In the coal-to-ethylene glycol (CTEG) process, precisely estimating quality variables is crucial for process monitoring, optimization, and control. A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables, which often incurs substantial monetary costs and significant time delays. The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models. To address this issue, our study introduces the transferable adversarial slow feature extraction network (TASF-Net), an innovative approach designed specifically for few-shot quality prediction in the CTEG process. TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework, effectively capturing the nonlinear and inertial characteristics of the CTEG process. Additionally, the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step. A key strength of TASF-Net lies in its ability to navigate the complex measurement noise, outliers, and system interference typical in CTEG data. Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly. Furthermore, an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain. The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset. Compared with some state-of-the-art methods, TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process. (c) 2024 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Process industries rely on inferential models to provide real-time estimates of quality variables. Among the various types of inferential models, the slowness preference approach stands out as effective in mimicking chemical processes by encapsulating system inertia. This study proposes a new deep generative model, grounded in Bayesian principles, which leverages the slowness representation to identify slow and nonlinear patterns in sequential industrial data. Notably, the model characterizes a variable attention mechanism that adaptively identifies quality-related input variables. Moreover, a min–max game theoretical adversarial learning strategy is designed to enhance the model's robustness and the ability to effectively approximate the real data distribution. The mathematical formulation of the model is presented within a semi-supervised framework, accommodating scenarios with limited labeled data. Finally, this study unequivocally showcases the superior performance of the proposed model in predicting carbon monoxide content in the recycled gas using data from a real coal-to-ethylene glycol process. © 2024 Elsevier Ltd

A side-reactor column (SRC) configuration, com-prising a vacuum column coupled with atmospheric side reactors, is proposed to overcome the thermodynamic restriction in the esterification of cyclohexene with acetic acid to produce cyclohexyl acetate. Meantime, this configuration can avoid the utilization of the high-pressure steam and provide enough zone for catalyst loading. In order to obtain the minimum total annual cost (TAC), the process is optimized by a mixed-integer nonlinear program-ming optimization method based on the improved bat algorithm. The results indicate that the optimized SRC configuration saves about 44.81% of the TAC compared to the reactive distillation process. Based on the optimized SRC process, dynamic control is carried out. The dual-point temperature and temperature -composition control structures are proposed to reject throughput and feed composition disturbances. The dynamic performances demonstrate that the temperature-composition control structure is better in maintaining product purity.

Soft sensor has been extensively applied for online estimation of the key quality variables in modern industrial processes, which is extremely important for the process to achieve efficient monitoring and smooth control. To build an accurate data-driven model, the dynamic correlation and strong nonlinearity of process sequential data must be considered in soft sensor modeling. Therefore, a dynamic feature regression network (DFR) is proposed in this paper for industrial soft sensor modeling, consisting of a dynamic feature extraction network and a feature regression network to explore different industrial data features. First, the dynamic feature extraction network maps time series samples to a set of hidden dynamic features, while the feature regression network performs deeper feature extraction and output regression on key quality variables. Furthermore, since both networks consist of unsupervised feature extraction and supervised feature regression, a large scale of unlabeled samples can be utilized in the semisupervised learning of model parameters. Finally, the feasibility and efficacy of the proposed model are verified through the coal-to-ethylene glycol process data to predict the carbon monoxide content. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

机器人作为机电一体化技术的最高集成应用,是衡量一个国家自动化水平的重要标志。一般来说,可操作机器人是按照能够重复编程且实现自动控制,并应用于相关自动化系统中的机器人,是工业机器人中科技水平和含量较高的典型代表,特别是在轨迹跟踪过程中遇到的相关内容是目前亟待解决的关键问题,极具研究价值和意义。鉴于此,本文以可操作焊接机器人为研究对象,分析轨迹跟踪过程中的关键问题及解决方法,提出了有效的解决方法并开展了相关问题的分析与研究工作。本文的主要研究内容如下:（1）针对可操作机器人工作环境中的不确定性因素,特别是变参数、变负载等问题,使用智能控制方法,设计并提出了新型抗干扰滑模控制器,结合低通滤波器的特性,实现了输入信号的有效过滤,实现了机器人的有效控制,并最终应用于可操作机器人的轨迹跟踪过程中,优化了输入扭矩,提升了轨迹跟踪精度。实验表明本方法提升可操作机器人轨迹跟踪的力控制特性和跟踪性能。（2）针对可操作机器人的关键零部件以及连杆存在的柔性特征问题,首先优化机器人刚柔耦合动力学方程,建立新型刚柔耦合动力学方程,提出了新型抗柔性鲁棒控制器,使用扩张状态观测器实现了变量的有效观测,降低了由于柔性特征而存在的轨迹跟踪误差,提升了可操作机器人的动态品质。在整个研究过程中,实现了连续观测和连续控制,为下一步的碰撞检测过程和柔性振动抑制研究提供了良好的基础。（3）针对可操作机器人人机交互过程中的碰撞检测和避碰规划的关键问题,主要考虑在主动碰撞模式和不同接触位置的情况下,采用阻抗控制器和带通滤波器相结合的方式,提出了新型的碰撞检测控制器,实现有效的碰撞检测和避碰规划。在轨迹跟踪过程中实现了PID控制器和碰撞检测控制器的切换,有效避免了碰撞问题给机器人轨迹跟踪过程中带来的影响,最终提升了可操作机器人与外部的交互能力。（4）针对运动规划的过程中可操作机器人存在的柔性振动问题,通过傅里叶级数作为轨迹基函数,分析可操作机器人实际运动轨迹在时域和频域内的特性以及由此特性引起的对关节振动存在的影响,提出了基于Pontryagin最大值原理和微分方程BVP求解极值状态的新方法,最终实现动态补偿。为减少机器人运动过程中前馈转矩中的高频谐波分量对机器人轨迹跟踪带来的影响,通过离散轨迹点拟合的方法实现了最优控制。有效的降低了柔性关节在轨迹跟踪过程中出现的振动现象和关节变形量,提升了轨迹跟踪精度和动态品质。综上所述,本文主要对可操作机器人轨迹跟踪过程中的智能鲁棒控制器、刚柔耦合状态下的抗柔性控制、人机环境中的碰撞检测和避碰规划、柔性关节下的振动抑制问题进行了系统而有效的研究。通过应用相关的控制理论,提出了新型控制器并在实验平台上进行了有效的验证。最终通过实验研究,验证了所提方法的有效性和实用性,实现了可操作机器人轨迹跟踪过程中精度提高和动态品质提升的目标。

为了解决乙酸乙酯生产过程中转化率低的问题，提出渗透汽化-酯化反应耦合技术。首先利用Aspen Custom Modeler （ACM）软件建立内置式连续性渗化膜反应器（PVMR）模型并进行验证，然后利用Aspen Plus考察反应温度、进料酸醇比、膜面积与反应液体积比对PVMR过程性能的影响。结果表明，乙醇转化率增量与反应温度之间呈正相关性；随着进料酸醇比增大，乙醇转化率增量呈先增大后减小趋势；增加膜面积与反应液体积比能促进PVMR性能。在反应温度90℃，进料酸醇比为2，膜面积与反应液体积比为100 m-1时，PVMR过程乙醇转化率为82.4%。研究结果为PVMR生产乙酸乙酯节能集成工艺开发提供基础和参考。

本文针对工业过程中最常用的间歇补料反应过程,利用间歇过程的重复性和二维动态特性,以二维系统理论为基础,二维系统的状态空间模型,分析迭代学习控制的二维特性。将传统的PI控制器作为内环结构,使用迭代学习控制器优化其参考轨迹,得到基于二维系统的迭代学习PI控制方法（ILC-PI方法）,并通过稳定性分析确定控制器参数选择方式。将ILC-PI控制方法应用于一类多批次间歇补料反应过程中,结果表明,虽然初始收敛速率较低,但是随着批次不断优化,最终的跟踪性能较好。

针对PTA生产系统中副产物醋酸甲酯水解工艺,研究基于最小经济成本的集成优化与厂级动态控制。首先根据醋酸甲酯水解工艺动力学机理模型,建立Aspen稳态模拟系统,采用灵敏度分析计算待优化变量的可行域,建立经济优化总成本目标函数;然后