Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.

This paper proposes a novel strategy for dynamic open-loop optimization of multivariable nonlinear systems. The methodology is based on the Fourier series and orthonormal polynomials for the control vector parameterization in a sequential direct solution approach. The advantages of this technique are that a few number of parameters is required for optimization and a smooth control profile is obtained. The proposed strategy is evaluated in the case study of recombinant protein production, that is a nonlinear system with two control actions, the substrate and inhibitor feed flow rate. The algorithms are tested through simulations and the results are compared with those published in the bibliography.

This paper proposes a new control law based on linear algebra. This technique allows nonlinear path tracking in multivariable and complex systems. This new methodology consists in finding the control...

The objective of this study is to develop a kinetic model that incorporates time-delay mechanisms to describe the gradual release behavior of pectin. Three models were used: modified Peleg, logistic, and second-order linear with time delay (SOPDT), to represent the extraction kinetics of grapefruit peels at 60, 70, and 90 °C. Model fitting was performed using a hybrid methodology combining Monte Carlo simulations with genetic algorithms. The SOPDT model provided the best fit, with the lowest squared error values (J = 7.06 at 60 °C, 5.63 at 70 °C, and 8.71 at 90 °C), requiring only two parameters to represent the entire process. In contrast, the other models required six. The maximum pectin yield reached over 120% at 90 °C, compared to <80% at lower temperatures. These results demonstrate the potential of the SOPDT model for efficient process description and control.