Continuous fermentation of dilute acid-pretreated de-oiled rice bran (DRB) to butanol by the Clostridium acetobutylicum YM1 strain was investigated. Pretreatment of DRB with dilute sulfuric acid (1%) resulted in the production of 42.12 g/L total sugars, including 25.57 g/L glucose, 15.1 g/L xylose and 1.46 g/L cellobiose. Pretreated-DRB (SADRB) was used as a fermentation medium at various dilution rates, and a dilution rate of 0.02 h−1 was optimal for solvent production, in which 11.18 g/L of total solvent was produced (acetone 4.37 g/L, butanol 5.89 g/L and ethanol 0.92 g/L). Detoxification of SADRB with activated charcoal resulted in the high removal of fermentation inhibitory compounds. Fermentation of detoxified-SADRB in continuous fermentation with a dilution rate of 0.02 h−1 achieved higher concentrations of solvent (12.42 g/L) and butanol (6.87 g/L), respectively, with a solvent productivity of 0.248 g/L.h. This study showed that the solvent concentration and productivity in continuous fermentation from SADRB was higher than that obtained from batch culture fermentation. This study also provides an economic assessment for butanol production in continuous fermentation process from DRB to validate the commercial viability of this process.

The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature.

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