基于焦化废水水质氧化动态的精准曝气控制数据集

通过焦化废水处理感知-决策-评估综合智能曝气控制策略流程通过对焦化废水处理系统入水特征（如流量、温度、BOD、色度、pH等）的识别与划分实现入水感知；利用机器学习智能预测算法，基于焦化废水处理厂的历史运行数据挖掘过程知识，建立系统处理能力、电能消耗、入水特征与曝气控制策略的预测模型，结合启发式智能算法针对不同入水情况推理决策最优曝气策略；面向焦化废水处理厂处理需求、处理出水平稳性、处理出水水质关键指标与处理电能消耗等关键运行参数搭建综合评估体系，完善感知-决策-评估综合智能曝气系统自主更新性能，实现焦化废水处理供氧曝气策略的智能优化推理决策，满足焦化废水处理供氧曝气过程“保质降耗”的优化目标。The integrated perception-decision-evaluation intelligent aeration control strategy for coking wastewater treatment achieves influent perception by identifying and classifying the influent characteristics of the coking wastewater treatment system, including flow rate, temperature, BOD, chromaticity, pH, etc. Leveraging machine learning-based intelligent prediction algorithms, it mines process knowledge from historical operational data of coking wastewater treatment plants, establishes prediction models that correlate system treatment capacity, energy consumption, influent characteristics and aeration control strategies, and combines heuristic intelligent algorithms to infer and determine the optimal aeration strategy for different influent conditions. A comprehensive evaluation system is built targeting key operational parameters such as the treatment demands of coking wastewater treatment plants, effluent quality stability, key effluent water quality indicators and treatment energy consumption, to enhance the autonomous performance update capability of the integrated perception-decision-evaluation intelligent aeration system, realize intelligent optimal inference and decision-making for the oxygen supply aeration strategy of coking wastewater treatment, and meet the optimization goal of 'quality assurance and consumption reduction' in the oxygen supply aeration process of coking wastewater treatment.

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过程中的精准曝气控制，通过机器学习算法和智能决策系统，基于废水入水特征和历史运行数据，优化曝气策略以实现水质稳定和能耗降低。数据集以Excel格式提供，应用于煤焦和化工企业的废水处理场景，旨在提升曝气系统的智能化水平，达到‘保质降耗’的优化目标。