基于焦化废水水质分析与深度处理技术的智能加药控制数据集

通过对焦化废水水质关键参数的感知分析，建立多种工况类型，融合专家经验知识和化学机理知识，形成不同工况下模式自适应匹配的药剂投加优化设定与控制策略；构建加药处理性能评估方法，在线实时反馈加药处理质量，形成“前馈+反馈”复合智能控制模式，实现废水处理加药的动态优化控制，满足处理效果最佳和节省药剂成本的双重效果。This work first conducts perceptual analysis of key water quality parameters of coking wastewater to establish multiple working condition types. Then, by integrating expert empirical knowledge and chemical mechanism knowledge, optimized dosing settings and control strategies with adaptive pattern matching for different working conditions are formulated. Next, a performance evaluation method for the dosing treatment process is developed to provide real-time online feedback on treatment quality, establishing a hybrid 'feedforward + feedback' intelligent control mode. Finally, dynamic optimal control of chemical dosing in wastewater treatment is realized, achieving the dual goals of optimal treatment effect and reduced chemical agent costs.

搜集汇总

背景与挑战

背景概述

该数据集专注于焦化废水处理的智能加药控制，通过分析水质关键参数并融合专家经验与化学机理知识，构建自适应工况的药剂投加优化策略。它应用于煤焦和化工企业的废水处理环节，采用“前馈+反馈”复合控制模式，旨在实现动态优化控制，以达到最佳处理效果并节省药剂成本。数据以Excel格式提供，技术核心包括智能控制和自动加药。