Development of sunlight-driven eutectic phase change material nanocomposite for applications in solar water heating

Organic phase change materials (PCMs) have been utilized as latent heat energy storage medium for effective thermal management. In this work, a PCM nanocomposite, consisting of a mixture of two organic PCMs (referred to as eutectic gel PCM) and minimal amount (0.5 wt%) of nanographite (NG) as a supporting material, was prepared. Differential scanning calorimeter was used to determine the melting temperature and latent heat of pristine PCM, paraffin (61.5 °C and 161.5 J/g), eutectic gel PCM (54 °C and 158 J/g) and eutectic gel PCM nanocomposite (53.5 °C and 155 J/g). The prepared PCM nanocomposites exhibited enhanced thermal conductivity and ultrafast thermal charging characteristics. The nanocomposites were employed for two different applications: (i) providing hot water using an indigenously fabricated solar water heating (SWH) system and (ii) solar rechargeable glove that can be rapidly warmed and used. Experimental results on SWH system show that the use of PCM nanocomposites helps to increase the charging rate of PCM while reducing the discharging rate of heat by PCM to water, thus enhancing the maximum utilization of solar energy and hence improving the efficiency of the SWH system. The experimental results on solar rechargeable glove revealed that the glove has the ability to retain the temperature up to 3 hours

Simulation and Optimization of Artificial Neural Network Modeling for Prediction of Sorption Efficiency of Nanocellulose Fibers for Removal of Cd (II) Ions from Aqueous System

Simulation and optimization of an Artificial Neural Network (ANN) for modeling biosorption studies of cadmium removal using nanocellulose fibers (NCFs) was carried out. Experimental studies led to the standardization of the optimum conditions for the removal of cadmium ions i.e. biomass dosage (0.5 g), test volume (200 ml), metal concentration (25 mg/l), pH (6.5) and contact time (40 min). A Single layer ANN model was developed to simulate the process and to predict the sorption efficiency of Cd (II) ions using NCFs. Different NN architectures were tested by varying network topology, resulting in excellent agreement between experiment outputs and ANN outputs. The findings indicated that ANN provided reasonable predictive performance for training, cross validation and testing data sets (R2 = 0.998, 0.995, 0.992). A sensitivity analysis was carried out to assess the influence of different independent parameters on the biosorption efficiency, and pH > biomass dosage > metal concentration > contact time > test volume were found to be the most significant factors. Simulations based on the developed ANN model can estimate the behavior of the biosorption phenomenon process under different experimental conditions. doi:10.14456/WJST.2014.