Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade.

Biosurfactants are promising additives for gas hydrate technology applications. They are believed to have better eco properties than conventional kinetic hydrate promoters such as sodium dodecyl sulfate (SDS). In this article, the research advances on the use of biosurfactants for gas hydrate formation enhancement have been reviewed and discussed in detail to provide current knowledge on their progress in green chemistry technologies. Specifically, the use of bio promoters in carbon capture, gas storage and transportation are discussed. By far, biosurfactants seem to perform better than conventional hydrate promoters and have the potential to lead to the commercialization of gas hydrate-based technologies in terms of improving hydrate kinetics.

Yield prediction is an integral part of every refinery process due to the intricacy of the process and composition of crude oil. Inability to sufficiently predict the yields before refinery process creates challenges such as inadequacy in planning the operating conditions to meet product target, product optimisation failure and inability to meet product market specification. Predicting yields using a yield pattern model practised by the Tema Oil Refinery is economical; however, it is time consuming and tedious. This paper focused on using a Visual Basic 6.0 programming language to predict product yields that is based on kinetic reaction models. A user friendly and efficient software application has been developed to predict product yields within a short time. This software is compatible with all windows platforms and satisfactorily predicts crude oil yields with a certainty range of 94.0 to 99.0%. Key words: Crude oil, crude oil yield, yield correlations, visual basic 6.0.