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.     

Lactobacillus reuteri-fortified camel milk infant formula: Effects of encapsulation,in vitro digestion,and storage conditions on probiotic cell viability and physicochemical characteristics of infant formula

Lactobacillus reuteri fortified camel milk infant formula (CMIF) was produced. The effect of encapsulation in different matrices (sodium alginate and galacto-oligosaccharides) via spray drying, simulated infant gastrointestinal digestion (SIGID), and storage conditions (temperature and humidity) on the viability of L. reuteri in CMIF and the physicochemical properties of CMIF were evaluated. Compared with free cells, probiotic cell viability was significantly enhanced against SIGID conditions upon encapsulation. However, L. reuteri viability in CMIF decreased after 60 d of storage, predominantly at higher storage humidity and temperature levels. At the end of the storage period, significant changes in the color values were observed in all CMIF, with a reduction in their greenness, an increase in yellowness, and a wide variation in their whiteness. Moreover, pH values and caking behavior of all CMIF stored at higher temperature (40°C) and humidity [water activity (a_w) = 0.52] levels were found to be significantly higher than the samples stored under other conditions. Over 30 d of storage at lower humidity conditions (a_w = 0.11 and 0.33) and room temperature (25°C), no significant increase in CMIF lipid oxidation rates was noted. Fourier-transform infrared spectroscopy analysis showed that, compared with the other storage conditions, CMIF experienced fewer changes in functional groups when stored at a_w = 0.11. Microscopic images showed typical morphological characteristics of milk powder, with round to spherical-shaped particles. Overall, camel milk fortified with encapsulated L. reuteri can be suggested as a promising alternative in infant formula industries, potentially able to maintain its physicochemical characteristics as well as viability of probiotic cells when stored at low humidity levels (a_w = 0.11) and temperature (25°C), over 60 d of storage.     

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.     

Elliptical distribution-based weight-determining method for ordered weighted averaging operators

The ordered weighted averaging (OWA) operators play a crucial role in aggregating multiple criteria evaluations into an overall assessment supporting the decision makers’ choice. One key point steps is to determine the associated weights. In this paper, we first briefly review some main methods for determining the weights by using distribution functions. Then we propose a new approach for determining OWA weights by using the regular increasing monotone quantifier. Motivated by the idea of normal distribution-based method to determine the OWA weights, we develop a method based on elliptical distributions for determining the OWA weights, and some of its desirable properties have been investigated.     

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.     

培养原料种类的减少对人工窖泥发酵剂菌群结构的影响

为实现人工窖泥发酵剂（APMSC）的品质控制，从前期开发的发酵剂配方中分别取出老熟窖泥、曲药、酒糟和黄水初步形成4个简化配方，采用扩增子测序和色谱技术比较了各配方发酵剂的微生物群落结构及香味物质含量差异。结果显示，各配方发酵剂样品中共检测到70个微生物操作分类单元（OTU），其中71.43%为各组样品共有，未检测到独有OTU。各配方样品的优势微生物均存在于其余分组中，仅物种相对丰度有所差异。根据酿酒有益微生物的丰度高低，选择了一种简化配方（无老熟窖泥作原料）发酵剂养护窖池，使酿造白酒的主体香味物质含量得到显著提高。结果表明，即使缺失了某一种培养原料，人工窖泥发酵剂的功能微生物物种也可能由其他原料补充，而原料种类的减少是实现发酵剂品质控制的主要途径。     

融合环境因素的有限空间毒气扩散危害程度分析

为探究环境因素对毒气扩散危害程度的影响,通过对比有限空间内外环境差异,了解气体在有限空间内受力情况,并以传统高斯烟羽模型为基础,考虑扩散气体与有限空间边界碰撞产生的反射作用,建立了适用于有限空间气体扩散的改进高斯烟羽模型。深度剖析有限空间气体扩散环境,挖掘气体扩散主要关联因子,构建风速、地表粗糙度对有限空间气体扩散的影响函数;参照有限空间环境特征,分别设置3种不同风速与2种不同地表粗糙度,推求6种不同环境条件下气体扩散面积,阐明风速与地表粗糙度对有限空间气体扩散的影响程度。结果表明:在单一风速条件下,风速越高,地表粗糙度越低,洞室内H2S气体滞留时间越短,洞室内气体的扩散面积越小;风速越低,地表粗糙度越高,洞室内气体的扩散面积越大。     

基于计算机控制的新型鸡精组分建模研究

以鲜鸡肉粉(富钙型)、味精、食盐、白砂糖、淀粉为主要原料,根据谷氨酸钠、呈味核苷酸二钠、总氮、水分含量的要求,利用计算机数学建模法计算出鸡精中各原料的组分。选出8组较优的方案制成鸡精,上述4种物质的含量与模型非常接近,其中第1组方案的营养价值和感官评价均最好,配方为鲜鸡肉粉14%、味精22%、食盐36%、白砂糖7%、淀粉5%。实验结果表明基于计算机控制的新型鸡精组分建模准确,具有指导意义。     

基于汉风色典的不同色差公式的色差均匀性

为给纺织行业在纺织品贸易和质量控制等方面优选色差公式提供参考,研究了3个重要的和1个最新色差公式的色差均匀性,借助国际上色样多、视觉等距性好的汉风色典,利用X-Rite测色仪测试近2万个色样的色度参数,然后分别从向度、彩度、浓度3个方向选取3万多对相邻色样,采用MatLab编程计算4种色差并统计分析其均值、不匀率和性能因子。结果表明:不同方向不同评价指标所得结果不尽相同;在色差均匀性方面,CAM16-UCS最好,CIEDE2000_(2∶1∶1)略优于CMC_(2∶1),且二者明显优于CIELAB;在色差大小方面由小到大依次为CIEDE2000_(2∶1∶1)、CMC_(2∶1)、CAM16-UCS、CIELAB。