Optimization for simultaneous enhancement of biobutanol and biohydrogen production

Acetone-butanol-ethanol (ABE) fermentation guarantees a sustainable route for biohydrogen and biobutanol production. This research work is committed towards the enhancement of biohydrogen and biobutanol production by single and multi-parameter optimization for the improvement of substrate energy recovery using C. saccharoperbutylacetonicum. Single parameters optimization (SPO) manifested that headspace of 60% (v/v) and butyric acid supplementation of 9 g/L and temperatures of 30 degrees C and 37 degrees C were suitable for obtaining maximum biohydrogen and biobutanol production, respectively. The interaction between these parameters was further evaluated by implementing a 5-level 3-factor Central Composite Design (CCD). In the present study, a central composite design was employed to enhance the biohydrogen and biobutanol production. In addition, the experimental results were analyzed by response surface methodology (RSM) and artificial intelligence (AI) techniques such as artificial neural network (ANN). The prediction capability of RSM was further compared with ANN for predicting the optimum parameters that would lead to maximum biohydrogen and biobutanol production. ANN yielded higher values of biohydrogen and biobutanol. ANN was found to be superior as compared to RSM in terms of prediction accuracy for both biohydrogen and biobutanol because of its higher coefficient of determination (R-2) and lower root mean square error (RMSE) value. Process temperature (32.65 degrees C), headspace (58.21% (v/v)) and butyric acid supplementation (9.16 g/L) led to maximum substrate energy recovery of 78% with biohydrogen and biobutanol production of 5.9 L/L and 16.75 g/L, respectively. Process parameter optimization led to a significant increase in substrate energy recovery from Biphasic fermentation. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This research focuses on enhancing biohydrogen and biobutanol production by optimizing parameters such as temperature, headspace, and butyric acid supplementation. By using Central Composite Design, the study aims to maximize substrate energy recovery. Artificial intelligence techniques like artificial neural network show promise in predicting optimal parameters for maximum biohydrogen and biobutanol production.