Direct utilization of kitchen waste for bioethanol production by separate hydrolysis and fermentation (SHF) using locally isolated yeast

Kitchen wastes containing high amounts of carbohydrates have potential as low-cost substrates for fermentable sugar production. In this study, enzymatic saccharification of kitchen waste was carried out. Response surface methodology (RSM) was applied to optimize the enzymatic saccharification conditions of kitchen waste. This paper presents analysis of RSM in a predictive model of the combined effects of independent variables (pH, temperature, glucoamylase activity, kitchen waste loading, and hydrolysis time) as the most significant parameters for fermentable sugar production and degree of saccharification. A 100 mL of kitchen waste was hydrolyzed in 250 mL of shake flasks. Quadratic RSM predicted maximum fermentable sugar production of 62.79 g/L and degree of saccharification (59.90%) at the following optimal conditions: pH 5, temperature 60°C, glucoamylase activity of 85 U/mL, and utilized 60 g/L of kitchen waste as a substrate at 10 h hydrolysis time. The verification experiments successfully produced 62.71 ± 0.7 g/L of fermentable sugar with 54.93 ± 0.4% degree of saccharification within 10 h of incubation, indicating that the developed model was successfully used to predict fermentable sugar production at more than 90% accuracy. The sugars produced after hydrolysis of kitchen waste were mainly attributed to monosaccharide: glucose (80%) and fructose (20%). The fermentable sugars obtained were subsequently used as carbon source for bioethanol production by locally isolated yeasts: Saccharomyces cerevisiae, Candida parasilosis, and Lanchancea fermentati. The yeasts were successfully consumed as sugars hydrolysate, and produced the highest ethanol yield ranging from 0.45 to 0.5 g/g and productivity between 0.44 g L^–1 h^–1 and 0.47 g L^–1 h^–1 after 24-h incubation, which was equivalent to 82.06–98.19% of conversion based on theoretical yield.     

In-Situ Phytoremediation of Pahs Contaminated Soils Following a Bioremediation Treatment

Phytoremediation of pollutants in soils is an emerging technology, using different soil-plant interaction properties. For organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), phytodegradation seems to be the most promising approach. It occurs mostly through an increase of the microbial activity in the plant rhizosphere, allowing the degradation of organic substances, a source of carbon for soil microbes. Despite a large amount of available data in the literature concerning laboratory and short term PAH phytodegradation experiments, no actual field application of such technique was previously carried out. In the present study, a soil from a former coking plant was used to evaluate the feasibility and the efficiency of PAH phytodegradation in the field during a three years trial and following a bioremediation treatment. Before the phytoremediation treatment, the soil was homogenized and split into six independent plots with no hydrological connections. On four of these plots, different types of common plant species were sowed: mixture of herbaceous species, short cut (P1), long cut (P2), ornamental plants (P3) and trees (P4). Natural vegetation was allowed to grow on the fifth plot (P5), and the last plot was weeded (P6). Each year, representative sampling of two soil horizons (0–50 and 50–100 cm) was carried out in each plot to characterize the evolution of PAHs concentration in soils and in soils solution obtained by lixiviation. Possible impact of the phytoremediation technique on ecosystems was evaluated using different eco- and genotoxicity tests both on the soil solid matrix and on the soil solution. For each soil horizon, comparable decrease of soil total PAHs concentrations were obtained for three plots, reaching a maximum value of 26% of the initial PAHs concentration. The decrease mostly concerned the 3 rings PAHs. The overall low decrease in PAHs content was linked to a drastic decrease in PAHs availability likely due to the bioremediation treatment. However, soil solutions concentration showed low values and no signficant toxicity was characterized. The mixture of the herbaceous species seemed to be the most promising plants to be used in such procedure.     

CDES: A pixel-based crowd density estimation system for Masjid al-Haram

CDES is an automatic crowd density estimation system that can be used to estimate crowd density from digital images taken at Masjid al-Haram. Developed using a combination of image processing and artificial intelligence (AI) technologies, CDES possesses the capability to count the number of people in moderately high crowds from a flexibly selected region of interest (ROI). Background removal and edge detection are first applied to the image for crowd feature extraction. Then, the extracted crowd foreground blob pixels are scaled accordingly to correct perspective distortion. Finally, the corrected pixel blobs act as input for the backpropagation (BP) neural network to estimate the number of people within the blob. Using the area of the selected ROI, the crowd density is calculated and classified into five ranges from very low to very high. The experimental results are presented.