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.     

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。     

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。     

考虑区域协调性的城际列车开行方案优化

针对城际列车开行方案没有有效匹配城市轨道交通运能的问题，提出一种考虑区域协调性的城际列车开行方案优化方法。首先，以旅客出行费用最小和铁路运输效益最大为优化目标，考虑城际列车载客能力、出发地目的地（Original Destination，OD）客流需求和通过能力等约束；然后，在此基础上增加运能匹配度的限制，构建了考虑区域协调性的城际列车开行方案多目标非线性规划模型，并设计改进的模拟退火算法求解模型；最后，以广深城际铁路为例并进行两组对比分析。实验结果表明:考虑区域协调性的列车开行方案可以使旅客出行总广义费用降低约4.06%，铁路部门的效益提高约9.58%，旅客和铁路的系统总成本降低约23.27%；与遗传算法相比，改进的模拟退火算法在求解质量与收敛速度上均有较大提高。所提模型和算法可充分兼顾旅客和铁路双方利益，能够为城际列车开行方案优化问题提供有效解决方法。     

Revisiting the Parallel Strategy for DOACROSS Loops

Journal of Computer Science and Technology - DOACROSS loops are significant parts in many important scientific and engineering applications, which are generally exploited pipeline/wave-front...     

采用随机Petri网的嵌入式机载软件可靠性检测

针对嵌入式机载软件设计中存在的典型缺陷问题，结合嵌入式机载软件任务调度特性，提出采用随机Petri网对嵌入式机载软件设计进行仿真验证的可靠性检测方法，以提高嵌入式机载软件设计的可靠性。该方法采用随机Petri网对嵌入式机载软件系统行为建模，并给出典型缺陷的检测策略和判定准则，然后通过对Petri网模型进行仿真验证，检测系统是否存在此类设计缺陷；并给出了软件设计的运行流程的仿真验证算法，以支持对相应设计的可靠性检测。通过与其他可靠性检测方法的比较，表明了该方法的有效性。     

基于种群划分与变异策略的粒子群优化算法

粒子群算法因其形式比较简洁，参数设置灵活，操作简便易行，并且能够快速收敛，从而引起广泛关注。但是传统的粒子群算法也有缺陷：收敛速度慢以及容易陷入局部最优等。针对这些问题，本文借鉴小生境的方法，在进化初始阶段，对种群进行划分，将初始种群分为子种群，对不同的子种群进行不同的变异策略；在进化过程中，针对不同的子种群，设置不同的惯性权重因子ω，用来增强全局搜索能力与局部搜索能力。实验结果表明，本文提出的算法较传统的粒子群算法具有较快的收敛性以及找寻的全局最优解更接近真实解集，收敛精度比较高。     

基于改进耦合增强随机共振的滚动轴承故障诊断

针对强背景噪声下经典随机共振方法对滚动轴承故障特征提取效果差的问题,提出了一种基于改进耦合增强随机共振的滚动轴承故障诊断方法。首先,利用一个定参双稳系统和一个变参双稳系统构成耦合随机共振系统,外部输入直接作用于定参双稳系统;然后,通过调节变参双稳系统参数和耦合系数实现耦合系统的随机共振控制,并借助遗传算法实现控制参数的自适应选取。实验和工程应用验证了所提方法的有效性和优越性。