Optimal processing network design under uncertainty for producing fuels and value‐added bioproducts from microalgae: Two‐stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm

Fractional metrics, such as return on investment (ROI), are widely used for performance evaluation, but uncertainty in the real market may unfortunately diminish the results that are based on nominal parameters. This article addresses the optimal design of a large‐scale processing network for producing a variety of algae‐based fuels and value‐added bioproducts under uncertainty. We develop by far the most comprehensive processing network with 46,704 alternative processing pathways. Based on the superstructure, a two‐stage adaptive robust mixed integer fractional programming model is proposed to tackle the uncertainty and select the robust optimal processing pathway with the highest ROI. Since the proposed problem cannot be solved directly by any off‐the‐shelf solver, we develop an efficient tailored solution method that integrates a parametric algorithm with a column‐and‐constraint generation algorithm. The resulting robust optimal processing pathway selects biodiesel and poly‐3‐hydroxybutyrate as the final fuel and bioproduct, respectively. © 2016 American Institute of Chemical Engineers AIChE J, 63: 582–600, 2017     

Least tail‐trimmed squares for infinite variance autoregressions

We develop a robust least squares estimator for autoregressions with possibly heavy tailed errors. Robustness to heavy tails is ensured by negligibly trimming the squared error according to extreme values of the error and regressors. Tail‐trimming ensures asymptotic normality and super‐‐convergence with a rate comparable to the highest achieved amongst M‐estimators for stationary data. Moreover, tail‐trimming ensures robustness to heavy tails in both small and large samples. By comparison, existing robust estimators are not as robust in small samples, have a slower rate of convergence when the variance is infinite, or are not asymptotically normal. We present a consistent estimator of the covariance matrix and treat classic inference without knowledge of the rate of convergence. A simulation study demonstrates the sharpness and approximate normality of the estimator, and we apply the estimator to financial returns data. Finally, tail‐trimming can be easily extended beyond least squares estimation for a linear stationary AR model. We discuss extensions to quasi‐maximum likelihood for GARCH, weighted least squares for a possibly non‐stationary random coefficient autoregression, and empirical likelihood for robust confidence region estimation, in each case for models with possibly heavy tailed errors.     

Incorporating setting information for maintenance‐free quality modeling of batch processes

Typically, the operation condition of the batch process is changed frequently, following different recipes or manufacturing various production grades. For quality prediction purpose, the prediction model should also be updated or rebuilt, which leads to a significant model maintenance effort, especially for those processes which have various phases. To reduce such effort, a maintenance‐free method is proposed in this article, which incorporates the setting information of the batch process for modeling. The whole process variations are separated into two parts: setting information related and other quality related variations. By constructing a relationship between setting variables and other process variables, the data variations explained by the setting information can be efficiently removed. Then, a robust regression model connecting process variables to the quality variable is developed in different phases of the batch process. The feasibility and effectiveness of the proposed method is evaluated through an industrial injection molding process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 772–779, 2013     

精确定位伺服系统的近似时间最优控制

提出了一种近似时间最优控制与时间延迟观测器相结合的快速无静差定点跟踪控制器的设计。控制律以近似时间最优控制律为主体,引入对未知扰动的补偿,利用时间延迟控制技术设计一个对扰动的观测器,同时通过一个现值观测器对系统的不可量测状态进行估计。把这种控制方案用于典型的双积分伺服系统的定位控制,并在MATLAB/Simulink中进行了仿真研究,结果表明所设计的控制系统可以对给定目标进行快速和准确的跟踪,且对扰动和系统参数差异具有较好的性能鲁棒性。     

基于梯度估计的直接自适应控制

A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an adaptive optimal control algorithm is proposed. The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed. The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.     

不敏变换鲁棒Kalman滤波器在发酵过程中的应用(英文)

State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However, there are many difficulties in dealing with a non-linear system, such as the instability of process, un-modeled dynamics, parameter sensitivity, etc. This paper discusses the principles and characteristics of three different approaches, extended Kalman filters, strong tracking filters and unscented transformation based Kalman filters. By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance, an improved Kalman filter, unscented transformation based robust Kalman filter, is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process. The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.     

基于稳健优化的注塑件变形预补偿方法研究

针对凭经验进行预补偿设计容易造成预补偿失败的问题,提出了基于稳健优化的注塑件变形预补偿方法,综合考虑了塑料物性参数、浇注系统、冷却系统及工艺参数对注塑件翘曲变形量的影响,使变形预补偿更具有针对性。首先给出注塑件变形预补偿的原理,并提出利用模流分析来获得变形预补偿的数据,给出了MPI中实现预补偿的方法;接着提出了基于稳健优化的变形预补偿方法及实现流程,建立了适合变形预补偿的稳健优化模型;最后通过实例验证了提出的方法具有较好的可行性及工程实际应用价值。     

非线性多变量系统的多模型广义预测解耦控制

针对实际工业过程中多变量系统存在着非线性、工况范围广、耦合强的特点,提出基于设定值观测器的非线性多模型广义预测解耦控制算法。该方法由线性广义预测控制器、一种新的设定值观测器和切换机构组成。理论分析和仿真结果表明,该控制策略不但可以保证闭环系统B IBO稳定和渐近收敛,而且能够得到很好的控制效果。     

大型装备电液系统鲁棒H_∞位置控制

针对大型装备电液系统非线性、参数不确定性问题,建立了比例阀控非对称缸液压系统的非线性动态模型。考虑反馈控制,建立了具有不确定参数系统线性化数学模型。基于边界理论和线性矩阵不等式,提出了电液系统鲁棒H_∞位置控制策略。着重考虑系统结构化系数的擅动,构建了具有积分行为的H_∞控制器,设计了估测系统内部状态的观测器。通过仿真和实验,验证了提出的算法和控制策略。对比了系统参数不变、外负载力变化下和系统参数变化下系统的鲁棒性和稳定性。仿真和实验结果表明,提出的具有积分行为的鲁棒H_∞位置控制策略能提高系统的鲁棒性和稳定性。     

新型无速度传感器直接转矩控制系统

无速度传感器技术中最常用的方法是自适应观测器,观测器的增益和速度自适应律决定了观测器的性能。传统采用极点配置方法的自适应观测器存在区域不稳定的问题,针对此缺陷提出了一种基于线性矩阵不等式的新型观测器。仿真结果表明:新型观测器克服了传统方法存在的区域不稳定的缺点,能够在全速范围内准确辨识转速。