State of the Art Review on Statistical Modelling and Optimization of Bioenergy Production from Oil Seeds

The article captures a brief state-of-the-art encompassing statistical modelling and optimization of bioenergy production from oil seeds. Exploring the research space of bioenergy production from biomass oil, various numerical approaches have been employed to model and optimize the process parameters for maximum response yields. Various performance studies have also been carried out to evaluate the predictive capabilities of emerging Artificial intelligence (AI) algorithms on modelling of bioenergy production from seed oil using the conventional Response Surface Methodology (RSM) as a reference base. For the records, the precision of measurement, management of uncertainties, more accurate data analysis and prediction are techniques which these methodologies have the capacity to do. The commonly used techniques for optimization and modelling studies of bioenergy from biomass oil and the analysis of their usage according to their performance metrics are detailed in the body of this work. Aside the relative limitation of RSM models in highly non-linear processes as compared to the robustness of AI models, RSM models still continue to hover on large scale applications when it comes to modelling and optimization studies on bioenergy production from oil-rich plant seeds. In the era of big data analysis in relation to the test and measure of analytical performances and prediction ability, AI models have begun to gain attention in the last two decades owing to their better estimation capabilities and ability to handle more data points in real-time prediction of bioenergy production. This study has been able to show successful applications of both RSM and AI models in bioenergy field, with a pointer to further adoption of the later in more related studies as a result of its relative advantages.