Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

Efficient geometrical parametrization for finite-volume-based reduced order methods

In this work, we present an approach for the efficient treatment of parametrized geometries in the context of proper orthogonal decomposition (POD)-Galerkin reduced order methods based on finite-volume full order approximations. On the contrary to what is normally done in the framework of finite-element reduced order methods, different geometries are not mapped to a common reference domain: the method relies on basis functions defined on an average deformed configuration and makes use of the discrete empirical interpolation method to handle together nonaffinity of the parametrization and nonlinearities. In the first numerical example, different mesh motion strategies, based on a Laplacian smoothing technique and on a radial basis function approach, are analyzed and compared on a heat transfer problem. Particular attention is devoted to the role of the nonorthogonal correction. In the second numerical example, the methodology is tested on a geometrically parametrized incompressible Navier-Stokes problem. In this case, the reduced order model is constructed following the same segregated approach used at the full order level.     

操作窗口对排风柜污染物控制效果影响的实验研究

为提升排风柜的控污能力,在排风柜性能测试实验台上,采用示踪气体测试法和正交试验法,研究了操作窗口竖直开度、水平开度、面风速和操作窗口位置对排风柜前呼吸区污染物控制效果的影响。极差分析和方差分析结果表明:各因素对呼吸区污染物控制效果影响的强弱顺序依次为竖直开度、水平开度、面风速、操作窗口位置;操作窗口水平开度、竖直开度增大,排风柜对呼吸区的污染物控制效果减弱;面风速增大,控制效果增强;水平开度与竖直开度对控制浓度的影响存在较大的相关性,这2个因素对控制浓度的影响仅在另一因素达到一定水平后才能体现。     

铝合金6061-T6三维铣削加工有限元仿真及实验

为了获得6061-T6铝合金材料在铣削过程中铣削参数对铣刀切削性能的影响,使用有限元软件AdvantEdge建立有限元模型,研究了铣削深度、铣削宽度和主轴转速对切削力及温度的影响。根据仿真结果分析可得,铣削参数对切削力的影响铣削深度﹥铣削宽度﹥主轴转速;对温度的影响铣削宽度﹥铣削深度﹥主轴转速。通过实验对比,发现仿真结果与实验结果误差不超过30%,且切削力的走向基本一致,说明仿真结果是可信的。     

基于AnyCasting的铝合金弹底转座压铸工艺参数优化

结合铸造生产实际,合理设计了铸件的结构。采用AnyCasting数值模拟软件,用正交试验方法分析了铝合金弹底转座压铸工艺过程中浇注温度、充型速度以及模具预热温度对铸件质量的影响规律。结果表明,模具预热温度对铸件质量的影响最大,浇注温度次之,充型速度最小,最优的工艺参数是浇注温度为650℃、充型速度为0.25 m/s和模具预热温度为180℃。同时,在最优工艺参数的基础上,结合实物验证了模拟的可靠性,并观察了铸造铝合金的微观组织。     

基于神经网络遗传算法的磁粒研磨TC4材料工艺参数优化

目的利用磁粒研磨光整加工技术提高TC4材料的表面质量,使用BP神经网络建立加工工艺参数和表面粗糙度之间的关系,使用遗传算法寻找最优工艺参数组合。方法使用双级雾化快凝法制备的金刚石磁性磨料对TC4材料工件进行L9(34)正交试验,借助Matlab软件建立结构为4-12-1的BP神经网络,根据正交试验结果训练BP神经网络,探究工艺参数主轴转速n、加工间隙δ、进给速率v、磨料粒径D和表面粗糙度Ra之间的关系。使用决定系数R2评判BP神经网络训练结果,基于训练好的BP神经网络使用遗传算法对工艺参数进行全局寻优。使用计算得到的优化工艺参数进行试验,并测量工件表面粗糙度,与计算得到的表面粗糙度做对比。结果BP神经网络的预测误差在1.5%以下,通过决定系数R2优化的模型可在训练样本较少的情况下进行有效可靠的预测。遗传算法优化的结果,在主轴转速为1021.26 r/min、加工间隙为1.52 mm、进给速率为1.04 mm/min、磨料粒径为197.91μm下,获得最佳表面粗糙度,为0.0951μm。使用调整后的工艺参数,在主轴转速为1020 r/min、加工间隙为1.50 mm、进给速率为1.0 mm/min、磨料粒径为196μm下,试验得到的表面粗糙度为0.093μm,与计算得到的最佳表面粗糙度误差为2.21%。结论采用磁粒研磨光整加工技术与寻优参数结合,可以有效提高TC4材料加工后的表面质量。     

正交优化带油特种钢材表面高温耐磨涂料

目的 改善特种钢材在高温、中低速等恶劣工况服役条件下的摩擦磨损性能。方法 采用涂料制备技术对带有二甲基硅油处理的特种钢材进行表面处理。以环氧E-51树脂为基料，可膨胀石墨、CaF2等高温耐磨材料为颜填料，制备了一种适用于特种钢材表面带油涂装的涂料，并以高温磨损、热失重率、腐蚀电流密度等共同作为评价标准，其中高温磨损作为主要评价标准。在单因素实验的基础上进行正交优化试验，利用熵权法将9组正交优化试验进行分析。结果 优化试验结果表明，当颜基比为1.2∶1、石墨用量为35%、CaF2用量为22%、玻璃鳞片用量为18%时，漆膜的高温磨损率仅为0.97%，200 ℃热失重率仅为5.41%，400 ℃热失重率为37.84%，腐蚀电流密度仅为1.337′10-7 A/cm2，漆膜的综合性能最好，其他性能也同样满足国家标准，其中漆膜的耐碱性能远大于国家标准。结论 通过综合加权法计算的正交优化方案的性能比正交9组试验有较为明显的提高，其中高温耐磨率由2.5%降低至0.97%，腐蚀电流密度由2.61′10-7 A/cm2降低至1.337′10-7 A/cm2，而耐高温性能也由原本的最高350 ℃提高到400 ℃。     

Effects of electrical parameters and their interactions on plasma electrolytic oxidation coatings on aluminum substrates

Based on orthogonal experiments, the effects of voltage, frequency, duty ratio and their interactions on the thickness and corrosion resistance of coatings prepared by plasma electrolytic oxidation (PEO) on aluminum in an alkaline silicate-containing electrolyte were investigated. The thicknesses of these coatings were obtained by measuring their cross-section using Image J software. Their corrosion resistances were evaluated in HCl and NaCl media through spot test and electrochemical test. The results show that the experimental design of this study is the key to investigate the interactions among these electrical parameters. Additionally, not only each independent factor, but also their interactions exhibit a remarkable influence on the coatings. The combination of high voltage, low frequency and large duty ratio significantly increases the coating thickness and content of the corrosion resistance phase, and thus improves the corrosion resistance of the coating in HNO₃ medium. Conversely, the coating possessing the densest microstructure and best corrosion resistance in NaCl medium is obtained when low voltage and high frequency match with a small duty ratio.     

沿海地区紫菜中总砷含量及砷形态分析

建立高效液相色谱-氢化物发生-原子荧光光谱(HPLC-HG-AFS)联用法分析紫菜中4种砷的形态。在优化仪器参数的基础上,采用正交试验法分析样品前处理中的酸浓度、萃取温度和超声时间对砷提取量的影响。当硝酸浓度0.15 mol/L、萃取温度80℃、超声时间1 h时,萃取效果最佳。对低、中、高3个水平进行方法验证,砷形态的回收率为84.0%~113.6%,精密度为1.3%~4.6%,结果显示,沿海地区的紫菜产品中总砷含量高,但有毒砷占比小。方法快速高效,可用于测定紫菜中砷形态的含量。     

人工砂混凝土性能试验研究

采用正交试验方法配制了不同人工砂取代率、石粉含量、粉煤灰含量的混凝土, 对比了每组混凝土的坍落度, 研究了3 d、28 d龄期混凝土的抗压性能。结果表明: 最优配比的人工砂取代率、石粉含量和粉煤灰含量分别为10%、3%和5%, 此时28 d抗压强度为40.1 MPa, 满足30 MPa的混凝土设计强度。