基于被控变量在线建模的化工过程实时优化方法

选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。     

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。     

一种基于模型的多变量协调控制技术

给出的基于模型的多变量协调控制技术将被控变量的动态控制问题与操作变量的静态优化问题有机地结合起来.对于操作变量多于被控变量的“胖”系统,本控制技术在保证对关键变量实施定点控制的同时,综合考虑操作变量的控制速度、操作成本等经济因素,尽可能将一些操作变量协调至理想操作区内,达到优化(节能、降耗)的目的.对于操作变量少于被控变量的“瘦”系统,则据被控变量的重要程度对其施加不同的控制.给出了控制器的唯一解条件、稳态无差和稳定性性质.针对催化裂化主分馏塔给出的仿真结果说明本协调控制有良好的控制性能,适合生产过程的需要.     

利用输出反馈方法实现聚合物分子量分布的控制

利用正交多项式组合神经网络对聚合反应分子量分布(MWD)进行建模,MWD被解构为若干矩值组成的矩向量函数,由此二元MWD控制问题可被转换为一元分布矩控制问题。以矩向量为直接被控对象,通过对矩的控制实现MWD的跟踪。为便于求解这类非仿射非线性多变量系统的控制策略,提出了改进型非线性自回归正交多项式网络结构,建立模型输出与U(k)之间的线性映射关系;针对高维被控矩向量,证明了矩向量中独立变量与分布函数参变量间的数量对等关系,给出了矩向量降维准则,将高维输出控制转化为低维问题。基于改进的神经网络模型,利用输出反馈方法对MWD矩向量进行控制,实现了对MWD的形状跟踪,仿真实验证明了方法的有效性。所提出的方法为非线性多变量系统的建模控制问题提供了新思路。     

乙苯催化脱氢制苯乙烯的预测函数控制及其稳定性分析

针对乙苯催化脱氢制苯乙烯过程的特点,给出多输入多输出系统的预测函数控制算法应用设计.同时基于Lyapunov函数方法分析了闭环控制系统的稳定性能,是判断不确定性存在时控制系统具有鲁棒能力的一种定量指标.选取某苯乙烯厂乙苯进量、蒸汽过热炉A 出口温度、蒸汽过热炉B出口温度、主蒸汽流量、压缩机入口压力5个量为控制输入,乙苯脱氢转化率及脱氢选择性为被控变量,应用所设计预测函数控制算法实现了脱氢系统的控制.实验结果表明,该算法对于乙苯脱氢生产过程具有好的鲁棒性和抗干扰性.     

全局自优化控制策略及其测量变量子集选择

针对过程系统的优化运行问题,介绍一种基于Monte Carlo模拟的全局自优化控制策略。利用非线性模型计算整个操作空间内的平均经济损失,通过对某些条件进行合理假设,得到全局被控变量的解析表达形式。为了平衡传感器成本和系统性能,在全局自优化控制策略的基础上,引入混合整数约束,对测量变量子集进行选择。通过求解混合整数规划问题,能够同时获得最优的测量变量子集以及由其构成的全局被控变量,此外上述子集选择方法还可以处理附加的结构性约束问题。通过对蒸发过程的研究表明,该方法可以更加高效地处理测量变量子集选择问题,通过对精馏塔案例的研究,进一步验证了该方法在处理结构性约束问题中的优势。     

钢制外压容器加强圈的优化设计

本文以现代优化设计理论为基础,针对外压容器加强圈的工程设计和应用特点,通过选取设计变量,确定欲优化的性能指标;给定技术限制条件,建立性能指标与设计变量之间的函数关系;以及运用适当的优化方法进行计算等过程,阐述最小重量外压容器加强圈优化设计的基本方法。     

模糊系统的模型化和优化

过程工业中的生产过程变量关系比较复杂，难于获得精确的模型，常使得过程系统的模拟和优化有较大误差。本文提出基于模糊逻辑表述和处理系统变量间的不精确近似关系，使模型方程间的冲突和不协调性趋于最小，模糊目标函数趋于最大。从而实现对模糊系统的模拟和优化。     

流量同时作为工艺优化变量和串级控制副变量时的工艺优化实现与分析

针对如何通过现存的"主被控变量-流量"这种典型的化工过程串级控制实现流量优化的在线优化问题,对在线优化的实现结构进行分析,给出一种流量优化变量通过一阶惯性环节改变主回路被控变量设定值的在线优化的理想方案,通过CSTR的仿真实例证明了优化实现方案的可行性。     

锅炉循环清洗调度问题优化

多操作周期的锅炉蒸汽系统是一个性能随时间衰减的混合整数非线性规划问题,其目标函数由燃煤量和锅炉清洗维护的费用构成。针对此类非线性问题,提出了最速下降近似线性规划（SDALP）的算法,利用最快速下降的目的性搜索思想,引入移入、移出向量和积极富裕量,通过对调整变量边界的判断条件和途径进行了新的定义,排除了传统近似线性规划方法中主观选择初始可行解、步长限制量以及缩小系数等变量可能导致的将最优解排除在变量边界之外的问题。将该算法应用于锅炉蒸汽系统循环清洗调度问题,实验结果表明,SDALP算法解出的调度方案优于原电厂调度方案,节能效果明显。     

Enhanced-efficiency operating variables selection for vapor compression refrigeration cycle system

In this paper, a novel enhanced-efficiency selection of operating variables based on self-optimizing control (SOC) method for the vapor compression refrigeration cycle (VCC) system is proposed. An objective function is proposed to maximize the energy efficiency of the VCC system while meeting with the demand of indoor thermal comfort. With the detailed analysis of operating variables, three unconstrained degrees of freedom are selected among all the candidate operating variables. Then two SOC methods are applied to determine the optimal individual controlled variables (CVs) and measurement combinations as CVs. The model predictive control (MPC) method based controllers and PID controllers are designed for different sets of CVs, and the experimental results indicate that the proposed selection of CVs can achieve a good trade-off between optimal (or near optimal) stable operation and enhanced-efficiency of the synthesized control structure.     

Decentralized plantwide control strategy for large-scale processes. Case study: Pulp mill benchmark problem

The plantwide control (PWC) complexity increases for highly-integrated and large-scale chemical processes. This work presents a novel framework for decentralized PWC which includes: (i) the selection of the controlled variables (CVs), (ii) the pairing between the manipulated variables (MVs) and the CVs, and (iii) the determination of the controller algorithms as well as their tuning parameters for closed-loop operation. The proposal is to solve the steps (i) and (ii) simultaneously, driving the selection of the most effective PWC structure from a Pareto optimal set. Here, algorithms based only on steady-state information are considered to give a systematic procedure which tries to minimize the use of heuristic considerations. Genetic algorithms (GA) and the Hungarian algorithm (HA) are used here because they provide a good trade-off between computational effort and acceptable results. The proposed methodology is completely tested in a pulp mill benchmark and compared with a previous one.     

Branch and bound method for regression-based controlled variable selection

Self-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements. Recently, Ye, Cao, Li, and Song (2012) have proposed a globally optimal method for selection of self-optimizing CVs by converting the CV selection problem into a regression problem. In this approach, the necessary conditions of optimality (NCO) are approximated by linear combinations of available measurements over the entire operation region. In practice, it is desired that a subset of available measurements be combined as CVs to obtain a good trade-off between the economic performance and the complexity of control system. The subset selection problem, however, is combinatorial in nature, which makes the application of the globally optimal CV selection method to large-scale processes difficult. In this work, an efficient branch and bound (BAB) algorithm is developed to handle the computational complexity associated with the selection of globally optimal CVs. The proposed BAB algorithm identifies the best measurement subset such that the regression error in approximating NCO is minimized and is also applicable to the general regression problem. Numerical tests using randomly generated matrices and a binary distillation column case study demonstrate the computational efficiency of the proposed BAB algorithm.     

换热网络的优化改进和控制

通过对换热网络的定性分析提出了换热网络优化改进的4条规则：①控制变量应对调节变量反应灵敏；②选择的调节变量数应小于或等于控制变量数；③一组控制变量应有一组调节变量相匹配；④如果在标准操作条件下现有换热网络是最佳的，而且改进过程中对它做最小的改变就使换热网络适合当前的或多周期操作条件, 那么该改进费用一定是最小的，因此这种改进就是最佳的.根据上述规则，结合换热网络的灵敏度分析建立了换热网络优化改进及控制的数学模型和求解方法，给出了换热网络优化改进的步骤，通过实例计算及应用证明本文所提方法正确、可靠、有效，可在解决实际工程问题中使用.     

Bidirectional branch and bound for controlled variable selection: Part I. Principles and minimum singular value criterion

The minimum singular value (MSV) rule is a useful tool for selecting controlled variables (CVs) from the available measurements. However, the application of the MSV rule to large-scale problems is difficult, as all feasible measurement subsets need to be evaluated to find the optimal solution. In this paper, a new and efficient branch and bound (BAB) method for selection of CVs using the MSV rule is proposed by posing the problem as a subset selection problem. In traditional BAB algorithms for subset selection problems, pruning is performed downwards (gradually decreasing subset size). In this work, the branch pruning is considered in both upward (gradually increasing subset size) and downward directions simultaneously so that the total number of subsets evaluated is reduced dramatically. Furthermore, a novel bidirectional branching strategy to dynamically branch solution trees for subset selection problems is also proposed, which maximizes the number of nodes associated with the branches to be pruned. Finally, by replacing time-consuming MSV calculations with novel determinant based conditions, the efficiency of the bidirectional BAB algorithm is increased further. Numerical examples show that with these new approaches, the CV selection problem can be solved incredibly fast.     

Economically efficient operation of CO2 capturing process part I: Self-optimizing procedure for selecting the best controlled variables

We study optimal operation of a post-combustion CO₂ capturing process where optimality is defined in terms of a cost function that includes energy consumption and penalty for released CO₂ to the air. Three different operational regions are considered.In region I, with a nominal flue gas flowrate, there are two optimally unconstrained degrees of freedom (DOFs) and the corresponding best self-optimizing controlled variables (CVs) are found to be the CO₂ recovery in the absorber and the temperature at tray no. 16 in the stripper. In the region II, with an increased flue gas flowrate, the heat input is saturated and there is one unconstrained DOF left. The best CV is temperature at tray no. 13 in the stripper. In region III, when the flue gas flowrate is further increased, the process reaches the minimum allowable CO₂ recovery of 80% and there is no unconstrained DOF. We have then reached the bottleneck and a controller is needed to adjust the feed flowrate to avoid violating this minimum.The exact local method and the maximum gain rule are applied to find the best CVs in each region.     

基于加权偏离度统计方法的预测控制性能评估算法

针对带区域约束条件的预测控制系统性能评估问题,在考虑过程输出变量约束类型的基础上,提出了基于加权偏离度统计方法的控制性能评估算法。该方法依据控制要求的不同,将输出变量分为质量变量和约束变量,并结合工程经验合理选择变量的权重。基于系统闭环运行数据和约束设置,通过计算变量的加权偏离度得到控制系统的性能评估指标,从而为预测控制器的参数调整和性能提升提供了决策依据。系统仿真实例和工程应用证明了该评估算法对区域预测控制系统性能评估的有效性。     

Improvements on multiloop control design via net load evaluation

The plant-wide control problem is a very important topic in process control. A particular control structure design will define (restrict) the future operability degree for the plant under study. Classical control policies (decentralized or full) are not always the best solution. In this context a systematic and generalized strategy to solve the multivariable plant-wide control problem is proposed here. The methodology called minimum square deviation (MSD) considers several points such as the optimal controlled variables (CVs) selection based on the sum of square deviation (SSD) and controller structure design supported by net load evaluation (NLE) analysis. The overall problem is combinatorial and is solved by accounting several steady-state tools and new indexes minimizing the heuristic load. Four well-known case studies are presented and other approaches taken from the literature are accounted for the sake of comparison. A robust stability test, μ-tools, is also performed for concluding about the control policies.