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Abstract. In this work we propose a monitoring methodology through image processing. The images are collected, in situ and from time to time at the same time samples are collected and are measured analytically, in our case the growth of the microorganism. Using the Fourier Transform with the objective of searching for patterns that can be correlated to the analytical measures of concentration of the microorganisms. The results were, from the point of view of the correlations found between the data, satisfactory. We can conclude that the methodology is feasible, but it needs improvements.

This paper presents the modeling and control of a low-cost experimental module for a laboratory of process control. The plant is a two tanks coupled system with the systems control being done via a DAQ. The modeling was done approximating the system as a first-order one, then, determining the black box and white box models. Also, the process was done varying parameters in the system to compare the resulting poles, which was done changing the orifice in the tank being modeled. After, PI controllers were designed to control the plant, tests were done for different set points and the results analyzed using an algorithm. The script made determined the overshoot and settling time for each run and compared them graphically. Finally, it was determined the effect of each modeling in the resulting controller response.

The purpose of this work was to propose a methodology for monitoring fermentative processes through image processing. The growth of the microorganisms during the fermentation process was evaluated in this work. The images were collected in situ from time to time. At these same time intervals samples of the fermentative medium were removed, analytically measured the concentrations of microorganisms. Using the Wavelet Transform with the objective of searching for patterns that can correlate to the analytical measures of concentration of microorganisms with the images taken during the fermentation process. The results were satisfactory from the point of view of the correlations found between the data. We can conclude that the methodology is possible, but it needs improvements.