The industries are bearing with the high concentration of COD in the industrial wastewater, which is to be reduced within the acceptable limits given by the state pollution control board (SPCB). The Fenton process is a conventional process to reduce the COD of the effluent, but has a disadvantage of large production of ferric hydroxide during the Fenton Reaction. But to overcome the disadvantage of sludge formation of hydroxide, employment of the additional process of solar and photo Fenton. By employing the process COD reduction of the effluent is increased. This research presents a comparative study of the reduction of COD of the effluent taken from the industry, which was treated by Solar-Fenton process, Photo-Fenton process and Solar-Photo Fenton process. The experimental results revealed that compared with conventional Fenton process, reduction in Chemical Oxygen Demand (COD) increased by applying the Solar-Fenton as well as Photo-Fenton Process and both. The important conclusion from the research is that every Advance Oxidation Processes (including Fenton) dependent on many process parameters. Temperature effect, initial pH effect, H2O2 dose, Fe +2 concentrations, and most important reaction time were checked and analyzed for reducing the COD. The key point of this research is Reduction of COD using conventional Fenton process followed by UV/solar process. However, using solar energy (renewable source) in place of UV also give same results. It's a greener approach.
Food storage is an essential process for food security and it needs to be free from any biological contamination. For the same, agriculture produce needs to be completely dried before sending for storage. The present work discusses a systematic approach to model drying parameters of corn kernels in a fluidized bed dryer. Experiments were designed according to a higher level Box-Behnken design combined with response surface methodology. Four parameters were chosen to vary namely: amount of corn kernels (50 -100 gm), temperature of drying (50 – 80⁰C), air velocity (6.01 – 8.08 m/s) and drying time (30 – 60 min) for experiments as well as for the model. The reduction of moisture content was determined after each experiment for understanding the behaviour of drying process. The model equations were obtained and surface response plots were generated in MATLAB to investigate the drying behaviour of corn kernels with all four parameters. Ultimately, this work represents the dependence of moisture removal on all four parameters chosen with efficient use of response surface methodology and Box-Behnken design. Analysis of variance confirmed that velocity of air and amount of corn are the most significant parameters along with temperature and time of drying. Optimum condition with the model were obtained as 50 gm of corn kernels, 80 ⁰C drying temperature, 8 m/sec velocity of air, and 60 min time of drying for 73.3 % of moisture from corn kernels.
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