This review showcases a comprehensive analysis of studies that highlight the different conversion procedures attempted across the globe. The resources of biogas production along with treatment methods are presented. The effect of different governing parameters like feedstock types, pretreatment approaches, process development, and yield to enhance the biogas productivity is highlighted. Biogas applications, for example, in heating, electricity production, and transportation with their global share based on national and international statistics are emphasized. Reviewing the world research progress in the past 10 years shows an increase of ~ 90% in biogas industry (120 GW in 2019 compared to 65 GW in 2010). Europe (e.g., in 2017) contributed to over 70% of the world biogas generation representing 64 TWh. Finally, different regulations that manage the biogas market are presented. Management of biogas market includes the processes of exploration, production, treatment, and environmental impact assessment, till the marketing and safe disposal of wastes associated with biogas handling. A brief overview of some safety rules and proposed policy based on the world regulations is provided. The effect of these regulations and policies on marketing and promoting biogas is highlighted for different countries. The results from such studies show that Europe has the highest promotion rate, while nowadays in China and India the consumption rate is maximum as a result of applying up-to-date policies and procedures.

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Artificial neural network modelling has been employed to investigate the effects of various environmental and machine factors on the energy gain from wind farm systems. Numerical comparison of artificial neural network and nonlinear regression from XLSTAT showed that ANN possessed better numerical accuracy in predicting multivariate data. Several artificial neural network models are developed and tested with several structures to obtain the best prediction performance in energy gain from different wind farms in Jordan. The best performing artificial neural network model was used to predict the energy gain from wind farm based on changes in annual wind speed, turbine rotor diameter and turbine power. As a result of 20% increase in turbine power, 14.4%–31% energy gains were recorded across different wind farms. The proposed artificial neural network model was also a good predictor for energy cost resulting from specific wind farm design.

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This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results.

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