Isaías Cháirez Hernández

Alkaline pretreatment of Mexican pine residues for bioethanol production

The locally sourced residue samples of Pinus arizonica, Pinus cooperi , and Pinus durangensis from the state of Durango in Mexico were analyzed for optimal yield of ethanol production. The samples were mixed at an equal proportion using a particle size of 0.59 mm. Each individual mixture was pretreated with either NaOH or Ca (OH) 2 (at 0.5, 1.0 and 1.5% w/v) for periods of 30, 60, and 90 min at 60, 90, and 120°C. The pretreated blending was subjected to enzymatic hydrolysis for 130 h at 80 rpm and 50°C with an enzymatic load of 25 filter paper units (FPU) and 50 IU β-glucosidase per gramme of cellulose to obtain a maximum yield of reducing sugars (RS) with NaOH subject at 120°C for 90 min. The results show that the hydrolysis yield depends on temperature and alkali concentration particularly (NaOH), which increased from 2.0 to 3.5% w/v. The best yield of glucose (41.33% w/w) was obtained using a pretreatment of 2.5% NaOH for 90 min, 120°C, and a hydrolysis residence time of 130 h. The removal of lignin and hemicellulose acetylation was observed to have influence on the enzymatic digestibility of cellulose. This process could theoretically produce a maximum yield of 90.19% of ethanol / substrate (glucose) and about 80 L of bioethanol per dry ton of woody biomass from pine residues. Keywords: Lignocellulosic biomass, alkaline pretreatment, enzymatic hydrolysis, fermentable sugars, fermentation African Journal of Biotechnology Vol. 12(31), pp. 4956-4965

USO DE UN REACTOR DE PLACA PLANA (TiO2/VIDRIO) PARA LA DEGRADACIÓN DE 2,5-DICLOROFENOL POR FOTOCATÁLISIS SOLAR

La degradacion de compuestos organicos no biodegradables presentes en aguas residuales es un punto central de la fotocatalisis solar debido a su alta eficiencia y aceptables costos de operacion. Se estudio la degradacion de 2,5-diclorofenol (2,5-DCF) en solucion acuosa con concentracion inicial variable (C 0 ≤ 98 mg/L), por fotolisis y fotocatalisis solar con un reactor de placa plana (1 m 2 ) y vidrio impregnado con TiO2 sintetizado por el metodo sol-gel. Se establecieron dos variables de respuesta, la demanda quimica de oxigeno y la concentracion de 2,5-DCF determinada a 280 nm, por tres factores: angulo de inclinacion (20o y 26o), flujo (355 L/h y 407 L/h) y dos procesos (fotolisis y fotocatalisis). Lo anterior en condiciones de alta (ħ ʋprom = 847.4 W/m 2 ) y baja intensidad (ħ ʋprom = 453.6 W/m 2 ) de radiacion solar, para lo cual se recirculo la solucion acuosa sobre la placa de vidrio durante 60 min, bajo regimen laminar y con muestras cada 5 min. Se determinaron las constantes de velocidad K 1 (min –1 ) y K 2 (mol/L) –1 de orden primero y cero de reaccion, respectivamente. Se consideraron elementos de la dinamica del reactor en la evaluacion de la cinetica quimica de reacciones fotocataliticas. Se alcanzaron porcentajes de degradacion de 2,5-DCF superiores al98 % por fotocatalisis solar.

GRASSHOPPER DENSITY POPULATION CLASSIFICATION WITH NEURAL NETWORKS

Satellite images of the grassland area in Durango Mexico were obtained of altitude, slope, average annual temperature, annual precipitation, type of vegetation, type of soil, normal vegetation index, percentage of herbaceous and percentage of bares soil, in order to relate them with grasshopper density population (GDP) surveyed in 35 sampling sites from June to November in 2003 in the study area. A stepwise regression analysis was performed with the most abundant grasshopper species Phoetaliotes nebrascensis (Thomas), Melanoplus lakinus (Scudder) and Boopedon nubilum (Say) with data extracted from the satellite images. Results showed R > 0.798, F(4,27) > 9.86 and P > 0.000016. The significant variables were normal vegetation index, type of vegetation, altitude and precipitation. GDP raster maps were interpolated using the stepwise regression equations. Then, classification neural networks models were used in order to classify GDP maps. Analysis of percentage of classification error showed that the adequate number of hidden neurons was between six and twelve. Results of error classification were 21% for P. nebrascensis, 5% for M. lakinus and 20% for B. nubilum. Neural networks are practical tools to classify grasshopper population and it will help to take control measurements in overpopulated areas.