Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms.

In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R2 of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R2 values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.

Reuse of sea water reverse osmosis brine to produce Dunaliella salina based ß-carotene as a valuable bioproduct: A circular bioeconomy perspective.

Due to population growth and global warming, the use of the sea water reverse osmosis process to obtain freshwater is increasing rapidly. A sustainable method with low environmental impact is limited for the management of brine with a high salt content, which is released as a result of the process. Some microalgae species can grow in salty environments and produce ß-carotene. This study aims to evaluate the commercial potential of ß-carotene production from microalgae grown in sea water reverse osmosis brine from a bioeconomy perspective. Synthetic media are often used for the production of ß-carotene from algae, the use of sea water reverse osmosis brine is not common and the commercial potential of this application has not been evaluated before. In terms of the development of the ß-carotene market, the strengths and weaknesses of the process, opportunities, and threats are thoroughly examined in this report. Also, with the use of sea water reverse osmosis, a daily 750 tons of algal ß-carotene can be produced. The biotechnological production of microalgal ß-carotene and the reuse of salt water within the scope of circular bioeconomy are seen as a sustainable solution due to the fact that the strengths of the process are dominant, and the market value of natural ß-carotene is increasing day by day.

Determination of photoautotrophic growth and inhibition kinetics by the Monod and the Aiba models and bioenergetics of local microalgae strain.

The usage of fossil fuels results in a high amount of greenhouse gas (GHG) emissions and renewable green energy requirements entail saving ecological balance. Therefore, microalgae cultivation is widespread as a suitable raw material to produce renewable and sustainable fuel. Mathematical models are useful tools for the estimation of different conditions of a system. In this study, mathematical models were developed for monitoring the cultivation of local species of microalgae based on the chlorophyll-a and biomass concentration. Coefficients that were calculated from the Monod kinetic model were µmax; 0.03 day-1, KS, Ci; 0.53 mM with an R2 value of 0.93 and from the Aiba inhibition kinetic model was µmax and KS, Ci 1.48 day-1 and 0.08 mM with an R2 value of 0,73. According to the literature, there was no model was developed for the determination of kinetic coefficients based on chlorophyll-a production due to the inorganic carbon consumption. While both growth and inhibition models were developed for the inorganic carbon consumption, chlorophyll-a concentration was used for the growth model and biomass concentration was used for the inhibition model which caused and directly affected by the decrease of light penetration. The maximum biomass and chlorophyll-a concentrations were found as 1.2 g/L and 27.8 mg/L respectively with 10.24 mg/L. day-1 nitrogen and 1.19 mg/L.day-1 phosphorus uptake rate.

Association of MDR1, CYP2D6, and CYP2C19 gene polymorphisms with prophylactic migraine treatment response.

Prophylactic therapy response varies in migraine patients. The present study investigated the relationship between the resistance to the drugs commonly used in prophylactic therapy and the possible polymorphic variants of proteins involved in the metabolism of these drugs. Migraine patients with the MDR1 3435TT genotype exhibited a better treatment response to topiramate than migraine patients with the CC and CT genotypes (p=0.020). The MDR1 C3435T polymorphism was also found to be a higher risk factor for topiramate treatment failure in a comparison of the number of days with migraine (ß2=1.152, p=0.015). However, there was no significant relationship between the treatment response to topiramate and either the CYP2D6 or CYP2C19 polymorphism, and there were no significant correlations between the treatment responses to amitriptyline, propranolol, and valproic acid and the MDR1, CYP2D6 and CYP2C19 gene polymorphisms. This is the first study to investigate the effect of the polymorphic variants on prophylactic therapy response in migraine patients.