Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

D S Manu,Arun Kumar Thalla

doi:10.1007/s13201-017-0526-4

Abstract

The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014–September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP.

Highlights

Improper maintenance of wastewater treatment plant (WWTP) can trigger serious ecological and public health problems and it may be a reason for various water borne diseases affecting human health and aquatic life
The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling
From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the interrelationship between various wastewater quality variables and SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP

Summary

Introduction

Improper maintenance of WWTP can trigger serious ecological and public health problems and it may be a reason for various water borne diseases affecting human health and aquatic life. Various control actions have to be implemented for efficient monitoring of process performance during the operation of wastewater treatment plant (WWTP) (Boelee et al 2011). Models are necessary for the reason that, the effects of tuning the operating variables can be studied more transiently on a computer than by doing experiments. By simulating the performance assessment models using suitable influential variables, one can rapidly respond to any changes in the processes and devise operational strategies to shift the plant to new operating conditions which improves its stability, the quality of the effluent and at the same time achieve reduction in the running costs (Miller et al 1997; Nair et al 2016; Kumar and Saravanan 2009).

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