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.     

An automated treatment planning strategy for highly noncoplanar radiotherapy arc trajectories

Radiation therapy is a technology-driven cancer treatment modality that has experienced significant advances over the last decades, due to multidisciplinary contributions that include engineering and computing. Recent technological developments allow the use of noncoplanar volumetric modulated arc therapy (VMAT), one of the most recent photon treatment techniques, in clinical practice. In this work, an automated noncoplanar arc trajectory optimization framework designed in two modular phases is presented. First, a noncoplanar beam angle optimization algorithm is used to obtain a set of noncoplanar irradiation directions. Then, anchored in these directions, an optimization strategy is proposed to compute an optimal arc trajectory. The computational experiments considered a pool of twelve difficult head-and-neck tumor cases. It was possible to observe that, for some of these cases, the optimized noncoplanar arc trajectories led to significant treatment planning quality improvements, when compared with coplanar VMAT treatment plans. Although these experiments were done in a research environment treatment planning software (matRad), the conclusions can be of interest for a clinical setting: automated procedures can simplify the current treatment workflow, produce high-quality treatment plans, making better use of human resources and allowing for unbiased comparisons between different treatment techniques.     

Numerical Investigation of the Capture of Polydisperse Particles by Charged Droplets

The capture of particles by charged droplets was simulated by considering the electrostatic interactions of droplet-droplet and droplet-particle. The results indicate that the electrostatic repulsion between droplets leads to a dynamic accumulation mode of particles. However, the droplet spacing has an insignificant effect on the capture efficiency when the electrostatic deposition predominates. The increase of droplet charge remarkably improves the capture efficiency, in which the capture of fine particles accounts for the largest proportion. Compared to the droplet charge, the droplet size shows a limited improvement in the capture efficiency. Reducing the droplet velocity prolongs the capture time instead of enhancing the capture capacity per unit time, thereby improving capture efficiency.     

基于VERICUT的偏心放置分段式齿圈铣齿方法研究

为解决大直径且齿宽较宽的分段式齿圈的铣齿问题,提出采用偏心放置的主程序调用子程序循环加工的铣齿方法。根据主程序数学模型和子程序数学模型构建偏心放置铣齿方法的数学模型,基于VERICUT的仿真模型和仿真主、子程序,实现齿槽的开粗和精铣仿真加工;通过VERICUT对比分析功能模块,以齿面残留模型为依据,验证了偏心放置铣齿方法的可行性。     

超高层劲性结构及大型机械设备立体协同施工技术

基于某超高层建筑群项目的主体施工阶段,对超高层劲性结构建筑群项目施工过程中可能遇到的问题,诸如大型机械立体协同、钢结构与钢筋连接节点优化、钢结构桁架层施工等重难点进行总结分析,并提出相应的解决方案以供探讨,可为同类型工程提供参考经验。     

Influence of Sm and Cd co-substitutions on physical,magnetic, Mössbauer,electric, and dielectric properties of Co2X hexagonal ferrites in presence of a hematite phase

X-type samarium-cadmium co-substituted hexaferrite with compositions Ba_2-xSm_xCo₂Cd_yFe_28-yO₄₆ (0.00 ≤ x ≤ 0.08, and 0 ≤ y ≤ 0.4) were prepared at 1340 °C using a simple heat treatment technique. All heated samples were characterized using FTIR, XRD, SEM, VSM, M?ssbauer, and low-frequency dielectric measurements. XRD analysis of prepared samples shows the formation of X as a major phase along with hematite. The M_S value varied from 67.01 Am²/kg to 50.43 Am²/kg; whereas the H_c value changed from 2.95 kA/m to 6.17 kA/m, A high value of M_S (67.01 Am²/kg) is observed in the pure sample, and a very low value of H_c (2.95 kA/m) is observed for x = 0.06, y = 0.3 compositions, but Mr/Ms < 0.5 confirm the multi-domain nature of prepared hexaferrites. Hysteresis loops of all samples are narrow, and confirmed that formed samples belong to magnetically soft. Mössbauer spectra of the three samples (S1, S3, and S5) show the existence of doublets. Significantly low values of coercivity, retentivity, and loss tangent in Sm–Cd substituted samples signified those prepared materials can be used to design electromagnets, transformer cores, electric motors, and maybe a potential candidate for lossless low-frequency applications.     

A globally convergent BFGS method for pseudo-monotone variational inequality problems

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm.     

A design method of data analytics process for condition based maintenance

Many condition monitoring systems use data analytics processes such as anomaly detection to understand machine conditions. Such data analytics processes have been designed by data scientists. However, domain knowledge is indispensable for designing the process, and data scientists have difficulty in acquiring such knowledge from domain engineers. This paper proposes a design method of data analytics processes and an engineering tool. In this method, data scientists propose hypotheses about each step of the process to domain engineers. Then, data scientists update the process on the basis of feedback from domain engineers. The engineering tool helps data scientists to interact with domain engineers.     

基于孪生支持向量回归机的转炉炼钢终点预测

为了提高转炉炼钢的终点命中率，建立了一种新的转炉终点预测模型，实现了对转炉终点碳质量分数和温度的准确预测。模型采用K最近邻孪生支持向量机(KNNWTSVR)算法，将权重矩阵引入到目标函数中，并利用鲸群优化算法进行求解，提高了传统算法的性能；然后基于某炼钢厂260 t转炉的实际生产数据，建立了转炉炼钢终点预测模型。结果表明，预测模型的终点碳质量分数(误差±0.005%)和温度(误差±15 ℃)的终点单命中率分别为94%和88%，双命中率达到84%。与其他两种现有的建模方法相比，本模型取得了最优的预测效果。该方法满足转炉炼钢实际生产的需求，也可适用于钢铁冶金其他领域的数学建模。