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Microalgae biomass is an up-and-coming option for supplying clean and reliable bio-products and energy for future years. However, its industrial production is not yet accomplished due to its economic unfeasibility. To date, several strategies have been used to improve microalgae productivity. Nonetheless, in most of these strategies, the objective function is minimized by nested procedures that have shown limitations dealing with discontinuities, which is very common in microalgae models. This paper describes the application of simultaneous optimization procedures in GEKKO Python package through the exploration of optimal biomass productivity under continuous operation of the strains Dunaliella tertiolecta and Chlorella vulgaris. The results show the successful implementation with fast convergence times: 7.43 s to solve 10800 equations with 900 degrees of freedom in Chlorella vulgaris and 11.45 s to solve 6600 equations with 600 degrees of freedom in Dunaliella tertiolecta. Furthermore, in biological terms, simulations show that once an optimal pH > 8 level is reached in the Chlorella model, the sensitivity of other variables such as Iin decreases dramatically. Therefore, it is possible to achieve high productivity even without increasing the required light intensity. In addition, in Dunaliella case, the results also infer that larger biomass productivity requires larger input substrate concentration.

This paper presents the design, implementation, and characterization of a miniature crab-like walking robot. The first prototype developed is a four-legged servomotor actuated machine that exhibits compact dimensions, low mass, and is capable of overcoming obstacles and moving on irregular terrain and confined spaces. Its bio-inspired design ensures the compliance of its locomotion mechanism even in the presence of external disturbances. The mechanical design, the implementation of the prototype, and its electronic control approach are first discussed. Next, a kinematic analysis characterizing its motion is presented. The aim of this device is to serve as an educational supporting platform for understanding robot kinematics and legged locomotion.

Microalgae have been explored as a promising alternative energy source over the last few years, and several control strategies have been developed to maintain their operational conditions. However, these bioprocesses still pose difficult challenges, due to the nonlinearity and uncertainties of microalgal dynamics. This paper addresses the problem of biomass regulation in a continuous culture of the microalga Dunaliella tertiolecta. Our strategy is based on a Proportional-Integral (PI) controller assisted by a Generalized Proportional Integral (GPI) observer, in order to mitigate the impact of disturbances on biomass levels. The proposed design was tested via numerical simulation, using MATLAB/Simulink™ 2020a environment. The robustness of the controller was demonstrated by subjecting the simulated bioreactor to disturbances and changes in reference biomass densities. The proposed strategy effectively tracked the reference within an insignificant deviation ±0.5%. Furthermore, our proposal effectively rejected disturbances that classical PI was unable to counter.

One of the most significant benefits of the Internet of Things (IoT) is its efficiency. Many companies use this system to automate business and manufacturing processes, remotely monitor and control operations, optimize supply chains, and conserve resources. Smart devices agglomerate a huge amount of IoT data that needs to be analyzed in real-time. Most of the sensors are in remote areas or difficult for people to access, for this reason, it is necessary to analyze in detail the type of technology used as well as the installation site to optimize the use of energy. This work presents a real application of IoT in the monitoring of a building, in addition, an algorithm is used that optimizes the energy used, increasing the battery lifetime. In this way, it is possible to have an intelligent and energy-saving system. This work analyzes the main wireless technologies such as Zigbee, WiFi and LoRa in order to propose the ideal routing protocol depending on performance metrics in a building on a university campus.

This paper presents a contactless environment mapping system for autonomous navigation of mobile robots. Based on ultrasonic sensing, the proposed system obtains reliable and accurate maps of unknown environments without the need of physical contact and regardless of the scene’s illumination. These maps include the location of obstacles and objects in the environment; therefore, they can be used for autonomous navigation and trajectory planning of mobile robots. This paper introduces the design, implementation, and preliminary results showing feasibility of the proposed system.

Microalgal cultures are developing into a promising ecofriendly technology for a host of applications. In this paper we investigate the response of the biomass (XSS), computed from a photo-bioreactor (PBR) algal growth numerical model, to its nine input variables. We explored this response using a Monte Carlo (MC) sensitivity analysis (SA) and Spearman’s Rho concordance coefficient method (MC-Rho). The model represented a continuous culture of the green microalgae Chlorella vulgaris. The photo-bioreactor was simulated using MATLAB as a steady state operation. Four important control variables were selected to represent different states of operation: dilution rate (D), incident light intensity (Im), hydrogen ion potential (pH), and partial pressure of CO2 \( (P_{{CO_{2} }} ) \). For each test point, a statistical array of values was generated of all the input variables with a 2% relative standard deviation. The output values were computed using the model, and then correlated with the input values. The model output turned out to be very sensitive to the four input variables that directly affect the carbon uptake and retention: dilution rate (D), partial pressure of CO2 \( (P_{{CO_{2} }} ) \), mass conversion yield (Yr), and gas-liquid mass transfer coefficient (kLa), in that order. The magnitude of the output relative standard deviation decreased as the output biomass (XSS) increased. However, the ratio of the output standard deviation to the input standard deviation was strongly non-linear and essentially unpredictable outside of a MC simulation.

One of the microorganisms used in the industry to generate high value products is the microalga. Due to its nonlinear dynamics and variation in time, bioprocesses that involve the use of microalgae pose challenges in modeling, controlling, and increasing their production. Normally, at the laboratory level, closed cultures of microalgae are carried out in photobioreactors. To produce biomass, the culture conditions can be controlled using a photobioreactor. This paper describes the design of an intelligent Proportional-Integral controller applied in a continuous culture of Chlorella vulgaris microalga, where the controlled variable is the dilution rate D(t). This controller is characterized by formulating the dynamics of the system with an ultra-local model. In this ultra-local model, both the uncertainties of the system dynamics and the disturbances are handled in one unified way. The performance of the intelligent Proportional-Integral controller was tested at simulation level using Matlab EnvironmentTM. In this research, the accomplishment of the controller was contemplated against disturbances, under nominal conditions and front variation of parameters. The simulations showed that the intelligent Proportional-Integral controller ensures excellent reference tracking capability. Finally, it is important to highlight the robustness of the strategy.

In this paper a complete kinematics analysis of the RV-3SB Mitsubishi robot is perform. An alternative methodology to address the kinematic problem based on the Screws theory is presented. Then, the design of a simulator to validate the methodology proposed, by comparing the values of the simulator with the real values of the robot is presented, MATLAB as a testing tool and programming language is used.

Microalgae are used for the industrial production of high value compounds. The aim in continuous bioreactors is to obtain the highest biomass production. It is necessary to guarantee that the bioprocesses attain and maintain the optimal reference biomass CX*(t), despite endogenous and exogenous disturbances. This paper describes the numerical simulation of the application of active disturbance rejection control (ADRC) to control the dilution rate (D(t)) in a continuous culture of the microalga Chlorella vulgaris. To reduce the bioprocess to a “SISO” system, the authors chose the dilution rate, D(t), to be the only control signal. The control proposal was illustrated and evaluated through a numerical simulation using MATLAB/Simulink™ environment. The performance of the ADRC was tested by the application of external perturbations and variation of parameters over a nominal case. At nominal conditions, D(t) was always maintained within the physical limits imposed by the bioprocess. Step and smooth type signals, at 96.4%·|Dmax(t)|, were imposed as external perturbation on the control signal input, D(t). The controller response kept the output signal CX(t) within an insignificant 0.0043%·|CXmax(t)|. The algal culture had a strongly asymmetric response to variations of the ideal maximum growth rate, μmax(t) ± 30%·|μmax(t)|, and of the nominal light intensity, Iin(t) ± 30%·|Iin(t)|. Nonetheless, the controller promptly returned the output signal to its reference value, CX(t)*. The numerical test of the control proposal, in summary, showed that the ADRC strategy ensures excellent reference tracking capability and robustness towards parametric uncertainties, un-modeled dynamics, and external disturbances.

This paper proposes a method to find an optimal decision curve to manage the incident light intensity ( q 0 ) that is applied to microalgae cultivation to maximize biomass concentration ( C X ) . Microalgae are characterized by the production of high value compounds of interest to industry; the challenge is to obtain the highest biomass concentration. Optimization of the performance of microalgae culture systems is important to guarantee the viability of the economical process. The advantages of this optimization proposal are to attain to CX maximum productivities, as well as its simplicity and its robustness against perturbations. The optimization proposal is illustrated and evaluated on a numerical simulation in a batch culture of microalga Chlamydomonas reinhardtii. Additionally, it was compared to a conventional constant light operation and with an optimization approach based on finding the ratio between q0 and CX (light-to-microalga ratio). In the analysis, the performance of the optimal decision curve was contemplated in presence of the perturbations and variation of parameters. The simulations of proposal in this paper shows an optimal behavior in terms of a maximum production CX · In addition, this would have a better behavior in front of robustness and disturbance rejection capabilities, compared to both, constant light operation and light-to-microalga ratio.

Microalgae are microorganism used for the industrial production of high value compounds. These can be cultivated in closed systems called photobioreactors. In PBR is possible to control the conditions of the culture, with the purpose of increasing the production of biomass or metabolites. The relation between the conditions of growth with the light intensity is essential for the increase of biomass. This paper describes the application of a Proportional-Integral-Derivative controller to control the light intensity within the broth in a batch culture of the Acutodesmus obliquus (Scenedesmus obliquus UTEX 393). The performance of the PID controller was tested with laboratory-level experiments using violet light. In the experiments, conventional lighting operation was compared with constant lighting inside the broth. The experimental results showed an increase in the percentage of lipid production compared to cultures where the intensity of light was not constant (conventional lighting).

12 páginas Looking for more active and motivating methodological alternatives from the students’ perspective, which promote analysis and investigation abilities that make the student a more participative agent and some learning processes are facilitated, a practical study was conducted in the University of La Sabana (Chía, Colombia), in Computing Engineering (INF) and Agroindustrial Production Engineering (IPA) academic programs under the guiding question: Is Second Life an attractive tool for students, does it increment motivation, promote participation and facilitate learning processes in electronic related subjects in Engineering degrees? Second Life (SL) platform was then used as a traditional on-site class complement in electronic related subjects. A follow up was conducted with several groups during a year and a half, some students followed the subject with the traditional on-site methodology and others did it following a mixed methodology that combined traditional on-site lessons with virtual sessions trough SL, making a comparison and applying a quantitative as well as a qualitative methodology for the information analysis and recollection. After the specified study period it was found that using SL as part of the methodology, increments motivation toward electronic related subjects, and promotes participation and investigation, due to the fact that this tool makes learning possible in a funnier, more interactive and deeper way than if it had been taught in a master class. Having real-time access from a virtual classroom to all types of audiovisual information, helps more senses get involved in the learning process, making it more effective; nevertheless a weakness was detected related to the attention and concentration: students tend to get distracted, because their classroom is the computer and from there they have access not only to the many worlds of SL but to all the social networks and the Internet in general.

12 páginas La programación por demostración es una técnica, donde, al contrario de la programación detallada que convencionalmente se hace al robot, éste aprende a partir de una o varias demostraciones por parte de un humano u otro robot. Se presenta una técnica que permite a un robot humanoide, llevar una herramienta, hasta la cabeza de un tornillo, siguiendo una trayectoria similar a las trayectorias demostradas por un ser humano. Además, permite variaciones en la ubicación y orientación, de la cabeza del tornillo. Esto se logra gracias al uso de técnicas de visión de máquina y de modelos probabilísticos estimados a partir de las trayectorias de las demostraciones. El procesamiento de la imagen consistió en la segmentación en el espacio de color, la selección de puntos de interés de la cabeza del tornillo y a partir de estos el cálculo de su posición y orientación. La trayectoria que se requiere ante las variaciones, se genera usando la técnica de modelo de mezclas de Gaussianas parametrizado en la tarea. A través de gráficas de las nuevas trayectorias e imágenes de la secuencia del robot ejecutando estas trayectorias, se muestra el funcionamiento de la técnica. Pese a algunas limitaciones de la plataforma robótica utilizada, se lograron resultados aceptables. Programming by demonstration is a technique where, contrary to detailed programming, the robot learns from one or several demonstrations of the execution of the task by a human or another robot. A technique which allows a humanoid robot, take a tool to the head of a screw, following a similar trajectory than demonstrated trajectory by a human being, is presented. The technique also allows variations in location and orientation in the screw head. This is achieved thanks to the use of machine vision techniques and probabilistic models estimated from the demonstrated trajectories. Image processing consist of color space segmentation, and interest point’s selection in the screw head to calculate its position and orientation. The required trajectory in the presence of variations is generated using parameterized Gaussian mixture models in the task. With the above, the new required trajectories are generated in accordance to the variations. The system operation is presented through the illustration of the new trajectories, and pictures of the robot following these trajectories. Despite some limitations of the robotic platform used, acceptable results were achieved.

Industrial robots are having major involvement in assembly lines, where they are able to identify, classify and even grasp objects and place them in non-default locations; furthermore, assembling pieces autonomously. We describe step by step the image processing to determine the objects orientation located in the field of view of the NAO robot, initial step in posterior developments in task execution using those objects. The processing stages described are: RGB color space segmentation, interest points detection and objects orientation estimation with respect to a reference frame. The results are compared with measures obtained using a commercial image processing software. The absolute error is less than 1.5° in the objects orientation angles, which is considered acceptable due to the size of the processed objects. The introduced methodology is a first step in the execution of assembling tasks or handling of tools by a robotic platform, where the artificial vision has a relevant place in the identification, location and orientation of the objects.