BIOSORPTION OF CU(II) IONS FROM INDUSTRIAL EFFLUENTS BY RICE HUSK: EXPERIMENT, STATISTICAL, AND ANN MODELING

Heavy metal removal from wastewater is a significant research area and recommends sustainable development. The heavy metals cause harmful health effects, increase environmental toxicity. Adsorption is a very effective method for heavy metal removal. A fixed bed for Cu(II) removal using rice hush, an agricultural waste, is reported in this paper. The study was carried out to determine the breakthrough curves with varying operating variables like influent concentration (10–30 mg/L), flow rate (10–40 ml/min), and bed height (4–10 cm) at pH 6. The variation of the process variables like influent concentration, flow rate, and bed height were investigated. The experimental data shows that adsorption capacity increases with the rise of influent concentration. The maximum value of adsorption capacity is 10.93 mg/g at a flow rate of 10 ml/min, bed height 4 cm, and influent concentration 30 mg/L. The applicability of the MLR and ANN modeling has also been successfully carried out. ANN has better predictability than MLR. The findings revealed that rice husk could be used to treat copper-containing industrial effluents.

ANN Modeling to Analyze the R404A Replacement with the Low GWP Alternative R449A in an Indirect Supermarket Refrigeration System

Artificial neural networks (ANNs) have been considered for assessing the potential of low GWP refrigerants in experimental setups. In this study, the capability of using R449A as a lower GWP replacement of R404A in different temperature levels of a supermarket refrigeration system is investigated through an ANN model trained using field measurements as input. The supermarket refrigeration was composed of two indirect expansion circuits operated at low and medium temperatures and external subcooling. The results predicted that R449A provides, on average, a higher 10% and 5% COP than R404A at low and medium temperatures, respectively. Moreover, the cooling capacity was almost similar with both refrigerants in both circuits. This study also revealed that the ANN model could be employed to accurately predict the energy performance of a commercial refrigeration system and provide a reasonable judgment about the capability of the alternative refrigerant to be retrofitted in the system. This is very important, especially when the measurement data comes from field measurements, in which values are obtained under variable operating conditions. Finally, the ANN results were used to compare the carbon footprint for both refrigerants. It was confirmed that this refrigerant replacement could reduce the emissions of supermarket refrigeration systems.

Neural Network Models for Traffic Estimation in Mobile Networks in Lagos, Nigeria

The network providers are now being challenged with their inability to accurate estimate and characterize traffic in a particular area, due to the increasing number of mobile communication services being rendered by the network providers Hence, this has been greatly undermining their design and planning processes and as such increasingly affected the Quality of Service(QoS).This research work addresses the traffic estimation in mobile communication network using Artificial Neural Network (ANN) approach using measured data collected in Lagos State,Nigeria.The Multilayer Perceptron (MLP) and Radial Basis Function (RBF) ANN techniques were used in the traffic modeling. The results of the ANN modeling showed that the Model 1 of MLP performed better than other models with Coefficient of Determination (R2) of 99%, Root Mean Square Error(RMSE) of 5.456 and Mean Bias Error(MBE) of 0.94.It is recommended that the dataset used in developing the ANN models be increased by collecting and using not more than 12months traffic data for ANN modeling .An appropriate design of the models should also be given a serious concern by choosing appropriate number of neurons at the hidden units of the neural networks .This will provide a good traffic estimation which the mobile network provider can be used in network design and planning.

Prediction of Mechanical and Toughness Properties of Ni-Modified Cr-Mo Alloy Steels for Transmission Gear

The purpose of the study was to predict the mechanical and toughness properties of Ni-modified alloy steels by adding 1.55%, 1.75%, and 1.95% of Ni-content to the existing Cr-Mo alloy steel of transmission gear material. Typically transmission gears have been working under severe working situations of loads and rotations. Due to these situations, the properties and qualities of gear materials are highly affected consequently, fatigue failure is instigated. So, improving the mechanical and toughness properties of the existing gear material is very vital and compulsory since these properties have a direct impact on gear fatigue failure. Investigations have been done on determining the mechanical and toughness properties of the Ni-modified Cr-Mo alloy steels, through ANN modeling prediction by associating the complex relation of input (chemical composition, tempering temperature) and output parameters (mechanical and toughness properties), and verified by experimental test approaches. Explored these materials property with ANN modeling and experimental test show that the more Ni-content added to the Cr-Mo alloy steel, the higher the ultimate and yield strength can achieve at every instant of tempering temperature. Likewise, fracture toughness, impact toughness, and percent of retained austenite of these materials were also investigated thoroughly as tempering temperature varies. Thus, a 1.55 % Ni-modified Cr-Mo alloy steel has a higher value of both impact toughness and fracture toughness compared with other Ni-modified alloy steels. Similarly, surface hardness was slightly decreased as the amount of Ni-content added increased at each instant of tempering temperature. Lastly, based on both predicted and experimental results, 1.55 % of Ni-modified Cr-Mo alloy steel showed a better combination of mechanical and toughness properties. Keywords: ANN modeling; Yield strength; Ni-modified; Tempering temperature; Fracture toughness; Surface hardness