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.

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。

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

A side-reactor column (SRC) configuration, which integrates vacuum distillation with atmospheric reaction, was developed for dimethyl adipate (DMA) production. The sequential optimization method was applied to optimize the design parameters including side stream drawn stage, reactor numbers, and catalyst loading with total annual cost (TAC) as objective function in order to realize the economical design. The best economic performance was achieved as the result of the specific SRC configuration featuring one column with 20 stages where DMA was drawn from the 5th stage and one side-reactor was equipped in the bottom. Moreover, a proportion-temperature cascade control structure was successfully developed which avoids the disturbance of inlet flow rate and composition.

针对乳酸与甲醇酯化反应体系中乳酸和甲醇的沸点分别为最高沸点与最低沸点,属于反应精馏设计中最劣沸点序列,导致乳酸甲酯反应精馏过程反应段效率低下的问题,设计了乳酸过量进料的反应精馏合成乳酸甲酯工艺。以年度总费用(TAC)为目标,

针对欠驱动桥式吊车如何在有效载荷情况下快速定位和消摆这一问题,笔者开展桥式吊车系统数学模型和消摆控制策略的研究,设计一种新型的反义RNA膜计算(aRNA-MC)优化算法,并将其分别用于最优比例积分微分(PID)控制器和最优滑模控制器的参