Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran.

The Balangu (Lallemantia iberica) species have a high gastronomical impact in the Middle East and Balkan region. It is widely used in the local food industry, such as confectionery, edible oil, and protein food. In this study, 49 ecotypes were collected from different regions of Iran. 37 agronomic traits were measured during the growing season and at harvest time. To find the correlation between the grain yield per unit area, grain yield per single plant (GYSP), oil percent (OP), and protein percent (PP) with other measured traits, which these were utilized as the labels of different machine learning (ML) procedures including Linear Regression (LR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boosting Decision Tree Regression (GBDTR). It was observed that there is a linear relationship between the measured agronomic traits and the considered labels. So, the LR, RFR, and GBDTR models showed the lowest mean absolute error, mean square error, and root mean square error than SVR models and good prediction ability of the test data. Although, the RFR and GBDTR have naturally lower bias than other methods in this study, but the GBDTR scheme is preferred because of the over-fitting shortcoming of the RFR technique. The GBDTR method showed better results rather than the other ML regression methods according to the RMSE 3.302, 0.040, 0.028, and 0.060 for GYUA, GYSP, OP, and PP, respectively.

Potential of the green alga Chlorella vulgaris for biodegradation of crude oil hydrocarbons.

Oil production and/or transportation can cause severe environmental pollution and disrupt the populations of living organisms. In the present study, biodegradation of petroleum hydrocarbons is investigated using Chlorella vulgaris as a green algal species. The microalga was treated by 10 and 20g/l crude oil/water concentrations at two experimental durations (7 and 14days). Based on the results obtained, C. vulgaris owned not only considerable resistance against the pollutants but also high ability in remediation of crude oil hydrocarbons (~94% of the light and ~88% of heavy compounds in 14days). Intriguingly, dry weight of C. vulgaris increased by the rising crude oil concentration indicating the positive effect of crude oil on the growth of the algal species. This biodegradation process is remarkably a continuous progression over a period of time.

Biodiesel production in crude oil contaminated environment using Chlorella vulgaris.

Biodiesel is a valuable alternative to fossil fuels and many countries choose biodiesel as an unconventional energy source. A large number of investigations have been done on microalgae as a source of oil production. In recent years, wastewater pollutions have caused many ecological problems, and therefore, wastewater phycoremediation has attracted the international attention. This paper studied the cultivation of Chlorella vulgaris in a crude oil polluted environment for biodiesel production. Intended concentrations were 10 and 20gperliter (crude oil/water) at two times. The results showed that the growth of C. vulgaris was improved in wastewater and the maximum amount of dry mass and oil was produced at the highest concentration of crude oil (0.41g and 0.15g/l, respectively). In addition, dry mass and oil yield of the microalga were significantly enhanced by increasing the experiment duration.