Research on soft measurement of ammonia nitrogen in wastewater treatment based on dynamic optimization modular neural network

2021 ◽  
Author(s):  
Chao Lu ◽  
SanYi Li
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Dynamic Optimization ◽  
Ammonia Nitrogen ◽  
Modular Neural Network ◽  
Soft Measurement

scholarly journals Prediction of BOD Concentration in Wastewater Treatment Process Using a Modular Neural Network in Combination with the Weather Condition

2020 ◽  
Vol 10 (21) ◽  
pp. 7477
Author(s):  
Wenjing Li ◽  
Junkai Zhang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Treatment Process ◽  
Weather Condition ◽  
Oxygen Demand ◽  
Wastewater Treatment Process ◽  
Training Time ◽  
Modular Neural Network

Since weather has a huge impact on the wastewater treatment process (WWTP), the prediction accuracy for the Biochemical Oxygen Demand (BOD) concentration in WWTP would degenerate if using only one single artificial neural network as the model for soft measurement method. Aiming to solve this problem, the present study proposes a novel hybrid scheme using a modular neural network (MNN) combining with the factor of weather condition. First, discriminative features among different weather groups are selected to ensure a high accuracy for sample clustering based on weather conditions. Second, the samples are clustered based on a density-based clustering algorithm using the discriminative features. Third, the clustered samples are input to each module in MNN, with the auxiliary variables correlated with BOD prediction input to the corresponding model. Finally, a constructive radial basis function neural network with the error-correction algorithm is used as the model for each subnetwork to predict BOD concentration. The proposed scheme is evaluated on a standard wastewater treatment platform—Benchmark Simulation Model 1 (BSM1). Experimental results demonstrate the performance improvement of the proposed scheme on the prediction accuracy for BOD concentration in WWTP. Besides, the training time is shortened and the network structure is compact.

Neural-Network Approach to Dynamic Optimization of Batch Distillation

2003 ◽  
Vol 81 (3) ◽  
pp. 393-401 ◽  
Author(s):  
M.A. Greaves ◽  
I.M. Mujtaba ◽  
M. Barolo ◽  
A. Trotta ◽  
M.A. Hussain
Keyword(s):  
Neural Network ◽  
Dynamic Optimization ◽  
Network Approach ◽  
Batch Distillation ◽  
Neural Network Approach

High-strength nitrogen removal of opto-electronic industrial wastewater in membrane bioreactor - a pilot study

2003 ◽  
Vol 48 (1) ◽  
pp. 191-198 ◽  
Author(s):  
T.K. Chen ◽  
C.H. Ni ◽  
J.N. Chen ◽  
J. Lin
Keyword(s):  
Wastewater Treatment ◽  
Industrial Wastewater ◽  
Organic Nitrogen ◽  
Membrane Bioreactor ◽  
High Strength ◽  
Experimental Period ◽  
Ammonia Nitrogen ◽  
Nitrifying Bacteria ◽  
Biological Degradation ◽  
The Past

The membrane bioreactor (MBR) system has become more and more attractive in the field of wastewater treatment. It is particularly attractive in situations where long solids retention times are required, such as nitrifying bacteria, and physical retention critical to achieving more efficiency for biological degradation of pollutant. Although it is a new technology, the MBR process has been applied for industrial wastewater treatment for only the past decade. The opto-electronic industry, developed very fast over the past decade in the world, is high technology manufacturing. The treatment of the opto-electronic industrial wastewater containing a significant quantity of organic nitrogen compounds with a ratio over 95% in organic nitrogen (Org-N) to total nitrogen (T-N) is very difficult to meet the discharge limits. This research is mainly to discuss the treatment capacity of high-strength organic nitrogen wastewater, and to investigate the capabilities of the MBR process. A 5 m3/day capacity of MBR pilot plant consisted of anoxic, aerobic and membrane bioreactor was installed for evaluation. The operation was continued for 150 days. Over the whole experimental period, a satisfactory organic removal performance was achieved. The COD could be removed with an average of over 94.5%. For TOC and BOD5 items, the average removal efficiencies were 96.3 and 97.6%, respectively. The nitrification and denitrification was also successfully achieved. Furthermore, the effluent did not contain any suspended solids. Only a small concentration of ammonia nitrogen was found in the effluent. The stable effluent quality and satisfactory removal performance mentioned above were ensured by the efficient interception performance of the membrane device incorporated within the biological reactor. The MBR system shows promise as a means of treating very high organic nitrogen wastewater without dilution. The effluent of TKN, NOx-N and COD can fall below 20 mg/L, 30 mg/L and 50 mg/L.

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