Nanjing University

Haidong Hu

Hongqiang Ren

Bing Wu

Jinfeng Wang

Kewei Liao

Lili Ding

Jinju Geng

Deyuan Dong

Ke Xu

Hui Huang

XuXiang Zhang

Jin Wang

Sijia Ma

Yuanji Shi

Xian Cui

A deep learning-based method for predicting the settleability of activated sludge includes: (1) collecting a plurality of activated sludge samples, acquiring raw data of images of the plurality of activated sludge samples, cleaning the raw data of the images, and standardizing data sizes of the images; (2) calculating a sludge volume index (SVI) for each of the plurality of activated sludge samples; (3) establishing, by using a ResNet50 deep neural network, a model for predicting the settleability of activated sludge; and (4) predicting the settleability of a target activated sludge using the model established in (3).

Deep learning-based method for predicting settleability of activated sludge

A device for in-situ measuring settleability of activated sludge includes a sample chamber, an ultrasonic time domain reflectometer, a magnetic stirrer, an ultrasonic probe, a sample inlet, a sample outlet, and a stagnant zone. The sample chamber is configured to hold an activated sludge sample, and the ultrasonic time domain reflectometer is configured to measure the settling characteristics of the activated sludge. The stagnant zone is disposed in the sample chamber. The sample inlet and the sample outlet communicate with the sample chamber. The stagnant zone includes a top part, a sidewall, and a bottom part. The sample inlet is connected to the top part of the stagnant zone. The sample outlet is connected to the sidewall of the stagnant zone. The magnetic stirrer is disposed at the bottom part of the stagnant zone. The ultrasonic probe is disposed on the top part of the stagnant zone.

Device and method for measuring settleability of activated sludge in-situ

A method for collaborative optimization control method for organic nitrogen and inorganic nitrogen in a denitrification process is provided. The method includes: establishing ASM-mDON-DIN models for simultaneous simulation of microbial dissolved organic nitrogen (mDON) and inorganic nitrogen (DIN) in denitrification processes; and selecting a corresponding ASM-mDON-DIN model according to a set carbon/nitrogen ratio to collaboratively optimize the concentration values of mDON and DIN in the effluent in the denitrification process, to obtain best process operation parameter values.

Method for collaborative control of organic nitrogen and inorganic nitrogen in denitrification process

A system for cleaning wastewater, includes: an absorption-biodegradation-denitrification (ABN) reactor, a sequential adsorption reactor, a disinfection reactor, and a sludge anaerobic fermentation reactor. The ABN reactor is an integrated reactor including: a biosorption tank, an intermediate sedimentation tank, a biologically-enhanced degradation tank, a denitrification biofilter, and a secondary sedimentation tank. The pretreated wastewater is introduced into the ABN reactor for removal of chemical oxygen demand, nitrogen and phosphorus; the ABN reactor effluent is introduced into the sequential adsorption reactor for the removal of high-risk pollutants; the sequential adsorption reactor effluent is introduced into the disinfection reactor for the elimination of viruses and other pathogenic microorganisms; the sludge produced by the ABN reactor is introduced into the anaerobic sludge fermentation reactor for alkaline fermentation. The system is effective for removing high-risk pollutants and reducing effluent toxicity, which can be used for the upgrading and reconstruction of the wastewater treatment system.

System for cleaning wastewater

The disclosure provides a method for evaluation of removal of nitrogen-containing organic matter from the wastewater. The method includes: 1) pretreating a wastewater sample from a wastewater treatment plant; enriching nitrogen-containing organic matter in the wastewater sample with a solid-phase extraction cartridge; separating the nitrogen-containing organic matter from a substrate and disruptors of the wastewater sample, and collecting the nitrogen-containing organic matter; 2) detecting and analyzing the nitrogen-containing organic matter collected in 1) with a Fourier-transform ion cyclotron resonance mass spectrometer, thereby obtaining mass spectra of the nitrogen-containing organic matter; 3) preprocessing peak data of the mass spectra of the nitrogen-containing organic matter in each wastewater sample; setting the nitrogen-containing organic matter corresponding to the peak data as a global variable; arranging wastewater samples into cross-sectional data according to wastewater treatment processes; creating an assessment matrix for evaluating removal of the nitrogen-containing organic matter.

Method for evaluating removal of nitrogen-containing organic matter in wastewater

A method for identifying and analyzing dissolved organic nitrogen of different sources in wastewater includes extracting DON in wastewater to obtain a DON extract, detecting mass spectrum peaks in the DON extract, pre-processing the spectral data of the wastewater sample; constructing a network relationship of the substance reaction in the wastewater sample; screening the substance reaction relationship of DON; and determining different sources of DON.

Method for identifying and analyzing dissolved organic nitrogen of different sources in wastewater and application of the method

A method of measuring concentration of dissolved organic nitrogen in sewage. The method includes: filtering a sewage sample using a filter membrane; measuring the concentrations of total dissolved nitrogen (TDN), ammonia nitrogen (NH), and nitric nitrogen (NO) in the sewage sample, respectively designated as C, Cand C; calculating the ratios of (C+C)/Cand C/C, and according to the ratios, calculating the concentration of dissolved organic nitrogen (DON) in the sewage sample.

Method of measuring concentration of dissolved organic nitrogen in sewage

A denitrification biofilter device, including: a regulating pool, a reactor body, a water tank, a first doser, a second doser, a backwash pump, a water inlet pump, and a blower. The regulating pool includes a raw water inlet and a water outlet. The reactor body includes a uniform water distributor, a filler layer, a buffer layer, a filter layer, a supporting layer, a filler supporting plate, a backwash water outlet, a treated water outlet, and a backwash water inlet. The treated water outlet and the backwash water inlet are disposed at the bottom of the reactor body and are connected to the water tank via pipes. The filler layer is filled with zeolites having a grain size of between 4 and 8 mm, a density of between 1.9 and 2.6 g/cm, a porosity greater or equal to 48%, and a specific surface area of between 570 and 670 m/g.

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Nanjing, China

Haidong Hu