Global NEST Journal

Unicellular microalgal culture represents a new opportunity for producing significant biofuel quantities in the future along with other specialty products, due to several major advantages microalgae species present when compared to conventional crops, including much faster growth rates, cultivation in a variety of environments and photobioreactor systems, and almost 100% recycling of nutrients. In the current research, the scaling-up of the cultivation of Chlorella vulgaris microalgae to a 4 m3 pilot-plant photobioreactor is examined, compared to the performance of a 25 L automated laboratory bioreactor. Beyond the size and configuration, the main differences of the two bioreactors are the mode of operation, the illumination nature and depth, the temperature, and pH. Specifically, temperature and illumination are naturally varying from day to day and season to season into the pilot-plant photobioreactor that is set inside a greenhouse. The specific growth factor appears to be higher for microalgal cultivation in the laboratory bioreactor. It is also found that the growth kinetics is severely slowed down during the winter months. This is primarily due to the low temperatures and the poor illumination observed during winter.

The school buildings in and around the city of Patras in the prefecture of Achaia, Greece, are classified on the basis of the prevailing environmental conditions and the existing human activities in the surrounding area. School buildings in urban and rural environment are investigated. The type and the sources of pollutants reaching the soil or the air in the school environment, air particles and noise are considered in this investigation. A number of school buildings have been constructed near traffic roads. As a result of this, the school environment is affected by noise and air particles produced from the combustion of petrol by the vehicles. In other cases, fine particles, related to resuspension of soil of the school yard and the surrounding area, affect the school environmental conditions. Similarly, under specific meteorological conditions, some schools in the rural environment can be affected by the agriculture activities, such as the use of fungicides. Specific measures are suggested to be taken for all groups of schools for the protection of the students' and the teachers' health.

Metals are essential elements in the modern technology as they are widely used for several industrial applications such as the manufacturing of electronic devices. Besides common metals, such as copper, iron and aluminium, electronic appliances can contain precious metals as well as metals defined as critical, including rare earth elements (REEs). In this regard, the chance to recycle metals from end-of-life appliances, namely Waste Electrical and Electronic Equipment (WEEE), has benefits for both the economy and the environment in terms of its protection and conservation. However, the existing recycling industry for the recovery of metals from WEEE is still in its infant stage and mostly addressed to the recovery of base metals; precious metals are also extracted, while REEs are practically almost never recycled.
The present work provides a comprehensive overview of the recycling technologies currently applied for the recovery of metals from WEEE, including the recovery of REEs, for which few and fragmented information is available in literature due to the limited experience and the restriction imposed by trade secrets. Advantages, as well as limits of both conventional technologies and recent research developments, are discussed and future perspectives are lastly pointed out.

In this study, a new model for biomass higher heating value (HHV) prediction based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was proposed. Proximate analysis (volatile matter, fixed carbon and ash content) data for a wide range of various biomass types from the literature were used as input in model studies. Optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in order to achieve optimum prediction capability. The best-fitting model was selected using statistical analysis tools. According to the analysis, PSO-ANFIS model showed a superior prediction capability over ANFIS and GA optimized ANFIS model. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE) and coefficient of determination (R2) for PSO-ANFIS were determined as 0.3138, 0.2545, -0.00129 and 0.9791 in the training phase and 0.3287, 0.2748, 0.00120 and 0.9759 in the testing phase, respectively. As a result, it can be concluded that the proposed PSO-ANFIS model is an efficient technique and has potential to calculate biomass HHV prediction with high accuracy.