Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

Laser micro-marking is an efficient technique for permanent marking and logo printing on materials. This study details the selection of an optimal parametric combination for laser micro-marking. In this work, markings were performed on Gallium Nitride (GaN) with varying the levels of marking parameters. The parameters considered in the present work are current (A), pulse frequency (Hz), and scanning speed (mm/sec). This experiment was designed using a “central composite design,” grounded in the response surface methodology. Mark intensity, which is a prominent response in laser marking, was considered the output response. The data interpretation involved analysis of variance (ANOVA) and mathematical modelling between the input parameters. It is essential to determine the relationship and significance of input-output variation. The interaction effect of various input parameters on mark intensity was also studied. Finally, two techniques, namely genetic algorithm (GA) and particle swarm optimization (PSO), were applied, and the optimal settings of input constraints were predicted.

Diamond-like carbon thin film coatings are extensively used in manufacturing, medical and aerospace industries due to their excellent tribological, mechanical, optical and biological properties. Due to the non-linear relationship of each process parameter in the deposition process, it is difficult to determine the response results without conducting large numbers of experiments. To counter this, a carefully designed experimentation scheme along with two nature-inspired metaheuristic algorithms are used in the current study. Hydrogen flow rate, Argon-C₂H₂ flow rate and deposition temperature are used as the design variables for maximizing the thin film hardness.