基于工业生产数据的协同熔炼过程智能感知与优化控制技术科学数据集

本数据集基于铅基固废协同冶炼的工业生产场景，面向工业大数据深度学习研究，利用集散控制系统保存的历史数据和化验分析的历史数据建立熔炼炉的数据驱动模型，实现对系统关键运行指标的实时预测与评估。历史数据采用美国GE公司的iFX60 SCADA数据采集软件，数据采集协议统一为OPC协议，课题从中采集温度、压力、流量等过程量。分析化验历史数据采集从分析化验管理系统以Excel文件形式导出，主要包含球料、高铅渣等成分数据。This dataset is developed for industrial big data deep learning research, based on the industrial production scenario of co-smelting of lead-based solid wastes. It utilizes historical data stored in the Distributed Control System (DCS) and laboratory assay historical data to build a data-driven model for the smelting furnace, enabling real-time prediction and evaluation of the system's key operating indicators. The historical process data was collected using the iFX60 SCADA data acquisition software from General Electric (GE) of the United States, with the OPC protocol as the unified data acquisition protocol, and process variables such as temperature, pressure and flow rate were collected via this system. Laboratory assay historical data was exported in the form of Excel files from the laboratory analysis management system, mainly containing composition data of pellet feed, high-lead slag and other relevant materials.

搜集汇总

背景与挑战

背景概述

该数据集基于铅基固废协同冶炼的工业生产场景，面向工业大数据深度学习研究，利用历史数据建立熔炼炉数据驱动模型，实现对系统关键运行指标的实时预测与评估。数据来源于集散控制系统采集的温度、压力、流量等过程量，以及分析化验管理系统导出的成分数据。