Comparative study of lactic acid production from date pulp waste by batch and cyclic-mode dark fermentation

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Valorization of biowaste into lactic acid by indigenous microbiota has gained attention due to its potential in resource recovery and environmental benefits. Batch and cyclic fermentation processes were compared in this study, showing that enzymatic pretreatment improved lactic acid production, especially in cyclic fermentation. The use of an Artificial Neural Network model helped optimize process parameters and predict lactic acid concentration accurately.