Under specific conditions, the fermentation of whey permeate (WP) by Brettanomyces claussenii can create bioproducts with high galactose concentrations and potential functionalities. The aims of this research are to optimize the fermentation of WP by B. claussenii using response surface methodology to maximize the production of ethanol and galactose, and to characterize various products obtained with this approach. For this purpose, five fermentation factors were studied to determine their impacts on ethanol and galactose: temperature (20 - 40°C), substrate concentration (5 - 15%TS), lactase enzyme/substrate ratio (0 - 40 IU/ g lactose), inoculation level (6 - 8 log cfu/mL), and time (6 - 30 days). Linear models, containing quadratic and interaction effects, were built for the optimization of both responses. Optimal levels were predicted for the maximum obtainment of ethanol and galactose simultaneously, which utilized the following parameters: 15%TS, 37 IU / g lactose, 28°C, 7.5 log cfu/mL, and 30 days, which together were predicted to produce 4.0%v/v ethanol and 51 g/L galactose in the final product. These parameters were then applied to 18-L fermentations, and the resulting fermentates were processed via distillation and freeze-drying. As a result, four product streams were obtained: a fermented product with 3.4%v/v ethanol and 56 g/L galactose; a 45%v/v ethanol distillate; a galactose-rich drink base (63 g/L); and a galactose-rich powder (55%w/w). These results demonstrate that it is possible to maximize the production of ethanol and galactose from the fermentation of WP and to design manufacturing processes based on these optimization models, to develop novel, potentially functional bioproducts from this stream.
As the Greek-style yogurt market continues to experience prosperous growth, finding the most appropriate destination for yogurt acid whey (YAW) is still a challenge for Greek yogurt manufacturers. This study provides a direct alternative treatment of YAW by leveraging the abilities of Mucor circinelloides and Mucor genevensis to raise the pH of YAW and to produce fungal biomass with a high lipid content. Aerobic cultivations of these species were conducted in YAW, both with and without the addition of lactase, at 30 °C, and 200 rpm agitation. The density, pH, biochemical oxygen demand (BOD), biomass production, lipid content, fatty acid profile, and sugar and lactic acid concentrations were regularly measured throughout the 14-day cultivations. The data showed that M. genevensis was superior at deacidifying YAW to a pH above 6.0—the legal limit for disposing of cultured dairy waste. On the other hand, M. circinelloides generated more fungal biomass, containing up to 30% w/w of lipid with high proportions of oleic acid and γ-linolenic acid. Additionally, the treatments with lactase addition showed a significant decrease in the BOD. In conclusion, our results present a viable treatment to increase the pH of YAW and decrease its BOD, meanwhile generating fungal oils that can be further transformed into biodiesel or processed into functional foods or dietary supplements.
Background: Electricity load forecasting plays an essential role in the dispatching operation of power systems. It can be divided into long-term, medium-term, and short-term according to the forecast time. Accurate short-term electric forecasting helps the system operate safely and reliably, reduces resource waste, and improves economic efficiency. Objective: To fully use the time-series characteristics in load data and improve the accuracy of short-term electric load forecasting, we propose an improved Informer model called Nysformer. Methods: Firstly, the input of data is improved, and the information is input into the model in the form of difference. Then, the Nystrom self-attention mechanism was proposed, approximating the standard self-attention mechanism using an approximation with O(n) time complexity and memory utilization. Results: We conducted experiments on a publicly available dataset, and the results show that the proposed Nysformer model has lower time complexity and higher accuracy than the standard Informer model. Conclusion: An improved informer network is proposed for short-term electric load forecasting, and the experimental results demonstrate the proposed model Nysformer can improve the accuracy of short-term electric load forecasting. other: No
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