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This paper describes an organosolv pretreatment of corncob waste to improve its anaerobic digestion for biogas production. Through a thermochemical process based on the use of ethanol and acetic acid, it was possible to separate the fractions of lignin, considered to be a natural inhibitor of anaerobic digestion processes. In addition, with this organosolv pretreatment, the available sugars in the carbohydrates present as monosaccharides, or simple sugars, were depolymerised, facilitating the digestion process. The obtained results include the chemical characterisation of the corncob, the hydrolysate, and the mixture with cow manure, finding that these substrates have potential to be used in anaerobic digestion. The total reducing sugars consumed were 96.8%, and total sugars were 85.75%. It was clearly observed that with the use of pretreatment with organosolv, the production of biogas was superior, because 484 NmL/gVS was obtained compared to the other reported treatments. It was also observed that adding the hydrolysate organosolv increased the production because the values of the control without hydrolysate were 120 NmL/gVS in the bottle experiment. When the experiment was scaled to the 5L reactor, the total volumes of biogas that were accumulated in 15 days of production were 5050 NmL/gVS and 1212 NmL/gVS with and without hydrolysate, respectively. This indicates that the organosolv pretreatment of corncob waste is effective in improving biogas production.

Summary This paper proposes an inverse optimal neural control method of a nonlinear anaerobic bioprocesses model for simultaneous hydrogen and methane production in presence of disturbances. Based on the fundamental properties of the system, a passivity approach is designed such that asymptotic stability is guaranteed. A recurrent high‐order neural network for unknown nonlinear systems in presence of unknown bounded disturbances and parameter uncertainties is proposed to identify nonmeasurable state variables of the system, which are directly related to biofuels production. Optimal control laws based on the neural model are proposed so that the passivation of the entire plant is preserved. The neural control strategy performance for trajectory tracking in presence of disturbances is proven. Results via simulation show the optimal control methodology efficiency to stabilize the H2 and CH4 productions along desired trajectories even in presence of disturbances.

In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection Algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances.