Biochemical Oxygen Demand (BOD) Estimation Using Soft Computing Methods

Wastewater characterization is very important when designing and determining performance of wastewater treatment plants. The most widely used parameters of wastewater characterization are the Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). BOD is a measure of the quantity of oxygen consumed by microorganisms during the decomposition of organic matter. BOD is the most commonly used parameter for determining the oxygen demand on the receiving water of a municipal or industrial discharge. It is also used to determine the size of waste treatment facilities and to measure the efficiency of some treatment process. BOD test requires five days. COD is used to measure the oxygen equivalent of the organic material in wastewater that can be oxidized chemically using dichromate in an acid solution. COD test take about 3 hours compared to the BOD test. There is no generalized correlation between BOD and COD. It is possible to develop such correlations for a specific waste contaminant in a specific wastewater stream, but such correlations cannot be generalized for use with any other waste contaminants or wastewater streams. In this study are used soft computing methods which are the powerful tool for input-output mapping. These are artificial neural networks (ANNs), fuzzy logic (FL) that is Mamdani Fuzzy interference system (FIS-Mamdani) and Sugeno fuzzy interference system (FIS-Sugeno), adaptive neuro-fuzzy inference system (ANFIS) approaches used to predict Biochemical Oxygen Demand. This application is modeled to predict BOD in a wastewater treatment plant. The results show that adaptive neuro-fuzzy inference system (ANFIS) technique is found to be significantly superior to others.