Continuous Electrochemical Treatment Of Dairy Industry Wastewater

Abstract

Dairy industry generates a vast amount of wastewaters and is characterized by high chemical oxygen demand (COD) and nutrient due to their high level of organic contents. The amount of wastewater generated is approximately 0.2 liter to 10 liter of waste per liter of processed milk. It may be either extremely acid or alkaline. The dairy wastes usually contain inert, organic or toxic materials and possibly pathogenic bacteria. Europe has the largest market in the dairy industry. US have the second largest market. Global: 1.9%, APAC: 3.5%, Western Europe: 0.2%, Africa: 2.2%, Eastern Europe: 2.4%, Latin America: 2.8%. India is one of the largest milk producers in the world with a market size of US$4.4 billion CAGR (2005-2009). Different method for the treatment of dairy wastewater is Aerobic, Anaerobic, Sequential batch reactor, trickling filter, oil filter separation, electrochemical method etc. Present work reports the dairy wastewater treatment by continuous electrochemical treatment method with aluminum electrodes. The effect of various parameters like pH, current (I), flow rate (F) and treatment time (t) on % COD removal (Y1) and specific energy consumed (Kwh/kg of COD removed) Y2 were observed through experimental design and analysis of data using central composite design (CCD) based on response surface methodology (RSM). For this four factors and three level full factorial CCD based on RSM have been used for the experimental design. Variables current (I); 0.5–2.5 A; electrolysis time (t): 30–120 min, pH: 5–10 and wastewater flow rate (F); 0.8-1.8 l/h have been considered as input parameters and % COD removal (Y1) and specific energy consumed (KWh per kg of COD removed) (Y2) have been taken as a responses of the system. A total of 30 experiments were suggested by RSM. The data obtained from the experiments was analyzed using Design-Expert trial version. Three analytical steps: adequacy of various models test (sequential model sum of squares and model summary statistics), analysis of variance (ANOVA) and the response surface plotting were performed to establish an optimum condition for the responses.The data obtained by the experiments set suggested were fitted to a second-order polynomial model equation.