Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

面向分拣机器人的珍珠形状视觉检测方法

针对人工进行珍珠形状分拣效率低、精度不稳定等问题,提出基于机器视觉的珍珠形状检测方法;采用背光成像方式消除珍珠表面纹理和光泽的影响,对获取的珍珠图像进行同态滤波等预处理算法,提高图像对比度;为了解决相互接触珍珠影响珍珠轮廓提取的问题,采用分水岭算法对珍珠图像进行分割,得到了独立存在的珍珠个体,再通过连通域标记、质心算法对珍珠进行定位;根据国家标准对珍珠形状的规定,基于珍珠图像信息建立珍珠形状参数模型,对珍珠形状进行量化;实验结果表明,不同形状的珍珠样本的检测误差为0.63%,形状统计精度为100%,算法耗时24 ms;该方法可准确高效地对珍珠进行分拣分级,具有一定的实用价值。     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

微型土压平衡盾构机液压系统故障分析

以微型土压平衡(Earth Pressure Balance,EPB)盾构机推进液压系统作为研究对象,介绍了推进系统的工作原理,对混入空气后的油液黏度和有效体积弹性模量进行分析,建立了推进系统数学模型,并对混入不同百分比空气的系统进行仿真运算,得到了液压缸位移响应和速度响应随系统含气量变化的关系。研究表明:随着液压系统空气含量的增加,液压缸位移响应发生迟滞,速度响应变慢,液压缸在运动时产生振动。结合各液压元件工作原理,依次对实际盾构机推进系统元件进行排气,最终排除了液压系统中混入的空气,为实现盾构机平稳推进提供了理论依据。     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

我国中空吹塑行业发展现状及“十四五”期间重点产品、工艺和设备发展方向

从原料、中空成型机和吹塑工艺等方面介绍了我国中空吹塑行业发展现状，并对电子化学品专用超洁净桶、高压储氢四型瓶、全电动中空成型机、微发泡中空成型技术和挤吹聚对苯二甲酸乙二醇酯（PET）容器等在“十四五”期间的重点产品、工艺和设备发展方向进行了展望。     

Influence of thermal runaway in styrene–acrylonitrile bulk copolymerization revealed by computational fluid dynamics modeling

The computational fluid dynamics (CFD) and kinetic-based moment methods coupled approach is adopted to simulate the bulk copolymerization of styrene–acrylonitrile (SAN) in a stirred tank reactor. Numerical simulations are carried out to investigate the impacts of impeller speed, monomer ratio, initiator ratio, and initial reaction temperature on the copolymerization process and product properties. Particularly, the Chaos theory is selected as a criterion for evaluating the occurrence of the thermal runaway. The Flory's and Stockmayer's distributions are employed to calculate chain length distribution and copolymer composition distribution of copolymer. The simulation results highlight that the appearance of thermal runaway can be postponed by properly increasing the rotation speed, decreasing the initiator loadings, initial acrylonitrile contents and initial reactor temperature. Furthermore, significant differences exist in the product properties that predicted by the ideal and non-ideal models, which demonstrates that the temperature heterogeneity plays a crucial role in SAN copolymerization. This study could offer references for the safe operation and design of polymerization processes.     

On the application of remote sensing towards the estimation of cultivated land lost to urbanization

In this research work, a 40-km² SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition ® program’s Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier.     

A double laser filtering approach to vibration noise rejection for hot-rolled strip flatness measurement

This paper proposes a new filtering method based on the Kalman filtering algorithm for hot-rolled strip flatness measurement system. The system involves processing slowly changing signal, which can be considered as a bounded random process, and its model parameters are completely unknown. The noise rejection strategy in double lasers can generate a compensation signal. Since the initial and accumulative error would lead to negative filter effect or even cause divergence, Kalman filter is integrated to effectively deal with the initial error and enhance convergence. In this setting, the noise rejection strategy is used as a prediction model to constitute a similar Kalman filter. The correlated error caused by measurement error is coped with by a compensation model based on the feature of correlated error to enhance the filter effect. Both theoretical analysis and simulations show that the new algorithm has a better filter effect than the traditional Kalman filtering algorithm for the system.