A Software Sensor for in-Situ Monitoring of the 5-Day Biochemical Oxygen Demand

Abstract

Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD 5 ), the present study developed two software sensors based on artifi cial intelligence techniques. It is aimed to estimate this indicator instantaneously. For this purpose, feed-forward and radial basis function neural networks (FFANN and RBFANN, respectively) were used. FFANN and RBFANN were employed to estimate the maximum values of BOD 5 (BOD 5(max) ) as a function of average, maximum and minimum dissolved oxygen in the Sefi drood River. Also, Levenberg-Marquardt (LM), resilient backpropagation, and scaled conjugate gradient algorithms were used to optimize the FFANN parameters. The results demonstrated that the performance of the LM algorithm in tuning the FFANN was better than the others, in the verifi cation step. Furthermore, the performance of each model was evaluated according to the mean square error, correlation coeffi cient and developed discrepancy ratio. The results showed that the performance of both FFANN and RBFANN models for the prediction of the BOD 5(max) were approximately the same.