To reduce the environmental footprints of fossil fuels, there is a need to source more biomasses to increase the renewable energy supply. However, a critical study of the energy-conversion process of biomass must be carried out to make the process economical. In this research, the optimization of the bio-oil production from the thermal conversion of novel biomass- cow hoof was carried out. Three independent variables- temperature of pyrolysis, heating rate and CaO catalyst mass were studied at 3 levels based on the rotatable central composite design (CCD) of the response surface methodology (RSM) to ascertain their influence on two responses- bio-oil yield and its HHV. The quadratic model was more suitable to fit the experimental data. At optimum values of the process variables, bio-oil yield of 50.64% and HHV of 23.86 MJ/kg were obtained. From the analysis of variance carried out, the model R2 values were 0.9949 and 0.9802 respectively for the bio-oil yield and HHV models which showed the models’ ability to predict the bio-oil yield and its HHV in the pyrolysis process is high. The characterization of the bio-oil revealed it has better fuel properties compared with most bio-oils from some biomasses hence it is a viable renewable energy source.

Composite materials are promising materials in the manufacturing industry due to the quality of their materials. However, in transforming these materials, the machining process experiences a high-heat generation rate, which has led to the study of temperature distribution, and reduction analysis at the cutting region. High-temperature generation during machining operation leads to thermal deformation on the developed component or parts, affecting the operation life span of the component. Thus, this study investigated the effect of mineral oil-based-Multi-walled carbon nanofluid (MWCNTs) compared to pure mineral oil in the turning of aluminum-silicon magnesium metal composite (AlSiMg) on temperature reduction and distribution. The nanofluid was prepared with 0.4g of MWCNT to 1 liter of mineral oil. The study employed the energy dispersive spectrometer to obtain the chemical composition of the developed nanofluid. Furthermore, Finite element software DEFORM 3D v11.0 uses a lagrangian incremental approach to simulate chip formation and temperature distribution on the workpiece. Also, to study the effects of the machining parameters on the temperature distribution. The experiment results showed a significant reduction of 11.9% in temperature when machining with nanofluid compared to pure mineral oil. The simulation results showed that the temperature increases as the cutting speed and feed rate increase. The minimum temperature via the DEFORM 3D Finite Element Model simulation was achieved at spindle speed 870 rpm, feed rate 2 mm/rev, and depth-of-cut 1 mm. In conclusion, the study recommends that the manufacturing industry employ the optimized machining parameters during the turning of AlSiMg metal matrix composite for a sustainable machining process.

The implementation of soft computing procedures in tool wear prediction and optimization is a significant process in machining operations for sustainable manufacturing of components with quality finishing. Tool wear is one of the response parameters that leads to a high rate of production cost due to constant tool substitution during machining, mostly when machining hard metals that are difficult to machine. With these challenges, several techniques have been put in place to optimize and predict tool wear rates, including turning, milling, grinding, shaping, and drilling. This study focuses on the evaluation of existing literature that employs soft computing procedures such as ANN-GA, ANFIS, ANFIS-PSO, and ANFIS-FCM in the prediction of cutting tool wear rate during machining processes. From the different study reviews, the results show that the application of these soft computing procedures significantly improves tool life during the manufacturing process by employing the optimal machining parameters in an eco-friendly nano-lubrication environment. This study also points out the challenges currently faced with these soft computing techniques and gives a sustainable way forward as a recommendation to improve the manufacturing process.