Robust soft sensors for SBR monitoring

2001 ◽  
Vol 43 (3) ◽  
pp. 101-105 ◽  
Author(s):  
D. Zyngier ◽  
O. Q. Araújo ◽  
M. A. Coelho ◽  
E. L. Lima
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Kalman Filtering ◽  
Ammonium Ion ◽  
Carbonaceous Matter ◽  
Process Variables ◽  
Extended Kalman Filtering ◽  
Soft Sensors ◽  
Nitrate Ion

In wastewater treatment some process variables may be very difficult to measure directly because of the non-availability or excessive cost of sensors. An alternative is to use ‘soft sensors” that provide online estimates of these inacessible variables by calculations based on auxiliary measurable variables. Two such sensors are proposed based on extended Kalman filtering or neural networks that could enable this monitoring of nitrate ion, ammonium ion and carbonaceous matter concentrations during nitrification of wastewater.

Distributed Extended Kalman Filtering for Wastewater Treatment Processes

2016 ◽  
Vol 55 (28) ◽  
pp. 7720-7729 ◽  
Author(s):  
Jing Zeng ◽  
Jinfeng Liu ◽  
Tao Zou ◽  
Decheng Yuan
Keyword(s):  
Wastewater Treatment ◽  
Kalman Filtering ◽  
Extended Kalman Filtering ◽  
Treatment Processes

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals Auto-correlation robustness of factorial designs and GAMS in studying the effects of process variables in a dual-objective adsorption system

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mutiu Kolade Amosa ◽  
Fatai A. Aderibigbe ◽  
Adewale George Adeniyi ◽  
Joshua O. Ighalo ◽  
Bisola Taibat Bello ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Factorial Design ◽  
Contact Time ◽  
Factorial Designs ◽  
Adsorption System ◽  
Process Variables ◽  
Operating Variables ◽  
Auto Correlation ◽  
General Algebraic Modeling System ◽  
Combined Models

AbstractThe performance of factorial designs is still limited due to some uncertainties that usually intensify process complexities, hence, the need for inter-platform auto-correlation analyses. In this study, the auto-correlation capabilities of factorial designs and General Algebraic Modeling System (GAMS) on the effects of some pertinent operating variables in wastewater treatment were compared. Individual and combined models were implemented in GAMS and solved with the trio of BARON, CPLEX and IPOPT solvers. It is revealed that adsorbent dosage had the highest effect on the process. It contributed the most effect toward obtaining the minimum silica and TDS contents of 13 mg/L and 814 mg/L, and 13.6 mg/L and 815 mg/L from factorial design and GAMS platforms, respectively. This indicates a concurrence between the results from the two platforms with percentage errors of 4.4% and 0.2% for silica and TDS, respectively. The effects of the mixing speed and contact time are negligible.

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