Multiple regression prediction of biochemical oxygen demand of wastewater from cocoa processing

Biochemical Oxygen Demand (BOD5) is an important parameter for verifying the quality of discharged water fr-om wastewater treatment plants. The 5 day duration required in determining BOD5 levels causes delay in decision making for process control of wastewater treatment facility, which normally requires only several hours of reside-nce time. Therefore, a multiple regression model was developed to predict BOD5 levels in wastewater produced from cocoa processing. The concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Oil and Grease (OG) in the wastewaters were determined quickly within ~ 2 hours, and the data was used to form-ulate a multiple regression equation to predict the levels of BOD5 and associated statistics within a very short time. Thirty (30) data sets of TSS, COD and OG wastewater parameters were used as independent variables to develop the regression equation with BOD5 as dependent variable. Additional twenty five (25) data set from the same cocoa processing wastewater were used to verify and calibrate the model, which showed very good agreeme-nt between the model prediction and measured BOD5 values. The multiple regression equation could be used to give quick estimate of the BOD5 levels of the wastewater from cocoa processing and thereby facilitate the waste-water treatment process for the factory.Keywords: Biological oxygen demand, Chemical oxygen demand, Total suspended solids, Wastewater, Cocoa Processin