精馏单元苯塔灵敏板温度、塔顶温差、回流罐液位的策略和方案做了研究,评估了多变量预测控制技术在先进控制系统中的应用效果和产生的经济效益。     

优化导热油炉供热流程——提高轻烃回收率和产品质量

本文主要是针对目前胜利油田河口采油厂压气站导热油(有机热载体)供热工艺流程布局,不能够满足脱乙烷塔和脱丁烷塔(即液化气塔)温度控制要求,形成塔釜温度波动变化较大,严重影响轻烃产品质量和产量。通过借鉴国内外导热油(有机热载体)供热流程和控制的发展趋势,提出对本单位的导热油(有机热载体)供热的改造方案,实现增强脱乙烷塔和脱丁烷塔的分离效果,提高产品收率和产品质量。     

化工过程非稳态开工的缓冲升温修正切换控制

化工过程的开工大多表现为一个升温过程。为满足升温过程的快速性而又不失稳定性的要求,有学者提出将时间最优Bang-Bang控制与其他控制方法结合来控制升温过程,但由于Bang-Bang控制对切换次数和切换点要求严格,致使其在实际应用中不够理想。在Bang-Bang组合控制的基础上引入缓冲升温控制,将整个控制系统分为4部分:全幅升温、全幅降温、缓冲升温、PID控制。将温度变化率作为缓冲升温与PID控制的切换变量,将修正切换控制问题等价为非线性规划问题,优化选取全幅升温、全幅降温、缓冲升温最优切换时间点。实例对比分析表明,该切换开工控制方案不仅避免了因切换次数与切换点选取不当导致的不良问题,且超调小,稳定快。     

面向可持续发展的半导体制造的集成产品和过程控制

Semiconductor fabrication is a manufacturing sequence with hundreds of sophisticated unit operations and it is always challenged by strategy development for ensuring the yield of defect-free products. In this paper, an advanced control strategy through integrating product and process control is established. The proposed multiscale scheme contains three layers for coordinated equipment control, process control and product quality control. In the upper layer, online control performance assessment is applied to reduce the quality variation and maximize the overall product performance (OPP). It serves as supervisory control to update the recipe of the process controller in the middle layer. The process controller is designed as an exponentially weighted moving average (EWMA) run-to-run controller to reject disturbances, such as process shift, drift and tool worn out, that are exerted to the op-eration. The equipment in the process is individually controlled to maintain its optimal operational status and maximize the overall equipment effectiveness (OEE), based on the set point given by the process controller. The ef-ficacy of the proposed integrated control scheme is demonstrated through case studies, where both the OPP (for product) and the OEE (for equipment) are enhanced.     

Generic model control — A case study

This paper outlines the use of a process model directly in a control algorithm. The process considered, a forced circulation single-stage evaporator, is a nonlinear interacting process. The control strategy employing a process model derived from fundamental mass and energy balances is shown to outperform single loop and predictive control strategies by a significant amount. The control structure is first presented in general form and then specifically applied to this process.     

Two-degree-of-freedom output feedback controllers for nonlinear processes

In this work, a nonlinear output feedback control algorithm is proposed, in the spirit of model-state feedback control. The structure provides state estimates using a process model, the measured output, and the residual between the model output and the measured output. These estimates will track the process states at a rate determined by a set of tunable parameters. An algebraic transformation of the state estimates is incorporated in the control structure to ensure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The transformed states are then used in the control law. This leads to a controller of minimal order possessing integral action. The control structure is shown to have the same properties as the standard model-state feedback structure. The resulting algorithm is a two-degree of freedom control law, in the sense that the control action is not a function of the error only, but the output and the set point are processed in different ways. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to demonstrate the closed-loop performance of the proposed method.     

Suboptimal control through model reduction

There has been a growing interest in control strategies that optimize a given performance criterion to improve process efficiencies. Optimal control techniques, such as LQP, have been available but did not find much acceptance in process industries because of certain problems that are involved in their application. In this paper an approach is presented which alleviates these problems. Simulation results show that the proposed approach works well for servo and regulatory control problems.     

GENERIC MODEL ADAPTIVE CONTROL

Generic Model Control (GMC) is a process model based control algorithm incorporating a process model directly within the control structure. It has been shown to produce excellent control, despite reasonable modelling errors. In this paper an algorithm is developed within a GMC framework which reduces the effect of larger modelling errors by regularly updating the model parameters. This new adaptive algorithm is capable of adapting model parameters in a nonlinear model, where the parameters appear in a nonlinear manner. Several examples are presented to illustrate the principles of the technique.     

A simple adaptive control strategy for temperature trajectory tracking in batch processes

In this paper, a simple adaptive control strategy is suggested for temperature tracking control of batch processes. A nonlinear controller, which is in structure very simple and consists of a single parameter, is proposed. To enable this controller to control a batch process adaptively, a simple parameter tuning algorithm is derived based on the Lyapunov stability theorem. The proposed adaptive control scheme is directly operational, which does not depend on process model and the only a priori process information required is the system response direction. To demonstrate the effectiveness and applicability of the proposed scheme, illustrative examples are provided. Extensive simulation results reveal that the proposed adaptive control strategy appears to be a simple and effective approach to batch process control, which provides robust control despite the wide range of operating conditions and nonlinear dynamics of the system.     

调节阀分程调节的实现

在生产过程控制中,为了扩大调节阀的可调范围,或者满足某些工艺操作的特殊需要,通常采用分程控制.这里介绍了分程控制的分类及实现方法,供大家参考.     

模糊非线性内模控制算法及其在pH值控制中的应用

pH值控制过程具有较强的非线性,历来是过程控制研究的一大热点,本文针对pH值控制系统提出了一种基于模糊推理网的非线性内模控制算法（FNIMC）。模糊推理网用于辨识对象的模糊模型;FNIMC由一个逆模控制器和具有一个可调参数的鲁棒滤波器组成。仿真结果表明该算法优于非线性PID调节器,且计算效率高。     

A time scale-bridging approach for integrating production scheduling and process control

In this paper, we propose a novel framework for integrating scheduling and nonlinear control of continuous processes. We introduce the time scale-bridging model (SBM) as an explicit, low-order representation of the closed-loop input–output dynamics of the process. The SBM then represents the process dynamics in a scheduling framework geared towards calculating the optimal time-varying setpoint vector for the process control system. The proposed framework accounts for process dynamics at the scheduling stage, while maintaining closed-loop stability and disturbance rejection properties via feedback control during the production cycle. Using two case studies, a CSTR and a polymerization reactor, we show that SBM-based scheduling has significant computational advantages compared to existing integrated scheduling and control formulations. Moreover, we show that the economic performance of our framework is comparable to that of existing approaches when a perfect process model is available, with the added benefit of superior robustness to plant-model mismatch.