scholarly journals Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran

2016 ◽  
Vol 2 (11) ◽  
pp. 555-567 ◽  
Author(s):  
Samaneh Khademikia ◽  
Ali Haghizadeh ◽  
Hatam Godini ◽  
Ghodratollah Shams Khorramabadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Mean Square ◽  
Cuckoo Optimization Algorithm ◽  
Artificial Neural

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP.

scholarly journals Artificial neural network (ANN) approach for modeling of selected biogenic compounds in a mixture of treated municipal and dairy wastewater

2019 ◽  
Vol 100 ◽  
pp. 00077
Author(s):  
Joanna Struk-Sokołowska ◽  
Piotr Ofman ◽  
Sevgi Demirel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Extreme Values ◽  
Treatment Plant ◽  
Dairy Wastewater ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents artificial neural network (ANN) model of wastewater treatment plant, which was used for average monthly concentrations of N-NH4+, N-NO3-, N-NO2-, total Kiejdahl nitrogen (TKN), PO43- and SO42- approximation. ANN model was developed for wastewater treatment plant located in Bystre, Poland which treats municipal wastewater with a share of dairy wastewater. The object was chosen because of the unique location, in the Great Mazury Lakes area and the need for its special environmental protection. Input layer of developed ANN model consisted of BOD, COD, concentrations of total nitrogen and total phosphorus, total organic carbon, sulphates, wastewater temperature and pH., The developed model reflected extreme values observed during study period. Average error percentage with which output variables were approximated equalled to 35.35%; 8.99%; 21.23%; 5.08%; 10.99%; 3.02% respectively for N-NH4+, N-NO3-, N-NO2-, TKN, PO43- and SO42-.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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