An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

脉冲电沉积镍-铜合金

采用脉冲电沉积技术在304不锈钢表面制备Ni-Cu合金镀层,镀液组成和工艺条件为:NiSO4ꞏ6H2O 200g/L,CuSO4ꞏ5H2O 10 g/L,十二烷基硫酸钠0.2 g/L,柠檬酸钠80 g/L,糖精0.2 g/L,pH 4.0,温度25°C,搅拌速率30 r/min,平均电流密度40~120 mA/cm2,脉冲频率0~100 Hz,占空比20%~90%,时间30 min。研究了平均电流密度、脉冲频率和占空比对Ni-Cu合金镀层的元素组成、表面形貌和显微硬度的影响,得到较优的工艺参数为:平均电流密度40 mA/cm2,脉冲频率50 Hz,占空比60%。该条件下所得Ni-Cu合金镀层由质量分数分别为56.53%和43.47%的Ni和Cu组成,呈“菜花”状形貌,结晶细致、均匀,显微硬度为614.4 HV。     

Investigation on phase shifting for a 4 K Stirling pulse tube cryocooler with He-3 as working fluid

He-3 is generally recognized for its ability to provide more excellent thermophysical performance than He-4, especially in the 4 K temperature range. However, this was not always the case in our preliminary experiments on a three-stage Stirling-type pulse tube cryocooler (SPTC). Our ongoing studies, as reported in this paper, demonstrate that the different working fluids also affect the performance through their phase shifting capability. This feature has been passed over in large part by researchers considering refrigerant substitution. Unlike previous theoretical analyses that focus primarily on regenerator losses, this report investigates the effects of the working fluid on the phase angle at the cold end in order to quantitatively reveal the relationship between the lowest attainable temperature and the cooling capacity. The analysis agrees well with our experimental results on a three-stage SPTC. While running with the operating parameters optimized for He-3, the lowest temperature of the SPTC decreased from 5.4 K down to 4.03 K. This is the lowest refrigeration temperature ever achieved with a three-stage SPTC.     

靶面激光光斑尺寸原位测量的研究

提供了一种简便易行的靶面激光光斑尺寸原位测量的方法。从高斯光束的横向光强分布特性出发,建立了激光烧蚀斑半径与辐照激光能量、光斑尺寸、烧蚀阈值间的关系式,模拟分析发现辐照激光光斑尺寸对烧蚀斑半径随辐照能量变化曲线有较大影响。对于脉宽为2 ms,波长为1064 nm的激光,实验测量了不同能量激光辐照下相纸烧蚀斑半径,并用推导出的关系式拟合测量数据,获得了靶面处光斑尺寸和样品烧蚀阈值。同时,也测量了不同位置处的光斑尺寸和样品烧蚀阈值,对高斯光束束腰位置和样品烧蚀阈值的光斑尺寸效应进行了验证。研究结果表明该技术结果可靠,简单高效。该技术可以为高能激光与固体物质相互作用的基础研究和激光加工等应用领域中实现简单方便地测量靶面光斑尺寸提供帮助。     

A three-dimensional surface measurement system implemented with Gaussian process based adaptive sampling

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.     

Microstructure,tribological behavior and magnetic properties of Fe−Ni−TiO2 composite coatings synthesized via pulse frequency variation

The Fe−Ni−TiO₂ nanocomposite coatings were electrodeposited by pulse frequency variation. The results showed that the nanocomposite with a very dense coating surface and a nanocrystalline structure was produced at higher frequencies. By increasing the pulse frequency from 10 to 500 Hz, the iron and TiO₂ nanoparticles contentswere increased in expense of nickel content. XRD patterns showed that by increasing the frequency to 500 Hz, an enhancement ofBCC phase was observed and the grain size of deposits was reduced to 35 nm. The microhardness and the surface roughness were increased to 647 HV and 125 nm at 500 Hz due to the grain size reduction and higher incorporation of TiO₂ nanoparticles into the Fe−Ni matrix (5.13 wt.%). Moreover, the friction coefficient and wear rate values were decreased by increasing the pulse frequency;while the saturation magnetization and coercivity values of the composite deposits were increased.     

Application of differential evolution for cascaded multilevel VSI with harmonics elimination PWM switching

Harmonic elimination pulse width modulation (HEPWM) method has been widely applied to multilevel voltage source inverter (MVSI) to remove low frequency harmonics from its output voltage. However, the computation of the HEPWM switching angles for MVSI is very challenging due to several constraints, namely angle sequencing, very tight angular spacing and the numerous possibilities of angles distribution ratio. Realizing the potential of Differential Evolution (DE) to handle complex problems, this work proposes its application to solve the HEPWM problem for cascaded MVSI. Its emphasis is on improving the availability of HEPWM for higher output voltage by extending the maximum range of modulation index (M). It also removes the discontinuities in the switching angles and reduces the number of distribution ratio required to obtain the required solution. Compared to the most advanced (similar) work, i.e., 7-level MVSI with seventeen switching angles, DE covers a wider range of M; the maximum achievable M is 2.80. Furthermore, it exhibits very low second order distortion factor (DF₂): for the worst case, the value of DF₂ is 0.0014%. To verify the viability of the proposed algorithm, simulation is carried out and hardware prototype is constructed. Both results show very good agreement with the theoretical prediction.