PI型预测函数控制算法的研究及在重油焦化装置的应用

By extending the system's state variables,a novel predictive functional controller has been developed.The structure of this controller is similar to that of classical proportional integral (PI) optimal controller and includes a control block that can perform a feed-forward control of future P-step set points.It considers both the state variables and the output errors in its cost function,which results in enhanced control performance compared with traditional state space predictive functional control (TSSPFC) methods that consider only the predictive ontput errors.The predictive functional controller (PFC) has been compared with TSSPFC in terms of tracking ability,disturbance rejection,and also based on its application to heavy oil coking equipment.The results obtained show the effectiveness of the controller.     

全加氢型炼厂重油加工延迟焦化装置的设计

以按照100%俄罗斯原油设计的某炼厂为例,在设计工况的基础上测算了掺炼不同比例沙中油时,渣油加氢脱硫和催化裂化装置进料性质的变化情况。经测算,建设1套100万t/a的延迟焦化装置,当掺炼沙中油达到60%时,有利于关键装置长周期稳定运行,且使净利润由256.66元/t提高到282.26元/t。     

焦化加热炉出口温度的预测函数控制

采用透明控制结构,设计了焦化加热炉出口温度的预测函数控制系统.针对影响出口温度的可测扰动采用前馈控制加以补偿,实际工业应用表明该控制系统具有较好的跟踪性能和较强的鲁棒性.     

PH-98型催化剂用于大庆焦化柴油和重油催化轻柴油混合油加氢精制的研究

为解决大庆焦化柴油和重油催化轻柴油产品的安定性和对加氢装置扩能的需要,进行了FH-98型催化剂用于大庆焦化柴油和重油催化轻柴油混合油加氢精制工艺的研究。研究结果表明,FH-98型催化剂对大庆焦化和重油催化混合柴油进行加氢精制,可以生产出0^#一等品柴油,能满足加氢装置扩能的需要。     

CS-Ⅱ型喷嘴在MIP重油催化裂化装置的工业应用

介绍了CS-Ⅱ喷嘴在1.7Mt/a重油催化裂化装置的应用情况。工业试验结果表明,在原料、催化剂、操作条件不变的情况下,改善了产品分布。使用CS-Ⅱ喷嘴后液化气收率提高了1.3个百分点;汽油收率提高了1.1个百分点;柴油收率提高了0.3个百分点。干气收率降低了1.5个百分点;焦炭收率降低了1.2个百分点;对产物性质无不良影响。证明CS-Ⅱ喷嘴能有效地改善产品分布。     

DGJ-Ⅱ型自动顶盖机在齐鲁石化焦化装置中的首次应用

介绍了DG J-Ⅱ型自动顶盖机在胜利炼油厂焦化装置的首次应用情况及出现的部分问题。     

先进控制技术在延迟焦化装置的应用

以某厂延迟焦化装置为工业应用背景,在焦化反应机理模型的基础上,对控制目标、主要变量选择、约束条件、控制器结构等方面进行详细的分析,设计了延迟焦化装置的先进过程控制系统。工业实际应用表明：先进控制系统投运后,整个生产过程的平稳性和控制精度得到了明显的改善。     

低磁剂在重油催化裂化装置上的应用

在中国石油抚顺石油化工公司150万t/a重油催化裂化装置上进行了低磁剂的工业试验。结果表明:在原料油和操作条件不变的前提下,加入低磁剂后,产品分布基本不变,对装置生产操作和产品质量无不良影响;催化剂单耗下降,减少了废催化剂的排放,经济效益和社会效益显著。     

DM-5005金属钝化剂在重油催化裂化装置上的工业应用

中国石油抚顺石油化工公司在1 500kt/a重油催化裂化装置上进行了DM-5005金属钝化剂的工业应用。结果表明,在原料油和操作条件不变的前提下,加入钝化剂后,对装置生产操作和产品质量无不良影响;干气、焦炭、油浆产率分别下降0.57,0.63,0.69百分点;液化气、汽油、液收分别提高0.96,1.75,1.2百分点;创效约11 985万元/a。DM-5005金属钝化剂能使催化剂保持良好的活性和产品选择性。     

基于2次核SVM的单步非线性模型预测控制

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.     

Multi-rate observer design and optimal control to maximize productivity of an industry-scale fermentation process

Chemical industries focus primarily on profitable operations, resulting in growing attention and advances in the field of digital twins and optimal control algorithms. However, most industries still struggle due to a lack of physical sensors, infrequent measurements, and asynchronous sampling. Thus, in this work, we have designed a multi-rate state observer for state estimation from plant measurements and developed a model predictive controller (MPC) that maximized the profitability of an industry-scale fermentation process (fermenter volume < 378,500 L). Additionally, as the fermentation process is complex due to the use of microorganisms, which cannot be accurately captured using a first-principles model, we utilize a previously developed hybrid model in the proposed MPC formulation. The MPC uses a GAMS-MATLAB framework to determine the optimal input profiles while considering practical process constraints. It is shown using multiple datasets, that the MPC can increase productivity while also decreasing the plant operating cost.     

在不分隔的反应器中原位电生成活性氯氧化降解蒽醌染料

The purpose of this paper was to investigate the possibility of treating C. I. Reactive Blue 19 wastewater by electrochemical oxidation via electrogenerated active chlorine, using metallic oxide coatings （dimensional stable anode, DSA） as anode. The electrolysis for the simulated wastewater was conducted at a constant current. Absorbances at 592 nm and 255 nm were measured to follow the decolorization of the dye and the degradatin of its aromatic ring. After 4 h of electrolysis under the experimental conditions： current density of 15 A·m^-2, 0.2 mol·L^-1 NaCl, 0.1 mol·L^-1 Na2SO4, 0.1 mmol·L^-1 dye, initial pH=6.4 and T=30℃, 100% decolorization of the dye and about 45% degradation of its aromatic ring were achieved, while no obvious change of total organic carbon was observed. The experimental results suggest that the decolorization of the dye and degradation of its aromatic ring were directly affected by current density, temperature, concentrations of the dye and sodium chloride, while slightly affected by initial pH and sodium sulfate concentration; the decolorization of the dye and degradation of its aromatic ring followed pseudo-first-order kinetics; and indirect electrooxidation, using electrogenerated active chlorine, predominated in the electrochemical oxidation.     

模糊预测函数控制在水泥回转窑分解炉温控系统中的应用研究

通过对水泥回转窑系统中预分解炉温度控制回路的特性分析,提出一种基于模糊模型的增量型预测函数控制算法,根据生料流量的波动来修正相应的预测函数控制律,并以Hoenywell公司的Plantscape集散控制系统为平台,开发了实时控制软件,实际应用表明对存在大迟后和参数时变的水泥回转窑系统,该算法能达到良好的控制效果。     

On the tuning of predictive controllers: Impact of disturbances,constraints, and feedback structure

The impact of problem formulation modifications on predictive controller tuning is investigated. First, the proposed tuning method is shown to adapt to disturbance characteristic changes and thus, takes full economic advantage of the scenario. The second topic concerns point‐wise‐in‐time constraints and the impact of constraint infeasibility. Specifically, we shift the tuning question from selection of nonintuitive weighting matrix parameters to that of a few key parameters and results in a rather intuitive trade‐off between expected profit and expected constraint violations. Finally, we show that simple modifications will allow for the consideration of various feedback structures, including computational delay and partial state information. The overall conclusions of the work are that the results of the automated algorithm will help build an intuitive understating of the dynamics of the process and ultimately result in a higher level trade‐off between profit and constraint observance. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3473–3489, 2014     

Data‐driven model predictive quality control of batch processes

The problem of driving a batch process to a specified product quality using data‐driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this “missing data” problem by integrating a previously developed data‐driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed‐loop simulations of a nylon‐6,6 batch polymerization process with limited measurements. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2852–2861, 2013     

Process design and control optimization: A simultaneous approach by multi‐parametric programming

We present a framework for the application of design and control optimization via multi‐parametric programming through four case studies. We develop design dependent multi‐parametric model predictive controllers that are able to provide the optimal control actions as functions of the system state and the design of the process at hand, via our recently introduced PAROC framework (Pistikopoulos et al, Chem Eng Sci. 2015;136:115–138). The process and the design dependent explicit controllers undergo a mixed integer dynamic optimization (MIDO) step for the determination of the optimal design. The result of the MIDO is the optimal design of the process under optimal operation. We demonstrate the framework through case studies of a tank, a continuously stirred tank reactor, a binary distillation column and a residential cogeneration unit. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

Model predictive control with active learning under model uncertainty: Why,when, and how

Optimal control relies on a model, which is generally uncertain because of incomplete knowledge of the system and changes in the dynamics over time. Probing the system under closed‐loop control can reduce the model uncertainty through generating input‐output data that is more informative than the data generated from normal operation. This paper addresses the problem of model predictive control (MPC) with active learning, with a particular focus on how incorporating probing in the control action can reduce model uncertainty. We discuss some of the central theoretical questions in this problem, and demonstrate the potential of active learning for maintaining MPC performance in the presence of uncertainty in model parameters and structure. Simulation results show that active learning is particularly beneficial when a system undergoes abrupt changes (such as the sudden occurrence of a fault) that can compromise operational safety, reliability, and profitability. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3071–3081, 2018