化工反应器系统的最大时滞可耐受度指数求解与应用分析

化工反应器系统具体操作过程中常会受到时滞和不确定参数影响,在一些操作工况下不确定参数是不可调节的。为了保证化工系统实际操作的安全和可靠性以避免环境污染事件和人员伤害,系统所能耐受的最大时滞值非常有必要得到求解和分析。提出了含不确定参数的化工反应器系统的时滞耐受度指数问题、给出了求解和应用分析框架。首先对反应器系统进行建模,采用泰勒展开式和拉普拉斯变换将反应器系统线性化成为含不确定参数及时滞的传递函数模型。其次采用PID闭环控制的控制策略对系统进行动态响应性能测试,PID参数采用MATLAB的NCD（nonlinear control design package）模块进行优化。最后采用二分法结合系统的动态响应性能测试结果对系统所能耐受的最大时滞值τ_max进行求解。以一个典型具有连续进出料的反应-分离系统为案例,对含不确定参数的反应器系统的时滞耐受度指数进行了求解和分析。研究结果表明,所提出的研究策略可以为含不确定参数的连续反应器系统的时滞耐受度指数的求解提供快速、简洁有效的方法和思路,从而提高化工反应器系统实际运行的安全和可靠性。     

化工反应器系统的最大时滞可耐受度指数求解与应用分析

化工反应器系统具体操作过程中常会受到时滞和不确定参数影响,在一些操作工况下不确定参数是不可调节的。为了保证化工系统实际操作的安全和可靠性以避免环境污染事件和人员伤害,系统所能耐受的最大时滞值非常有必要得到求解和分析。提出了含不确定参数的化工反应器系统的时滞耐受度指数问题、给出了求解和应用分析框架。首先对反应器系统进行建模,采用泰勒展开式和拉普拉斯变换将反应器系统线性化成为含不确定参数及时滞的传递函数模型。其次采用PID闭环控制的控制策略对系统进行动态响应性能测试,PID参数采用MATLAB的NCD(nonlinear control design package)模块进行优化。最后采用二分法结合系统的动态响应性能测试结果对系统所能耐受的最大时滞值τmax进行求解。以一个典型具有连续进出料的反应-分离系统为案例,对含不确定参数的反应器系统的时滞耐受度指数进行了求解和分析。研究结果表明,所提出的研究策略可以为含不确定参数的连续反应器系统的时滞耐受度指数的求解提供快速、简洁有效的方法和思路,从而提高化工反应器系统实际运行的安全和可靠性。     

动态实时优化策略下的分层式预测控制及其在化工过程中的应用

针对化工过程中那些因存在批处理、含有物料回流环节而很难达到稳态的过程以及一些因扰动的存在而很难精确地操作在一个设定点处的非线性过程,采用常规的稳态优化会产生低效或失效优化解的问题,提出一种动态实时优化策略。即在多层控制结构中的RTO层采用动态优化而非常规的稳态优化,依照过程的优化操作信息在满足过程动态规律和物料、产品市场价格变化的条件下实现生产的经济利润最优,事例仿真结果表明该方法的可行性和有效性。     

基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法，其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值，缺乏科学依据，从而大大影响了算法的收敛速度。针对这一问题，提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略，首先利用粒子群优化对间歇化工过程最优控制量进行求解，结果作为控制变量参数化方法初始给定轨迹，进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例，仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法,其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值,缺乏科学依据,从而大大影响了算法的收敛速度。针对这一问题,提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略,首先利用粒子群优化对间歇化工过程最优控制量进行求解,结果作为控制变量参数化方法初始给定轨迹,进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例,仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

多变量化工过程系统的状态空间及其维数的判定准则

化工过程具有多变量、非线性和连续性的特征。状态空间是一种有效的描述过程状态和特征的方法。今从过程测量的基本因次关系出发,结合机理分析,提出了化工过程状态空间维数的判定准则。若系统单进单出的流股中包含i个独立物质组成,需要考虑j维几何尺寸因素,则系统可用一个维数是2 i j的集中参数变量构成的状态空间来唯一确定。该准则可以用于确定主元分析方法(PCA)的主元数目。按照准则确定的空间维数选取主元可以完全描述线性系统的状态信息;对非线性系统,误差在可以接受的范围内。仿真案例中,PCA及过程独立变量分析和准则判定一致。在状态空间中采用PCA建立坐标描述过程的静态和动态特性,实现了过程状态的可视化,以及操作条件的多目标优化。     

基于LonWorks的GA优化复合模糊PID控制系统设计

设计了基于LonWorks的复合模糊PID控制系统,分析了该系统的硬件组成和软件实现方案.在控制嚣设计上,改进了传统的模糊PID控制器结构,采用三个子模糊控制器组成一个复合模糊PlD控制器.把过渡时间作为各子模糊控制器的公共输入并采用遗传算法(GA)对复合模糊PID控制器的隶属度函数进行了优化.通过Mstlab仿真表明,该控制器在参数确定上更具客观性,在控制带有大滞后环节和非线性环节的工业对象时响应速度更快、抗干扰能力更强、动态性能更好.     

慢时变化工过程裕量释放机制分析

化工过程普遍存在慢时变特性,在一个运行周期内慢时变参数的变化造成化工装置性能逐渐下降。为此,过程设计时需要按照慢时变参数可能的“最坏”影响对设计变量留出足够的设计裕量,在一个运行周期内通过操作逐渐释放,补偿慢时变参数的不利影响,且理想操作是保证到运行周期结束时化工装置性能恰好达到过程约束边界。本文对慢时变过程设计裕量的释放机制进行了分析,考虑含慢时变参数的全周期操作优化通用动态模型,通过最优控制的极小值原理求解该优化问题,建立了最优裕量释放轨迹和慢时变参数变化曲线之间的联系,从而证明最优裕量释放只与慢时变化工过程的运行周期有关。以乙炔加氢反应器为例验证了该裕量释放机制,对于慢时变化工过程,设定的运行周期越短,设计裕量释放越快,仅能获得较高的短期经济效益;反之,设定较长的运行周期,设计裕量缓慢释放,能获得更高的长期经济效益。     

基于鲁棒模型优化的工艺设计与控制系统集成

采用一种基于鲁棒模型优化方法求解工艺设计与控制集成问题,其核心思想是采用参数不确定状态空间模型描述闭环系统的非线性行为,基于该模型以及二次李雅普诺夫函数可估计系统在外部扰动作用下过程变量变化的最大边界,同时也可测试系统的鲁棒稳定性。该方法避免了求解集成优化问题常用的动态优化方法,可将其转化成一个非线性规划问题。该方法应用于CSTR反应过程的集成优化设计中,结果表明在存在外部扰动时,不仅可以保证过程的经济性能和动态性能,也可保证系统的鲁棒稳定性。     

一种自适应多策略差分进化算法及其应用

针对差分进化算法由于固定参数设置而易早熟或陷入局部最优的问题,提出了一种自适应多策略差分进化算法（SMDE）。该方法以基本差分进化为框架,首先引入一个变异策略候选集合,一个缩放因子候选集合和一个交叉参数候选集合,然后在搜索过程中,以过去的搜索信息为基础,自适应地为下一时刻进化群体中的每个个体从候选集合中选择一组合适的变异策略和控制参数,以便在不同的进化时刻设置合适的变异策略和控制参数。对10个常用的标准测试函数进行优化计算,并与其他算法的结果进行了比较,实验结果表明,SMDE具有较好的搜索精度和更快的收敛速度。将SMDE用于化工过程动态系统不确定参数估计问题,实验结果表明该算法能较好地处理实际工程优化问题。     

一种改进的热泵供暖系统动态实时优化策略

基于经济性目标的热泵供暖动态优化操作具有重要意义，但在操作区间内环境温度和模型参数变化的不确定性会对实际优化操控带来很大挑战。在完善热泵供暖系统模型的基础上，提出了一种改进的动态实时优化控制策略以改善系统的实际节能效果。该方法首先建立以压缩机和送水泵运行频率为控制变量的热泵供暖系统的非线性动态关系模型，并得到以24 h为周期、以综合性能指标最低为目标的动态实时优化命题。然后，在给定24 h环境温度预测情况下通过求解该优化命题得到热泵压缩机和送水泵的最优运行频率轨线，并以当前时间点的最优控制量对热泵供暖系统进行控制；接着，基于天气逐时预测和模型参数最新校验结果对环境温度轨线或者模型参数进行更新，不断地求解原优化命题以更新最优控制轨线，并不断地采用当前点的最优控制量对热泵供暖系统进行控制，直到当前时间点达到第24 h。实例计算结果表明：采用本文提出的方法可以进一步改善热泵供暖系统的动态优化操控效果，并能够很好地满足给定终端约束要求。本方法对于具有周期性和不确定参数的动态实时优化问题求解具有一定的借鉴意义。     

Nichtlineare stochastische Optimierung unter Unsicherheiten

Nonlinear Stochastic Optimization under Uncertainty Robust decision making under uncertainty is considered to be of fundamental importance in numerous disciplines and application areas. In dynamic chemical processes in particular there are parameters which are usually uncertain, but may have a large impact on equipment decisions, plant operability, and economic analysis. Thus the consideration of the stochastic property of the uncertainties in the optimization approach is necessary for robust process design and operation. As a part of it, efficient chance constrained programming has become an important field of research in process systems engineering. A new approach is presented and applied for stochastic optimization problems of batch distillation with a detailed dynamic process model.     

UNCERTAINTY AT BOTH DESIGN AND OPERATION STAGES

Process uncertainty is almost always an issue during the design of chemical processes (CP). In the open literature it has been shown that consideration of process uncertainties in optimal design necessitates the incorporation of process flexibility. Such an optimal design can presumably operate reliably in the presence of process and modeling uncertainty. Halemane and Grossmann (1983) introduced a feasibility function for evaluating CP flexibility. They also formulated a two-stage optimization problem for estimating the optimal design margins. These formulations, however, are based implicitly on the assumption that during the operation stage, uncertain parameters can be determined with enough precision. This assumption is rather restrictive and is often not met in practice. When available experimental information at the operation stage does not allow a more precise estimate of some of the uncertain parameters, new formulations of the flexibility condition and the optimization problem under uncertainty are needed. In this article, we propose such formulations, followed by some computational experiments.     

Multistage adaptive optimization using hybrid scenario and decision rule formulation

Scenario-based stochastic programming and linear decision rule (LDR)-based robust optimization are prevalent methods for solving multistage adaptive optimization (MSAP) problems. In practical applications such as capacity expansion planning of chemical processes, often multiple sources of uncertainty affect the problem which introduces challenges to traditional stochastic optimization methods. While a large number of uncertain parameters exist in the problem, using scenario-based method results in very large problem size and the solution becomes computationally expensive. In addition, when the constraints include multiplication of uncertain parameters and adaptive variables, the constraints are not linear with respect to uncertain parameters when the LDR method is used. In order to address these challenges, we propose two different hybrid methods where scenario and decision rule methods are combined to solve the MSAP problem. The article demonstrates the computational performance of the proposed hybrid methods using two chemical process planning examples.     

Resilient supply chain design and operations with decision-dependent uncertainty using a data-driven robust optimization approach

To addresses the design and operations of resilient supply chains under uncertain disruptions, a general framework is proposed for resilient supply chain optimization, including a quantitative measure of resilience and a holistic biobjective two-stage adaptive robust fractional programming model with decision-dependent uncertainty set for simultaneously optimizing both the economic objective and the resilience objective of supply chains. The decision-dependent uncertainty set ensures that the uncertain parameters (e.g., the remaining production capacities of facilities after disruptions) are dependent on first-stage decisions, including facility location decisions and production capacity decisions. A data-driven method is used to construct the uncertainty set to fully extract information from historical data. Moreover, the proposed model takes the time delay between disruptions and recovery into consideration. To tackle the computational challenge of solving the resulting multilevel optimization problem, two solution strategies are proposed. The applicability of the proposed approach is illustrated through applications on a location-transportation problem and on a spatially-explicit biofuel supply chain optimization problem. © 2018 American Institute of Chemical Engineers AIChE J, 65: 1006–1021, 2019     

Integrated design and control of semicontinuous distillation systems utilizing mixed integer dynamic optimization

Semicontinuous distillation systems are notoriously difficult to design and optimize because the structural parameters, operational parameters, and control system must all be determined simultaneously. In the past 15 years of research into semicontinuous systems, studies of the optimal design of these systems have all been limited in scope to small subsets of the parameters, which yields suboptimal and often unsatisfactory results. In this work, for the first time, the problem of integrated design and control of semicontinuous distillation processes is studied by using a mixed integer dynamic optimization (MIDO) problem formulation to optimize both the structural and control tuning parameters of the system. The public model library (PML) of gPROMS is used to simulate the process and the built-in optimization package of gPROMS is used to solve the MIDO via the deterministic outer approximation method. The optimization results are then compared to the heuristic particle swarm optimization (PSO) method.     

Dynamic flexibility analysis of chemical reaction systems with time delay: Using a modified finite element collocation method

Chemical reaction systems are often complex dynamic time-delay systems that have to operate successfully in the presence of uncertainties. Under these circumstances, flexibility analysis comes to be much important to the design and operation of time-delay chemical reaction systems. In this work, a modified finite element collocation method was proposed to carry out flexibility analysis of chemical reaction systems with time delay. The proposed method is combined with the linear quadratic regulator (LQR) and Lagrange polynomial for the optimal solution of control variables and state variables respectively. The method is investigated by two typical chemical reaction systems with time delay. All the results demonstrate that the proposed modified finite element collocation method may provide a powerful tool for studying the dynamic flexibility of chemical reaction systems with time delay.     

An approach to multicriteria optimization under uncertainty

Inherent in chemical process models are parameters that have uncertainty associated with them. This paper addresses multicriteria optimization that accounts for model and process uncertainty at the design stage. Specifically the authors have developed extensions of the average criterion method, the worst-case strategy and the ε-constraint method under the following conditions: (a) at the design stage the only information available about the uncertain parameters is that they are bounded by a known uncertainty region T, and (b) at the operation stage, process data is rich enough to allow the determination of exact values of all the uncertain parameters. The suggested formulation assumes that at the operation stage, certain process variables (called control variables) can be tuned or manipulated in order to offset the effects of uncertainty. Three illustrative examples (two benchmark and one direct methanol fuel cell) have been employed.     

Robust Optimal Operation of Two‐Chamber Microbial Fuel Cell System Under Uncertainty: A Stochastic Simulation Based Multi‐Objective Genetic Algorithm Approach

This investigation is performed to study the optimal operation decision of two‐chamber microbial fuel cell (MFC) system under uncertainty. To gain insight into the mechanism of uncertainty propagation, a Quasi‐Monte Carlo method‐based stochastic analysis is conducted not only to elucidate the effect of each uncertain parameter on the variability of power density output, but also to illustrate the interactive effects of the all uncertain parameters on the performance of MFC. Moreover, a systematic stochastic simulation‐based multi‐objective genetic algorithm framework is proposed to identify a set of Pareto‐optimal robust operation strategies, which is helpful to provide an imperative insight into the relationship between the mean and standard deviation of output power density. The results indicate that (1) the coefficient of variance (COV) value of output power density has a linear relationship with the COV value of each uncertainty parameter as well as all interactive parameters; and (2) a significant performance improvement with respect to both mean and standard deviation of power density is observed by implementing the multi‐objective robust optimization. These results thus validate that the proposed uncertainty analysis and robust optimization framework provide a promising tool for robust optimal design and operation of fuel cell systems under uncertainty.