Process simulation is a critical cornerstone of chemical engineering design. Current automated chemical design methodologies focus mainly on various representations of process flow diagrams. However, transforming these diagrams into executable simulation flowsheets remains a time-consuming and labor-intensive endeavor, requiring extensive manual parameter configuration within simulation software. In this work, we propose a novel multi-agent workflow that leverages the semantic understanding capabilities of large language models(LLMs) and enables iterative interactions with chemical process simulation software, achieving end-to-end automated simulation from textual process specifications to computationally validated software configurations for design enhancement. Our approach integrates four specialized agents responsible for task understanding, topology generation, parameter configuration, and evaluation analysis, respectively, coupled with Enhanced Monte Carlo Tree Search to accurately interpret semantics and robustly generate configurations. Evaluated on Simona, a large-scale process description dataset, our method achieves a 31. 1% improvement in the simulation convergence rate compared to state-of-the-art baselines and reduces the design time by 89. 0% compared to the expert manual design. This work demonstrates the potential of AI-assisted chemical process design, which bridges the gap between conceptual design and practical implementation. Our workflow is applicable to diverse process-oriented industries, including pharmaceuticals, petrochemicals, food processing, and manufacturing, offering a generalizable solution for automated process design.

Due to the interference of background noise, existing video anomaly detection methods are prone to detect some normal events in complex scenes as anomalies. Meanwhile, we note that the diversity of normal patterns has not been adequately considered, i.e., the normal events that are worthy of reference in the test data have not been properly utilized, which raises the risk of missed and false detections. In this work, we combine the tasks of next-frame prediction and predicted-frame reconstruction to propose a noise-resistant video anomaly detection method. For the prediction task, we develop an RGB Error-Guided Multiscale Predictive Coding (EG-MPC) framework to overcome the interference of background noise on the learning of appearance and motion features of objects at various scales, thus achieving high-quality frame prediction. For the reconstruction task, we introduce the Dynamic Memory Modules (DMMs) into the reconstruction network, and design sparse aggregation and selective update strategies for the memory items in the DMMs to effectively represent diverse normal patterns while increasing the difficulty of reconstructing anomalies, thus making it easier to distinguish between normal and abnormal frames. Extensive experiments on four benchmark datasets demonstrate that our proposed method outperforms state-of-the-art approaches, especially in complex scenarios.

In video anomaly detection task, to mitigate the interference of background noise on learning the appearance and motion features of foreground objects, existing object-centric methods often directly disregard scene information, making it challenging for them to detect scene-dependent anomalies. Moreover, we observe that most methods focus on reconstructing or predicting complete frame-level or object-level RGB information, which limits their inference speed. In this work, we propose a novel inter-frame RGB difference reconstruction network for efficient video anomaly detection. Specifically, we construct separate scene-dependent memory banks (SDMBs) for different scenes to store exclusive normal patterns, thus enabling sensitive detection of scene-dependent anomalies. Meanwhile, we design sparse aggregation and similarity-driven updating mechanisms for the memory items in the SDMBs, which effectively increase the reconstruction error of anomalies by adequately learning the diverse normal patterns in both the training and testing data, thus making the distinction between normal and abnormal frames easier. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches in terms of detection accuracy, false negative rate, and inference speed, particularly in situations with more complex scene and event types.