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Lignocellulosic biomass is an attractive raw material for the production of bioethanol because of its abundance and cheapness, but due to its complex structure the enzymes can penetrate the substrate and obtaining the fermentable sugars from the cellulose is a slow process. As indicated above, lignocellulosic biomass has to pass pretreatments to enhance the enzymatic saccharification. Dilute acid and alkaline H2O2pretreatments show promises to cheapen the process, making the scaling to industrial stage possible. The optimum conditions for the dilute acid pretreatment were time, 33 min, and acid concentration of 1.56% v/v, obtaining a xylose yield of 89.9% and removing 83.2% of hemicellulose. The alkaline H2O2pretreatment obtained a glucose concentration of 62.4 g/L with 5% H2O2, liquid:solid ratio 13.47:1 and time 20.7 h. Tween 80 increased the global glucose yield at 87% with 0.3% w/vof Tween and 5-h contact time. The dilute acid pretreatment removed 83% of the hemicellulose in corncob and the alkaline H2O2pretreatment 56.8% of lignin, obtaining 71% of glucose yield. Finally, Tween 80 increased 19% of global glucose yield.

Introduction: Many adaptive systems are difficult to analyze and to control using traditional techniques. Aims: In this work, we introduce a computerized tool to study agents having a life cycle, which evolve through time, and are subject to stochastic events. Methods: We used population's dynamic techniques and the high capability of computer systems to process data to build a simulator, which was developed using techniques based on evolution and adaptation of the best, known as genetic algorithms, introducing stochastic methods to model situations where the occurrence of events has a given probability distribution. To model the system, it is necessary to set up the software with environmental properties, to define the genotype information for each type of agent, and to include the rules to confer the phenotype. Also, it is possible to shape several aspects like the main data of the life cycle of the agents. Results: With all the data, the soft has an initial population with a required amount of agents and simulates the population's development across the time. For each interval of time it is possible to observe the population's density, to analyze the genetic evolution and to extract statistical data about the main characteristics of each agent. Comments: This tool is appropriate to simulate epidemiological events, because it is possible to simulate the development of one or more populations under different environments and conditions, during different intervals of time. It is also useful to identify the main features that may alter the system.