工况动态优化感知数据集
收藏资源简介：
本项目针对传统DCS系统在复杂工况下的动态控制不足问题，通过深度融合人工智能技术，构建覆盖“智能感知-决策-执行”全链路的AI智能控制系统。核心建设内容包括: 1.硬件升级:部署高性能AI服务器(含NVIDIAA4000GPU)、冗余存储与网络设备，支持1000个工业参数的实时采集与处理; 2.AI模型开发:基于LSTM循环神经网络，结合注意力机制，实现原料油酸价、含磷量波动预测(误差≤工艺阈值)及加碱量动态优化; 3.系统集成:通过OPC协议与DCS系统无缝对接，支持PID参数自适应调节，形成“数据采集-模型推理-指令下发”闭环控制; 4.故障管理:设计分层级故障预判与应急机制(感知层数据校验、决策层模型热切换、系统级冗余链路)，保障7x24小时连续运行。项目建成后，可实现药剂添加量精准控制，预计降低生产能耗15%，提升脱酸效率至98%以上，为化工行业数字化转型提供标杆示范。
This project addresses the insufficient dynamic control of traditional Distributed Control Systems (DCS) under complex operating conditions, and constructs an AI intelligent control system covering the entire "intelligent perception-decision-execution" pipeline by deeply integrating artificial intelligence technologies. The core construction contents include: 1. Hardware Upgrade: Deploy high-performance AI servers (equipped with NVIDIA A4000 GPUs), redundant storage and network equipment, which supports real-time collection and processing of 1000 industrial parameters; 2. AI Model Development: Based on Long Short-Term Memory (LSTM) recurrent neural networks and integrated with attention mechanism, realize the fluctuation prediction of raw material acid value and phosphorus content (error ≤ process threshold) and dynamic optimization of alkali dosage; 3. System Integration: Seamlessly connect with the DCS system via the OPC protocol, support adaptive adjustment of PID parameters, and form a closed-loop control process of "data collection-model inference-command issuance"; 4. Fault Management: Design a hierarchical fault prediction and emergency response mechanism (perception layer data verification, decision layer model hot swapping, system-level redundant links) to ensure 7x24 hours of uninterrupted operation. Upon completion, the project will achieve precise control of chemical addition amount, reduce production energy consumption by 15%, increase deacidification efficiency to over 98%, and provide a benchmark demonstration for the digital transformation of the chemical industry.
