Prediction of standard aeration efficiency of a propeller diffused aeration system using response surface methodology and an artificial neural network | Zendy

Subha M. Roy | Zendy; Mohammad Tanveer | Zendy; Debaditya Gupta | Zendy; C.M. Pareek | Zendy; B. C. Mal | Zendy

Open Access

Prediction of standard aeration efficiency of a propeller diffused aeration system using response surface methodology and an artificial neural network

Author(s) -

Subha M. Roy,

Mohammad Tanveer,

Debaditya Gupta,

C.M. Pareek,

B. C. Mal

Publication year - 2021

Publication title -

water science and technology water supply

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.318

H-Index - 39

eISSN - 1607-0798

pISSN - 1606-9749

DOI - 10.2166/ws.2021.199

Subject(s) - aeration , response surface methodology , propeller , mean squared error , artificial neural network , approximation error , standard deviation , coefficient of determination , absolute deviation , mathematics , rotational speed , root mean square , environmental science , engineering , statistics , computer science , marine engineering , mechanical engineering , machine learning , electrical engineering , waste management

Aeration experiments were conducted in a masonry tank to study the effects of operating parameters on the standard aeration efficiency (SAE) of a propeller diffused aeration (PDA) system. The operating parameters included the rotational speed of shaft (N), submergence depth (h), and propeller angle (α). The response surface methodology (RSM) and an artificial neural network (ANN) were used for modelling and optimizing the standard aeration efficiency (SAE) of a PDA system. The results of both approaches were compared for their modelling abilities in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), computed from experimental and predicted data. ANN models were proved to be superior to RSM. The results indicate that for achieving the maximum standard aeration efficiency (SAE), N, h and α should be 1,000 rpm, 0.50 m, and 12°, respectively. The maximum SAE was found to be 1.711 kg O2/ kWh. Cross-validation results show that best approximation of the optimal values of input parameters for maximizing SAE is possible with a maximum deviation (absolute error) of ±15.2% between the model predicted and experimental values.

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