Comparative study of lactic acid production from date pulp waste by batch and cyclic–mode dark fermentation
| Autor: | Ashfaq Ahmad, Fawzi Banat, Hanifa Taher |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
020209 energy
Pulp (paper) 02 engineering and technology Dark fermentation 010501 environmental sciences engineering.material Pulp and paper industry 01 natural sciences Lactic acid chemistry.chemical_compound Bioreactors Nutrient chemistry Food Fermentation 0202 electrical engineering electronic engineering information engineering Batch processing engineering Biomass Lactic Acid Cellulose Waste Management and Disposal 0105 earth and related environmental sciences Resource recovery |
| Zdroj: | Waste Management. 120:585-593 |
| ISSN: | 0956-053X |
| Popis: | Biowaste valorization into lactic acid (LA) by treatment with indigenous microbiota has recently gained considerable attention. LA production from date pulp waste provides an opportunity for resource recovery, reduces environmental issues, and possibly turns biomass into wealth. This study aimed to compare the performance of batch and cyclic fermentation processes in LA production with and without enzymatic pretreatment. The fermentation studies were conducted in the absence of an external inoculum source (relying on indigenous microbiota) and without the addition of nutrients. The highest LA volumetric productivity (3.56 g/liter/day), yield (0.07 g/g-TS), and concentration (21.66 g/L) were attained with enzymatic pretreated date pulp in the cyclic-mode fermentation at the optimized conditions. The productivity rate of LA was enhanced in the cyclic-mode as compared to the batch process. Enzymatic pretreatment increased the digestibility of cellulose that led to higher LA yield. An Artificial Neural Network model was developed to optimize the process parameters and to predict the LA concentration from date pulp waste in both fermentation processes. The main advantage of the ANN approach is the ability to perform quick predictions without resource-consuming experiments. The model predicted optimal conditions well and demonstrated good agreement between experimental and predicted data. |
| Databáze: | OpenAIRE |
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