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[Display omitted] • Biomass is a valuable stock for renewable bio-hydrogen production. • Dark fermentation of biomass produces hythane (bio-methane and bio-hydrogen). • NADH and formate pathway defines the efficiency of bio-hydrogen production. • Nanoparticles addition helps in better bio-hydrogen yield and biomass conversion. • H 2 Storage and transport hindrances need to be eliminated for commercialization. Modernization of lifestyle, industrialization, population has resulted in generation of huge amount of waste, with rich in organic and inorganic content. Waste biomass (agriculture, algae, sludge, livestock manure, plant source) are available in large quantity worldwide owing to their renewability. Biomass is processed via thermochemical, biological, electrochemical processes for value added products recovery. This review focuses mainly on bio-hydrogen production from biomass via dark fermentation. Dark fermentation of biomass results in hythane formation (i.e methane and hydrogen) apart from other gases. The parameters that influence dark fermentation mechanism are temperature, hydraulic retention time, pH, inoculum. The influence of these parameters on NADH and formate pathways defines the yield of bio-hydrogen. The major hindrances on commercialization of dark fermentation-based bio-hydrogen process is due to its storage and transport problems. Unlike commercial petrol-diesel, bio-hydrogen cannot be transported or stored for longer period, owing to its environmental concerns. This review paves way for further research on identifying genetic improvement strategies on microbes for better bio-hydrogen yield and developing bio-hydrogen storage and transport designs. [ABSTRACT FROM AUTHOR]

The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed. According to the result, it can be concluded that ANN model with MLPNN could produce the acceptable prediction of the wind speed for given on wind direction. • To predict the wind speed (m/sec) at 65 m height. • To build the ANN Models and Categorical Regression (CATREG) for wind speed prediction. • To produce the validated and acceptable prediction. [ABSTRACT FROM AUTHOR]