The influence of the temperature-dependent viscosity on the electroosmotic flow through a porous medium in a microchannel of parallel flat plates. At the end, an imposed electric field induces Joule heating and temperature gradients, modifying the viscosity of the fluid. The Phan-Thien–Tanner and Darcy–Forchheimer models describe the viscoelastic fluid through the porous media. Consequently, the electric double layer (EDL) overlaps at the center plane of the microchannel, causing an electrically charged porous matrix and modifying the zeta potential along the microchannel walls. Therefore, we define a zeta potential ratio of the porous matrix and that at the microchannel walls. Unlike similar investigations, the boundary condition for the zeta potential varies locally due to the temperature field, modifying the interaction among hydrodynamics, electrical, viscoelastic, thermal and porosity effects. A modified Péclet number is defined as a function of the porosity and thermal conductivity of the fluid and an effective charge density is determined based on the Poisson–Boltzmann equation, accounting for the pore size and the zeta potential ratio. The set of equations are solved numerically considering the lubrication approximation theory. We found that the influence of the temperature-dependent viscosity increases the shear stresses at the EDL region, particularly where the temperature rises to maximum values. Also, the overlapping of the EDL occurs mainly for low values of permeability. Finally, the pressure gradient involved in the Darcy law can be altered through the zeta potential ratio.

The operation of an inverted pendulum and its respective type of control are affected by the change of the values of its internal parameters. Changes with high uncertainty result in responses with undesirable outputs. In this work, a comparison is presented for the control of an inverted pendulum to determine the operation and characteristics of three types of control systems: Neuro-Fuzzy Control (NFC), Indirect Adaptive Control (IAC) and a Proportional Integral Derivative control (PID). The study considers several indices such as stabilization time, rise time, mean square error, overshoots, convergence, computational load, error, mathematical requirements, and performance indices for control systems. To demonstrate its operation, the controls are implemented in hardware, one for the NFC and another for the IAC under an Arduino UNO platform. The results indicate that the NFC and IAC controls do not generate a transient or impulse response, only a small delay and the rise and stabilization time are minimal. While PID presents a transient response and overshoot, as well as a stabilization time to reach the steady state response.

Power generation using renewable technologies has become a primordial option to satisfy the energy demand all over the world, with solar concentrating technologies widely applied for this purpose. A combination of a parabolic trough collector with direct steam generation has been considered an excellent option for power generation as the economic cost and complexity in the plant are reduced. The thermal evaluation of a solar power plant as well as the PTC in the DSG process is very important in viability and economic analyses. In this sense, as the main objective of this work, a numerical tool for evaluating DSG with PTC technology was developed. The SOLEEC software is a versatile, reliable, accurate, and user-friendly option to thermally evaluate a DSG with PTC technology. The user has the possibility of comparing the thermal behavior of different geometrical dimensions for a PTC and even consider different materials to satisfy the demand of superheated steam by a DSG process. The software has an error of less than 5% when compared with the literature results and was used in this paper to evaluate a power plant in Mexico, showing that the change to DSG proposing different PTC could reduce the solar field by about 35%.

Solid-state fermentation (SSF) is a process used to produce enzymes and secondary metabolites; however, its low efficiency limits its application, as it does not cost-effectively meet market demand. This article proposes modeling the operation and determining the optimal parameters of SSF processes through the application of artificial intelligence systems. To this end, programming algorithms were designed in MATLAB software to implement an artificial neural network (ANN), a genetic algorithm (GA), particle swarm optimization (PSO), and the artificial bee colony (ABC) algorithm. To verify the proposed method, the production of proteases used in the cheese industry was modeled and optimized. The results show that the optimal process parameters were correctly identified, the modeling precision and accuracy were increased (R² > 0.90), and the process resources required can be reduced. These findings suggest that the use of artificial intelligence systems in SSF processes is an effective tool to maximize their production.

This paper aims at developing an intelligent controller with fuzzylogic technique using LabVIEW application and its control procedureapplied to a solar tracking system.