| CPC C02F 3/12 (2013.01) [C02F 3/006 (2013.01); C02F 2209/005 (2013.01); C02F 2209/14 (2013.01); C02F 2209/225 (2013.01); C02F 2209/38 (2013.01); C02F 2209/40 (2013.01)] | 4 Claims |
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1. A method for predicting aeration quantity required to maintain a stable dissolved oxygen concentration in an activated sludge system, comprising the following steps:
step 1: measuring and recording influent flow rate, influent organic matter concentration, influent ammonia nitrogen concentration, dissolved oxygen (DO) concentration in the activated sludge system, sludge concentration, and aeration airflow rate in a biochemical tank of a wastewater treatment plant at intervals of a t1 time period over a certain period of time, wherein the sludge concentration is suspended solid concentration in the activated sludge system;
step 2: in view of lag effect in changes in the DO concentration in the activated sludge system, replacing the DO concentration data recorded at a time of measurement in the step 1 with DO concentration data after a t2 time period from the time of measurement;
step 3: filtering the DO concentration data after being replaced in the step 2, deleting the data that falls outside a normal range of DO concentration, and forming a dataset by combining the filtered DO concentration data with the influent flow rate, the influent organic matter concentration, the influent ammonia nitrogen concentration, the DO concentration in the activated sludge system, the sludge concentration, and the aeration airflow rate measured in the step 1;
step 4: building a random forest model, building a machine learning matrix using data in the dataset, dividing the machine learning matrix into a training set and a test set, training the random forest model, and evaluating prediction performance of the random forest model; and
step 5: taking a preset DO concentration required to be reached after the t2 time period at a current time as a target DO concentration value, inputting the target DO concentration value and influent flow rate, influent organic matter concentration, influent ammonia nitrogen concentration, and sludge concentration measured at the current time into the trained random forest model to predict an aeration airflow rate required to achieve the target DO concentration value, and adjusting an aeration airflow rate of an aeration blower according to the aeration airflow rate;
wherein the machine learning matrix in the step 4 is expressed as follows:
 in the matrix, FL1-FLm are data of aeration airflow rate, COD1-CODm are data of influent chemical oxygen demand, intFL1-intFLm are data of influent flow rate, N1-Nm are data of influent ammonia nitrogen concentration, DOt-1-DOt-m are DO concentration data in the activated sludge system, SS1-SSm are data of sludge concentration, and m denotes a total number of records taken at intervals of a t1 time period over a certain period of time;
a method for evaluating prediction performance in the step 4 is as follows: using an error between a predicted value and a measured value to characterize the prediction performance of the random forest model by calculating mean absolute percentage error (MAPE) and coefficient of determination (R2) of the trained random forest model on the test set; and when R2 is greater than 0.8 and MAPE is less than 20%, the random forest model is considered to have good prediction performance, otherwise, the DO concentration data in the dataset needs to be further replaced;
a method for further replacing the DO concentration data in the dataset is as follows: adjusting values of the t2 time period and the normal range of DO concentration, replacing the DO concentration data at a time of measurement in the dataset with DO concentration data after an adjusted t2 time period from the time of measurement, and using the dataset to build a machine learning matrix again, so as to train the random forest model again to have the good prediction performance;
the adjusted t2 time period represents lag feedback time for the changes in the DO concentration in the activated sludge system, and a value range thereof is 5-30 min and is an integer multiple of the t1 time period; and
in the step 5, the aeration airflow rate of the aeration blower is adjusted in advance to reach the required aeration airflow rate predicted by the trained random forest model, under a condition that a directly monitored value of DO concentration in the activated sludge system exhibits significant fluctuations or is about to exceed a normal range of a target value, such that the DO concentration after the t2 time period reaches the target value.
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