Direct contact membrane distillation for palm oil mill effluent treatment
Loading...
Date
2016-09-01
Authors
Iylia Idris
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Palm oil mill effluent (POME) is a major waste produced by the palm oil industry. The increase in the production of POME has caused environmental pollution, which ultimately has adverse effects on humans and aquatic life. POME is discharged at high temperature (~ 90 degree Celsius), requires days and spaces to cool it before undergone conventional treatment. Viewing the potential of reducing the treatment period, spaces and heat wasted to the environment, a direct contact membrane distillation (DCMD) treatment system was investigated consisting of pre-treatment and membrane processes. Coagulation study identify optimum coagulant dosage obtained was 4 g/L at pH 6.5, and the percentage of suspended solids removal was found to be 82% at 50 °C. The application of the direct contact membrane distillation (DCMD) was studied using two types of membranes: modified and unmodified PVDF hollow fibre membranes. Modified PVDF membrane was carried out to increase the hydrophobicity (contact angle: 150.3 ± 1.1°) through coating with Low density polyethylene (LDPE) solution. The highest flux was obtained at 80 °C for both types of membranes. The permeate water fluxes for unmodified and modified PVDF hollow fiber membranes were 2.63 L/m2.h and 2.38 L/m2.h, respectively. Furthermore, it was found that the unmodified PVDF hollow fiber membrane could not withstand long operation times compared to the modified PVDF hollow fiber membrane. The TPC values obtained for the modified PVDF hollow fiber membrane ranged between 0.57 to 0.62 at a temperature range of 60 to 80 °C. Meanwhile, the TPC values for the unmodified PVDF hollow fiber membrane at 60 to 80 °C were 0.59 to 0.64. This process can be classified as a well-designed system. The Artificial Neural Network (ANN) is a simple simulation that was implemented to carry out predictions of the permeate water flux. A multilayer Feedforward Artificial Neural Network (FANN) model was developed with three input variables (feed temperature, feed velocity, and type of membrane) and one output (permeate water flux) with 18 experimental data points. This optimized FANN model consisted of 1 hidden layer with 13 hidden neurons. The lowest mean square error (MSE) obtained was 0.0034, while the regression coefficient (R) values for training, validation, and testing were 0.9859, 0.9986, and 0.9984, respectively. These results showed a good training and performance agreement between the ANN model’s predicted data and the experimental data. The selectivity analysis was done to determine the sensitivity of the operating parameters. The most sensitive parameter was the feed temperature compared to feed velocity. The DCMD system is highly potential to replace the conventional treatment of POME. This system eliminate heat waste, shorten treatment period and the treated water generated from the system can be used for other process and safely discharged into the environment.