Cleaner Production Assessment for Wastewater Treatment Plants Based on Backpropagation Artificial Neural Network

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Fang He ◽  
Jian Wang ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Cleaner Production ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

scholarly journals Artificial Neural Network as a Virtual Sensor of Nitrate Nitrogen (V) Concentration in an Activated Sludge Reactor

2020 ◽  
Vol 30 (4) ◽  
pp. 188-200
Author(s):  
Lesław Płonka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Input Data ◽  
Nitrate Nitrogen ◽  
Wastewater Treatment Plants ◽  
Network Training ◽  
Activated Sludge Reactor ◽  
Artificial Neural

Abstract The paper discusses the use of an artificial neural network to control the operation of wastewater treatment plants with activated sludge. The task of the neural network in this case is to calculate (predict) the readings of the probe measuring the concentration of nitrate nitrogen (V) in one of the biological reactor tanks. Neural networks are known for their ability to universal approximation of virtually any relationship, including the function of many variables, but the process of “training” the network requires the presentation of many sets of input data and corresponding expected results. This is a difficulty in the case of wastewater treatment plants, because some key process parameters are usually not measured online (samples are taken and measurements are taken in the laboratory), and even if they are, the time intervals are large. Bearing in mind the aforementioned difficulty, this work uses a set of input data consisting only of information that can be measured with measuring probes. As a result of the conducted experiments a high compliance of the probe’s prediction with the expected values was obtained. The paper also presents data preparation and the network “training” process.

scholarly journals Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

2018 ◽  
Vol 154 ◽  
pp. 01041 ◽  
Author(s):  
Agus Harjoko ◽  
Tri Ratnaningsih ◽  
Esti Suryani ◽  
Wiharto ◽  
Sarngadi Palgunadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acute Myeloid Leukemia ◽  
Blood Cells ◽  
Myeloid Leukemia ◽  
Active Contour ◽  
White Blood Cells ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network ◽  
Acute Myeloid

Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.

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