Experimental Study and Model Development of Algal Based Industrial Wastewater Treatment

Industrial wastewater contain certain chemical nutrients especially compounds of nitrogen and phosphorus, and heavy metals which if discarded without treatment can lead to eutrophication and related environmental issues that affect the recycling processes of bio system. Currently, many efficient procedures exist for removing these nutrients but they usually present drawbacks in terms of cost-effectiveness, complexity of operation, waste generation (sludge) and/or high energy consumption. In order to overcome these problems, this research work focused on treating industrial wastewaters by using microalgae technology. Microalgae was cultivated on the industrial wastewater which provide not only water medium but also source of necessary nutrients suitable for algal cultivation, and thus treat the water by getting rid of these chemical nutrients. The algae selected for the study was spirulina sp. Various parameters like pH TDS, turbidity, Biological Oxygen Demand (BOD), Ammonium, Nitrogen, Phosphate and heavy metals were observed after the treatment. Percentage reduction rate of 35.6% (BOD), Algal productivity rate of 42% (average), and nitrogen reduction rate of 0.8% (average) were observed. Heavy metals involved such as iron, lead, cadmium and Zinc are found to decrease in the range of 60 to 80% per day and per growth of microalgae. Simulating the model for the effect of addition of CO2 into the pond, shows that at a flowrate of 10 m 3 /hour, 62.5% increase of algal productivity is achieved and the maximum productivity is reached at 25 m 3 /hour. The rate of productivity improvement gradually reduces as the gas flowrate increases. In the end of the studies, the simulation provides useful insights into optimal approaches for algal growth and thereby reducing costly experimental efforts. Therefore using microalgae is an environmentally safe alternative for treating wastewater as evident by algal growth rate, productivity and nutrient removal efficiency.