面向产品质量的精馏过程自优化控制及其应用

为解决精馏塔生产过程中各种影响因素波动造成的灵敏板最优温度变化,利用中控技术APCSuite软件设计了基于大数据和专家控制的温度自动寻优与产品质量约束功能块,对精馏塔生产过程进行自优化控制。在保证产品质量合格的前提下,减少产品质量过剩的现象,实现增产、降耗的目的。同时,通过模型预测和PID控制对精馏塔温度、液位、压力等关键工艺指标进行稳定控制。该方法在某企业生产装置中应用后,关键工艺指标标准偏差较未实施之前降低67.37%以上;人工操作次数由原450次/5天,到正常时不需人工操作;提高灵敏板温度3.72℃;蒸汽单耗降幅为3.78%,产品收率增幅为0.49%。     

自复叠制冷系统降温过程组分浓度优化及控制策略

混合工质低温制冷系统存在能效比较低、降温速度慢的问题。为提高混合工质自复叠制冷系统的降温性能,减小系统工作能耗,采用遗传算法集成Aspen Plus进行混合工质组分浓度优化,给出了不同工况下最优循环组分的需求规律。模拟结果表明,随着温度的降低,高沸点组分的需求逐渐降低,低沸点组分的需求逐渐增加。据此提出了一种有效的组分浓度控制策略并进行实验验证,实验结果表明,以膨胀储气罐和控制阀联合作用的方法可以增加系统降温速度,减小压缩机总功耗,控制系统的开机压力。     

多变量解耦自抗扰控制在气体流量装置中的应用

针对气体流量装置实验管路流量、压力耦合系统,通过机理法和阶跃响应法建立了其数学模型,并利用自抗扰解耦控制算法实现其解耦控制,以保证气体流量计性能测试过程的稳定性和控制快速性。对于气体流量装置多变量系统,自抗扰控制算法将耦合以及所有的内部不确定性和外部扰动都归结到总扰动中,通过扩张状态观测器和控制律对总扰动进行估计和补偿,使原系统被解耦成两个单输入单输出的子系统并利用PD控制器完成控制。自抗扰控制算法使系统在实现解耦的同时既减弱算法对于模型的依赖,又提高了系统的鲁棒性。仿真和实验结果表明,与PID控制算法相比,自抗扰控制算法调节时间更快,解耦效果更好,对扰动的抑制效果更优,性能鲁棒性更强。     

全局优化搜索新算法--列队竞争算法(Ⅰ)解非线性和混合整数非线性规划问题

提出了一种称为列队竞争算法(LCA)的群体搜索算法,该算法在进化过程中始终保持着独立并行进化的家族,通过家族内部的生存竞争和家族间的地位竞争这两种不同的竞争方式,使群体快速进化到最优或接近最优的区域.根据家族的目标函数值大小排列成一个列队,按列队中家族地位的不同分配不同的搜索空间,使局部搜索与全局搜索达到均衡,同时,应用逐步收缩搜索空间技术加速收敛速度.数值计算表明,列队竞争算法的搜索效率优于遗传算法和模拟退火法等算法.     

间歇过程的批间自优化控制

针对间歇过程的实时优化问题,提出了一种基于最优性条件近似法的批间自优化控制策略。首先获取标称工作点的最优输入轨迹形态,将其参数化为少量决策变量,简化问题复杂度。然后根据参数化后的决策变量得到批间优化的最优性条件,并建立批次终端可测变量和最优性条件之间的回归模型,将其作为被控变量进行批间跟踪控制。对一个间歇反应器进行了仿真研究,结果表明方法能有效实现间歇过程的批间自优化控制。     

非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用

所有实际工业过程都包含一定程度的非线性,如pH中和过程由于其本身的强非线性是工业过程控制中具有挑战性的难题,但至今为止仍缺乏有效的非线性控制方法。将基于差分方程模型的模型预测控制策略(model predictive control,MPC)推广到包含一个静态非线性多项式函数和一个线性差分方程动态环节的非线性Hammerstein系统,详细描述了基于静态非线性多项式函数的最优控制作用求解方法,提出了一套新的非线性Hammerstein MPC 控制策略(nonlinear Hammerstein predictive control,NLHPC)。pH中和过程控制仿真和控制实验表明,NLHPC的控制结果好于工业上常用的非线性 PID(nonlinear PID,NL-PID)控制器。     

一类多变量系统的自抗扰非线性动态解耦控制

针对一类多变量系统控制中的耦合问题,提出了一种基于自抗扰技术的非线性动态解耦控制(ADRC)方法。该方法不依赖于系统的精确数学模型,分别在控制器耦合矩阵部分已知和未知的情形下,在局部静态解耦的基础上,将各子系统的模型摄动、外扰和包括输入变量相互作用在内的动态耦合视为各通道上的扰动总和,通过引入虚拟控制和状态量,设计扩张状态观测器(ESO)估计总扰动并进行反馈补偿,进而再对各解耦子对象分别设计非线性单输入单输出ADRC以保证闭环系统稳定。最后以蒸馏塔模型的过程控制仿真验证了该方法具有良好的动态解耦效果,对模型不确定性和外部扰动具有较好的鲁棒性和适应能力。     

一类非自衡化工过程的最优控制

对控制能量存在约束时一类模型不确定非自衡化工过程的最优控制问题作了探讨.首先针对控制对象的标称模型应用谱分解最小化一个包含跟踪误差和控制能量在内的积分平方性能指标,导出一个控制能量约束条件下的最优控制律;然后再针对模型不确定性应用谱分解最小化实际系统性能和标称性能在整个频段上的方差,得到一个鲁棒控制器的设计方法,可使系统的标称性能对模型误差具有最优的鲁棒性.     

化工过程的自优化控制:原理、发展与应用展望

自优化控制(SOC)是一种通过选择被控变量实现化工过程实时优化的控制系统设计方法,具有控制结构简单、优化效果好等优点,近年来得到了快速发展。详细阐述了SOC的基本工作原理及其在过程控制系统中的定位与作用,对其发展历程和研究现状进行了系统性的总结,主要包括:被控变量求解方法、变量筛选快速算法、可变约束集问题、间歇过程SOC方法和混合实时优化策略等内容。最后,全面回顾了现阶段SOC在化工过程中的应用案例,并从理论和实践角度对其未来发展方向进行展望。     

有约束过程动态优化问题的改进克隆选择算法

状态变量带约束的过程动态优化问题是化工系统工程的重要课题,有一定的难度。通过将其转换为等价的非线性规划后,可采用元启发式方法求解。人工免疫系统的克隆选择算法(CSA)简练易用,全局搜索性能良好,但局部寻优能力较弱,且无处理约束的机制。为此,拟引入免疫网络自学习算子,均匀设计方法,以及目标与约束分离的处理机制,构建改进的克隆选择算法(ICSA),并将其用于状态变量带约束的间歇反应器和乙醇生物反应器的动态优化等实例,效果良好。试验结果表明三种策略有效地改进了CSA的性能,使ICSA能以较少的计算代价搜索到较优的控制策略。     

Simultaneous process synthesis and control design under uncertainty: A worst‐case performance approach

A new methodology that includes process synthesis and control structure decisions for the optimal process and control design of dynamic systems under uncertainty is presented. The method integrates dynamic flexibility and dynamic feasibility in a single optimization formulation, thus, reducing the costs to assess the optimal design. A robust stability test is also included in the proposed method to ensure that the optimal design is stable in the presence of magnitude‐bounded perturbations. Since disturbances are treated as stochastic time‐discrete unmeasured inputs, the optimal process synthesis and control design specified by this method remains feasible and stable in the presence of the most critical realizations in the disturbances. The proposed methodology has been applied to simultaneously design and control a system of CSTRs and a ternary distillation column. A study on the computational costs associated with this method is presented and compared to that required by a dynamic optimization‐based scheme. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2497–2514, 2013     

Operational strategy and planning for raw natural gas refining complexes: Process modeling and global optimization

Optimal operational strategy and planning of a raw natural gas refining complex (RNGRC) is very challenging since it involves highly nonlinear processes, complex thermodynamics, blending, and utility systems. In this article, we first propose a superstructure integrating a utility system for the RNGRC, involving multiple gas feedstocks, and different product specifications. Then, we develop a large‐scale nonconvex mixed‐integer nonlinear programming (MINLP) optimization model. The model incorporates rigorous process models for input and output relations based on fundamentals of thermodynamics and unit operations and accurate models for utility systems. To reduce the noncovex items in the proposed MINLP model, equivalent reformulation techniques are introduced. Finally, the reformulated nonconvex MINLP model is solved to global optimality using state of the art deterministic global optimization approaches. The computational results demonstrate that a significant profit increase is achieved using the proposed approach compared to that from the real operation. © 2016 American Institute of Chemical Engineers AIChE J, 63: 652–668, 2017     

Decomposition strategy for the global optimization of flexible energy polygeneration systems

The optimal design and operation of flexible energy polygeneration systems using coal and biomass to coproduce power, liquid fuels, and chemicals are investigated. This problem is formulated as a multiperiod optimization problem, which is a potentially large‐scale nonconvex mixed‐integer nonlinear program (MINLP) and cannot be solved to global optimality by state‐of‐the‐art global optimization solvers, such as BARON, within a reasonable time. A duality‐based decomposition method, which can exploit the special structure of this problem, is applied. In this work, the decomposition method is enhanced by the introduction of additional dual information for faster convergence. The enhanced decomposition algorithm (EDA) guarantees to find an ε‐optimal solution in a finite time. The case study results show that the EDA achieves much faster convergence than both BARON and the original decomposition algorithm, and it solved the large‐scale nonconvex MINLPs to ε‐optimality in practical times. © 2011 American Institute of Chemical Engineers AIChE J, 58: 3080–3095, 2012     

不确定条件下的湿法炼锌除铜过程机会约束优化控制

除铜过程是湿法炼锌净化工艺中的重要步骤,受生产环境多变、矿源多样、机理复杂等因素的影响,除铜过程存在不确定性,影响生产的稳定性和可靠性。针对除铜过程中入口溶液流量、底流返回量和入口铜离子浓度的不确定性,造成出口铜离子浓度不稳定的问题,研究不确定条件下的除铜过程机会约束优化控制方法。首先分析了除铜过程的不确定性,利用统计学方法分析不确定参数的分布特性,引入了机会约束的思想,将不确定条件下的除铜过程优化问题建模为机会约束优化问题。然后采用可行域映射方法,将机会约束优化问题转化为非线性规划问题。最后,使用序列二次规划求解该非线性规划问题。Monte Carlo仿真验证了该方法的有效性,可以提高系统的鲁棒性。     

Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

Real‐time economic model predictive control of nonlinear process systems

Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. © 2014 American Institute of Chemical Engineers AIChE J, 61: 555–571, 2015     

Modeling and control of protein crystal shape and size in batch crystallization

In this work, the modeling and control of a batch crystallization process used to produce tetragonal hen egg white lysozyme crystals are studied. Two processes are considered, crystal nucleation and growth. Crystal nucleation rates are obtained from previous experiments. The growth of each crystal progresses via kinetic Monte Carlo simulations comprising of adsorption, desorption, and migration on the (110) and (101) faces. The expressions of the rate equations are similar to Durbin and Feher. To control the nucleation and growth of the protein crystals and produce a crystal population with desired shape and size, a model predictive control (MPC) strategy is implemented. Specifically, the steady‐state growth rates for the (110) and (101) faces are computed and their ratio is expressed in terms of the temperature and protein concentration via a nonlinear algebraic equation. The MPC method is shown to successfully regulate both the crystal size and shape distributions to different set‐point values. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2317–2327, 2013     

Proactive fault‐tolerant model predictive control

Fault‐tolerant control methods have been extensively researched over the last 10 years in the context of chemical process control applications, and provide a natural framework for integrating process monitoring and control aspects in a way that not only fault detection and isolation but also control system reconfiguration is achieved in the event of a process or actuator fault. But almost all the efforts are focused on the reactive fault‐tolerant control. As another way for fault‐tolerant control, proactive fault‐tolerant control has been a popular topic in the communication systems and aerospace control systems communities for the last 10 years. At this point, no work has been done on proactive fault‐tolerant control within the context of chemical process control. Motivated by this, a proactive fault‐tolerant Lyapunov‐based model predictive controller (LMPC) that can effectively deal with an incipient control actuator fault is proposed. This approach to proactive fault‐tolerant control combines the unique stability and robustness properties of LMPC as well as explicitly accounting for incipient control actuator faults in the formulation of the MPC. Our theoretical results are applied to a chemical process example, and different scenaria were simulated to demonstrate that the proposed proactive fault‐tolerant model predictive control method can achieve practical stability and efficiently deal with a control actuator fault. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2810–2820, 2013     

An economic model predictive control approach to integrated production management and process operation

Managing production schedules and tracking time‐varying demand of certain products while optimizing process economics are subjects of central importance in industrial applications. We investigate the use of economic model predictive control (EMPC) in tracking a production schedule. Specifically, given that only a small subset of the total process state vector is typically required to track certain scheduled values, we design a novel EMPC scheme, through proper construction of the objective function and constraints, that forces specific process states to meet the production schedule and varies the rest of the process states in a way that optimizes process economic performance. Conditions under which feasibility and closed‐loop stability of a nonlinear process under such an EMPC for schedule management can be guaranteed are developed. The proposed EMPC scheme is demonstrated through a chemical process example in which the product concentration is requested to follow a certain production schedule. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1892–1906, 2017