金氰化浸出过程自适应优化

以某湿法冶炼厂金氰化浸出过程为背景,研究了浸出过程的自适应实时优化问题.针对实际过程中的模型失配问题,提出了一种金氰化浸出过程的修正项自适应实时优化策略,利用实际过程测量值及梯度信息不断修正原优化问题,使其迭代收敛到实际过程的最优设定点.仿真结果表明在过程输出存在适量的测量噪声时,对于模型参数不确定性、结构不确定性以及工况改变3种情况,该方法经过数次的迭代最终都能够收敛到实际过程的最优设定点附近,节约了生产成本,而且不需要模型更新步骤,这为湿法冶金全流程优化控制的顺利实施奠定了重要基础.     

基于NLQG的分批补料发酵过程预测控制方法

发酵过程具有高度非线性、时滞性和不确定性等特征,直接影响着发酵过程的有效调控。提出了一种基于非线性二次高斯(NLQG)的分批补料发酵过程预测控制方法。该方法由扩展Kalman滤波器(EKF)和NLQR串联构成,EKF给出被控变量的最优状态估计,NLQR获得被控变量的实时状态反馈,以实现分批补料发酵过程的动态预测控制。在LabVIEW软件平台中,利用ActiveX控件调用MATLAB环境下编译生成的COM组件设计了NLQG控制器,并用于青霉素分批补料发酵过程溶氧浓度的预测控制。实验结果表明,所提出的分批补料发酵过程预测控制方法对于被控变量的设定值变化有良好的跟踪效果,在不同的噪声环境下均能获得较高的控制精度,具有较强的鲁棒性。     

PTFE间歇聚合反应过程变周期动态经济优化控制

聚四氟乙烯(PTFE)间歇聚合生产模式可满足小批量、多用途以及高质量产品的市场需求。针对PTFE聚合过程存在强非线性和大时滞特性,提出了一种基于自由终端的动态经济优化控制方法。首先,将生产周期作为一个自由度纳入优化变量建立动态经济优化问题,采用改进控制变量参数化方法,控制输入被离散为可变长度的片状序列,便可将动态经济优化问题转换为非线性规划(NLP)问题;然后,采用基于梯度下降的内点罚函数法求解NLP问题,通过变周期预测时域的滚动优化控制方法优化控制输入和终端时间;最后将提出的变周期动态经济优化控制与传统PI控制、非线性模型预测控制进行对比测试分析,仿真结果表明本方法单位经济效益更高,生产周期更短,突显了间歇生产的灵活性。     

PTFE间歇聚合反应过程变周期动态经济优化控制

聚四氟乙烯(PTFE)间歇聚合生产模式可满足小批量、多用途以及高质量产品的市场需求。针对PTFE聚合过程存在强非线性和大时滞特性,提出了一种基于自由终端的动态经济优化控制方法。首先,将生产周期作为一个自由度纳入优化变量建立动态经济优化问题,采用改进控制变量参数化方法,控制输入被离散为可变长度的片状序列,便可将动态经济优化问题转换为非线性规划(NLP)问题;然后,采用基于梯度下降的内点罚函数法求解NLP问题,通过变周期预测时域的滚动优化控制方法优化控制输入和终端时间;最后将提出的变周期动态经济优化控制与传统PI控制、非线性模型预测控制进行对比测试分析,仿真结果表明本方法单位经济效益更高,生产周期更短,突显了间歇生产的灵活性。     

分批补料反应过程的非固定终端经济优化控制

针对批次生产周期不确定问题,提出一种非固定终端的经济优化控制方法。首先采用经济模型预测控制方法,用收益最大化的经济型目标函数代替终端约束,并将批次生产周期纳入被优化变量,建立动态经济优化问题,并通过对每个控制变量进行有差异的参数化,将动态优化问题转化为非线性规划（NLP）问题;然后使用内点罚函数法求解含非线性约束的优化问题,得到的最优控制序列和最佳批次生产周期,可将不确定扰动带来的损失降低到最小。其次采用非固定预测时域的滚动时域控制方法,不仅提高多变量系统的协同控制能力,而且根据实时预测终端产品产量不断优化更新关键操纵变量的控制分段函数的分割数及控制序列,从而可灵活优化操纵变量和操作时间的轨迹。最后在苯胺加氢过程上进行了批次优化控制性能测试,测试结果表明,非固定终端的经济优化控制从批次的总生产效益角度来优化每个批次生产的操作条件,实现批次反应过程生产时间与经济效益的最优化管理。     

焙烧酸浸渣中铜的形态对铜、金浸出率的影响

以某黄金冶炼厂含铜金精矿为研究对象,采用铜化学物相分析及浸出方法研究了焙烧?酸浸?氰化工艺处理含铜金精矿过程中焙烧酸浸渣中铜形态对铜、金浸出率的影响. 结果表明,含铜金精矿焙烧酸浸及氰化浸出时,铜形态对铜、金浸出率有显著影响,当酸浸渣中氰化易溶铜(氧化铜、次生硫化铜)含量大于0.10%时,金浸出率降低. 以原生硫化铜矿为主的含铜金精矿,适当提高焙烧温度、延长焙烧时间、增加初始酸浸酸度可有效降低酸浸渣中氰化易溶铜含量,提高铜浸出率,减弱其对金浸出率的影响.     

大型聚丙烯生产装置牌号切换滚动时域控制

基于滚动时域优化控制策略,提出了一种大型双环管聚丙烯生产过程最优牌号切换控制方法。首先结合机理分析,以Hammerstein模型描述聚丙烯牌号切换过程中质量指标动态特性;其次,根据牌号切换的性能指标,滚动优化计算每个时刻各反应器中聚合温度、氢气与丙烯浓度比、共聚单体乙烯与丙烯浓度比等操作变量。进一步结合双层控制结构和状态观测器设计,实现聚丙烯产品牌号切换的滚动优化闭环控制,并预测聚合物产品各质量指标的动态轨迹。最后,通过给出多个牌号切换实例仿真验证本文结果的有效性和实用性。     

基于预测的纸浆洗涤过程优化与控制

提出了基于预测的洗涤过程优化与控制一体化策略方法，该方法的控制级策略采用递推广义预测自校正控制器（ＲＧＰＣ）减少了现有广义预测控制（ＧＰＣ）算法在线计算量，同时采用多步预测信息的优化级目标函数，提高了优化策略的鲁棒性。     

污水处理过程的多目标多模型预测控制方法研究

针对污水处理过程节能降耗问题以及污水处理过程的高度非线性、强耦合、不确定性等特点.以基于活性污泥2号模型ASM2的A2/O污水处理过程为研究对象,提出了污水处理过程的多目标多模型预测控制方法.该方法首先采用聚类-PLS方法建立污水过程的多模型预测模型,然后构建了包含出水水质区间控制和经济性能指标的多目标优化结构的预测控制策略.仿真结果表明,与设定值预测控制方法相比,多目标优化预测控制策略在保证出水水质的前提下,能有效地节约能耗费用.     

基于期望响应的预测函数控制参考轨迹自校正方法

采用指数律参考轨迹的传统预测函数控制（PFC）,系统动态响应无法预先估计,不能直接按照指定的轨迹整定动态响应。本文提出了一种预测函数控制参考轨迹自校正策略。基于事先规定的期望响应,对指数律参考轨迹的衰减系数进行实时在线滚动优化,保证预测时域内的参考轨迹最大幅度地贴近期望轨迹,使得系统响应能够准确地跟踪期望轨迹,并在指定的时刻到达设定值,同时也保留了控制输出平滑的特性。仿真实验证实了该方法的有效性及其优点。     

Multi-stage economic model predictive control for a gold cyanidation leaching process under uncertainty

In order to dynamically operate the gold cyanidation leaching process (GCLP) under uncertainty, a multi-stage economic model predictive control (EMPC) is proposed for GCLP for the transient and steady-state economic optimization. The proposed multi-stage EMPC is composed of two steps. In the first step, the unmeasurable uncertain parameters are estimated by using Tikhonov regularization based method, so as to avoid amplification and propagation of the noise measurements into the estimation. Based on the estimated results, the scenario tree for multi-stage EMPC is generated from the historical data using a data-driven approach, and the control inputs are obtained from solving the resulting large nonlinear programming problem (NLP) at each sampling point. The resulting uncertainty model and the probability of each scenario are more consistent with the actual industrial GCLP, and the solutions are less conservative. The efficiency of the proposed multi-stage EMPC is verified through a simulated industrial GCLP. Compared with other EMPC methods, including classic EMPC and multi-stage EMPC with box uncertainty region, the proposed method can reduce the economic cost while accounting for the constraints at the same time.     

Economic model predictive control with time‐varying objective function for nonlinear process systems

Economic model predictive control (EMPC) is a control scheme that combines real‐time dynamic economic process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time‐varying process economics). In the present work, we focus on the development of a Lyapunov‐based EMPC (LEMPC) scheme that is formulated with an explicitly time‐varying economic cost function. First, the formulation of the proposed two‐mode LEMPC is given. Second, closed‐loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed‐loop simulations of a chemical process that the proposed LEMPC can achieve stability with time‐varying economic cost as well as improve economic performance of the process over a conventional MPC scheme. © 2013 American Institute of Chemical Engineers AIChE J 60: 507–519, 2014     

Smart manufacturing: Handling preventive actuator maintenance and economics using model predictive control

Integrating components and systems of the manufacturing process is an important area of research to enable the future development and deployment of the Smart Manufacturing paradigm. An economic model predictive control (EMPC) scheme is proposed that effectively integrates scheduled preventive control actuator maintenance, process economics, and process control into a unified methodology. To accomplish this goal, a Lyapunov‐based EMPC (LEMPC) scheme is formulated for handling changing number of online actuators (i.e., changing number of manipulated inputs). Closed‐loop stability under the proposed LEMPC is proven. Subsequently, the LEMPC is applied to a chemical process network used for benzene alkylation to demonstrate that the LEMPC can maintain stability and improve dynamic economic performance of the process network in the presence of changing number of available control actuators resulting from scheduled preventive maintenance tasks. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2179–2196, 2014     

Economic model predictive control of nonlinear process systems using empirical models

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J, 61: 816–830, 2015     

Selection of control configurations for economic model predictive control systems

Economic model predictive control (EMPC) is a feedback control method that dictates a potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in the EMPC system may be a general nonlinear function that describes the process/system economics. As this function is not derived on the sole basis of classical control considerations (stabilization, tracking, and optimal control action calculation) but rather on the basis of economics, selecting the appropriate control configuration, and quantifying the influence of a given input on an economic cost is an important task for the proper design and computational efficiency of an EMPC scheme. Owing to these considerations, an input selection methodology for EMPC is proposed which utilizes the relative degree and the sensitivity of the economic cost with respect to an input to identify and select stabilizing manipulated inputs with the most dynamic and steady‐state influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and integrated into a proposed input selection methodology for EMPC. The control configuration selection method for EMPC is demonstrated using a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3230–3242, 2014     

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     

Distributed economic model predictive control for operational safety of nonlinear processes

Achieving operational safety of chemical processes while operating them in an economically‐optimal manner is a matter of great importance. Our recent work integrated process safety with process control by incorporating safety‐based constraints within model predictive control (MPC) design; however, the safety‐based MPC was developed with a centralized architecture, with the result that computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. To address this potential practical limitation of the safety‐based control design, in this work, we propose the integration of a distributed model predictive control architecture with Lyapunov‐based economic model predictive control (LEMPC) formulated with safety‐based constraints. We consider both iterative and sequential distributed control architectures, and the partitioning of inputs between the various optimization problems in the distributed structure based on their impact on process operational safety. Moreover, sufficient conditions that ensure feasibility and closed‐loop stability of the iterative and sequential safety distributed LEMPC designs are given. A comparison between the proposed safety distributed EMPC controllers and the safety centralized EMPC is demonstrated via a chemical process example. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3404–3418, 2017     

Economic model predictive control of stochastic nonlinear systems

This work focuses on the design of stochastic Lyapunov‐based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov‐based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov‐based controller to ensure economic optimality, feasibility and stability in probability in a well‐characterized region of the state‐space surrounding the origin. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an EMPC scheme that treats the disturbances in a deterministic, bounded manner. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3312–3322, 2018