Prediction of Efficiency for a Passive Flat Plate Collector for Water Desalination using Artificial Neural Network

Artificial neural network was used for modeling and prediction of the efficiency of a  passive flat plate collector for water desalination. An extensive experimental program design was undertaken on the collector to obtain the parameters required for the modeling. The neural model to predict the efficiency was developed based on groups of experiments carried out. Five (5) parameters: ambient, inlet fluid and outlet fluid temperatures, radiation, and aperture area of the collector were used as inputs into the network architecture of 5 [5]1 1 in predicting the efficiency. After series of network architectures were trained using different training algorithms such as Levenberg-Marquardt, Bayesian Regulation, Resilient Backpropagation using MATLAB 7.9.0 (R20096), the LM 5 [5]1 1 was selected as the most appropriate model. Prediction of the neural model exhibited reasonable correlation with the experimental collector efficiency. The predicted collector efficiency gave minimal MSE errors and higher correlation coefficients and Nash-Scutcliffe efficiency (NSE) indicating that the model was robust for predicting the efficiency of a passive flat plate collector for desalination of water. Keywords: Collector efficiency, desalination, passive solar collector, artificial neural network, Nash-Scutcliffe efficiency, MSE error, modeling

Eco-friendly composites for brake pads from agro waste: a review

Natural fibers possess unique property densities that make them potential alternative reinforcement materials in synthetic brake pad composites. This article presents a comprehensive review for the potential and possibilities of reinforcing brake pads using natural plant-based fibers. The influential keys to designing brake pad composites are found to be thermal stability, interfacial bond of the matrix with the fiber, thermal fade, effectiveness, and recovery. Besides that, the optimization technique for manufacturing process of eco-friendly brake pads is also covered. It can be concluded that natural fibers can be used as potential materials for designing effective eco-friendly brake pad composites in the near future

Study of Corrosion Inhibition Potentials of Eichhornia crassipes Leaves Extract on Mild Steel in Acidic Medium using Artificial Neural Network

Prediction of corrosion behavior of steel in acidic environments is an essential step towards optimizing the design of equipment in any industrial setting. An artificial neural network (ANN) may be used as a reliable modeling method for simulating and predicting the corrosion behaviour. The present study has been conducted to investigate the corrosion inhibition potentials of Eichhornia crassipes (water hyacinth) leaves extract for mild steel in acidic media and to establish an appropriate ANN model for predicting corrosion behavior of mild steel in H 2 SO 4 inhibited by Eichhornia crassipes. The experimental procedure employed weight loss method for corrosion rate measurements. Results have shown that Eichhornia crassipes is an effective inhibitor for corrosion inhibition of mild steel in acidic medium. A Levenberg-Marquardt (LM) ANN with single hidden layer having five neurons was employed to simulate the corrosion behaviour. The neural network was trained using the experimental corrosion database. Finally, validity of the proposed model was tested using standard statistical parameters. Results indicate that the trained ANN model is robust for predicting corrosion behaviour of mild steel in acidic media.</jats:p

Comparative Analysis of Multiple Linear Regression and Artificial Neural Network for Predicting Friction and Wear of Automotive Brake Pads Produced from Palm Kernel Shell

In this study, comparative analysis of multiple linear regression (MLR) and artificial neural network (ANN) for prediction of wear rate and coefficient of friction brake pad produced from palm kernel shell was carried out. The inputs parameters used for the two models generated using inertia dynamometer were the percentages of palm kernel shell, aluminium oxide, graphite, calcium carbonate, epoxy resin, interface temperature of the brake pad, and work done by brake application. Two model equations were developed using MLR model for predicting wear rate and coefficient of friction while the neural network architecture BR 7 [5-3] 2 was used to predict wear rate and coefficient of friction. The predicted wear rate and coefficient of friction by MLR model were compared with ANN model along with the measured values using statistical tools such as means square absolute error (MAE), root means square error (RMSE), and Nash-Scutcliffe efficiency (NSE). The results revealed that the MLR model outsmarts the ANN model with the values of MAE and RMSE reasonably low and NSE reasonably higher. The best MAE and RMSE values of 0.000 were observed at the three values of measured wear rates and coefficient of friction that matched with the predicted values using MLR compared to -0.0300 and 0.0740 for ANN model. However, the ANN model was equally found suitable for the prediction of wear rate and coefficient of friction of brake pads developed. The implication of these results is that the two models have the capabilities of being used simultaneously when estimating the wear and coefficient of friction of brake pads

Evaluation of palm kernel fibers (PKFs) for production of asbestos-free automotive brake pads

AbstractIn this study, asbestos-free automotive brake pads produced from palm kernel fibers with epoxy-resin binder was evaluated. Resins varied in formulations and properties such as friction coefficient, wear rate, hardness test, porosity, noise level, temperature, specific gravity, stopping time, moisture effects, surface roughness, oil and water absorptions rates, and microstructure examination were investigated. Other basic engineering properties of mechanical overload, thermal deformation fading behaviour shear strength, cracking resistance, over-heat recovery, and effect on rotor disc, caliper pressure, pad grip effect and pad dusting effect were also investigated. The results obtained indicated that the wear rate, coefficient of friction, noise level, temperature, and stopping time of the produced brake pads increased as the speed increases. The results also show that porosity, hardness, moisture content, specific gravity, surface roughness, and oil and water absorption rates remained constant with increase in speed. The result of microstructure examination revealed that worm surfaces were characterized by abrasion wear where the asperities were ploughed thereby exposing the white region of palm kernel fibers, thus increasing the smoothness of the friction materials. Sample S6 with composition of 40% epoxy-resin, 10% palm wastes, 6% Al2O3, 29% graphite, and 15% calcium carbonate gave better properties. The result indicated that palm kernel fibers can be effectively used as a replacement for asbestos in brake pad production

Evaluation of some Properties of Polyester Based Hybrid Composites Produced From Luffa-Bananna Fibres

This study presents the evaluation of the mechanical, physical and dynamic mechanical properties of luffa-banana fibre reinforced polyester hybrid composites. The luffa fibre and banana fibres were extracted from luffa plant and banana stem respectively by manual stripping into strands. The luffa and banana fibres were then blended in the ratio of 50:50 for the production of the hybrid composites using hand lay-up method. Polyester-resin was used as binder and the percentages of luffa-banana fibres used were 3, 5, 6, and 9 %. The tensile strength, impact strength, flexural strength, density, water absorption, and the dynamic mechanical analysis (DMA) (storage modulus, loss modulus damping factor) of the produced luffa-banana hybrid composites were evaluated. The results of the density and water absorption obtained varied from 0.84-1.23 g/cm3 and 0 - 0.35 % respectively. The tensile and impact strengths (3.46 - 9.27 MPa and 0.66-3.26 J/cm2) of the produced hybrid composites were observed to increase with increasing fibre content from 3 - 6 % and decreased at 9 %. The results of DMA revealed that loss modulus of the hybrid composites and pure polyester were found to increase with increasing temperature up to glass transition temperature and then decreased. The damping factor was observed to increase with increasing temperature and goes at maximum level in transition region and while decreasing the in rubbery region. The properties of the produced hybrid luffa-banana composites showed that luffa and banana fibres can be used in synergy as raw materials for composites manufacture. As the properties evaluated were in agreement with standard composites used as interior design of cars.</jats:p