Activated Sludge Aeration Forecasting for Stable Dissolved Oxygen
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Solution Overview
Problem
Existing methods for adjusting aeration quantity in activated sludge systems to maintain stable dissolved oxygen concentration are lagging, leading to energy waste and unstable system operation due to manual adjustments based on experience, and existing control prediction models fail to address immediate DO concentration fluctuations.
Innovation Solution
A method involving data measurement, lag compensation, and a random forest model is employed to predict aeration airflow rate, using historical data to adjust airflow rates in advance to maintain stable DO concentration, incorporating influent flow rate, organic matter, ammonia nitrogen, and sludge concentration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of aeration quantity based on experience is used, then operation simplicity is maintained, but DO concentration stability deteriorates and energy consumption increases
Solution Approach 1:
The patent replaces manual experience-based adjustment with an automated control system that uses sensors to detect DO concentration and a controller to automatically adjust aeration quantity, eliminating the need for manual intervention while improving DO concentration stability
Solution Approach 2:
The patent implements a closed-loop feedback control system where DO concentration is continuously monitored and the measured value is fed back to the controller, which then adjusts the aeration quantity accordingly to maintain stable DO levels
2Reliability
If real-time aeration adjustment is implemented, then DO concentration stability improves, but response time lag worsens
Solution Approach 1:
The patent uses historical data and machine learning models to predict future DO concentration trends and adjusts aeration quantity in advance before DO concentration actually deviates from the target range, compensating for the inherent time lag in the system
3Reliability
If excessive aeration is applied to ensure DO concentration, then DO concentration stability improves, but energy consumption increases
Solution Approach 1:
The patent applies precise partial aeration by using machine learning predictions to determine the exact aeration quantity needed, avoiding excessive aeration while ensuring DO concentration remains stable through optimized control strategies
4Reliability
If automated control systems are introduced, then DO concentration stability improves, but system complexity increases
Solution Approach 1:
The patent integrates multiple functions including DO detection, data storage, historical data analysis, prediction modeling, and control execution into a unified automated control system, managing complexity through functional integration
Data Source
AI summary
Disclosed is a method for predicting aeration quantity required to maintain a stable dissolved oxygen concentration in an activated sludge system, including measuring operating parameters of a biochemical tank of a wastewater treatment plant at intervals of a t1 time period over a certain period of time; replacing the DO concentration data with DO concentration data after a t2 time period from the time of measurement; filtering the DO concentration data after being replaced to form a dataset; building a random forest model, and building a machine learning matrix using data in the dataset to train the random forest model; evaluating prediction performance of the trained random forest model; and using the trained random forest model to predict an aeration airflow rate required to achieve the target DO concentration value after the t2 time period, thereby adjusting an aeration airflow rate.