Automatização do método de concentração micelar crítica (CMC) para calcular a capacidade de troca de cátions (CTC) em argilas bentoníticas

Abstract

In order for bentonite clay to fulfill all its functions, its physicochemical and rheological properties must be carefully controlled to ensure its good performance. Thus, accurately characterizing the Cation Exchange Capacity (CEC) is crucial to guarantee the quality of the analyzed material. Therefore, a commonly used method to calculate the CEC is through methylene blue adsorption, which involves titration of this compound in a solution containing clay, observing the color change to determine the adsorbed quantity, reflecting the sample's CEC. In this context, it has been found that CEC can also be calculated from the Critical Micelle Concentration (CMC) of the surfactant titrated in a clay-containing solution. Thus, the determination of CEC occurs by measuring turning points through the solution's electrical conductivity. However, despite CMC proving to be a more objective method, it still presents a degree of subjectivity. Furthermore, both the methylene blue method and CMC require the execution of repetitive tasks for sample handling, compound dosing, process variable control, and manual conductivity measurement, which constitutes a repetitive and exhaustive mechanical process. Given that measurements in the CMC method can be automatically calibrated, process automation proves to be the best solution to make it more efficient and effective. Consequently, the results for CEC calculation will be more accurate, and the generated data will be more reliable. As a result, in addition to providing more security and promoting process standardization, automation of the procedure through dedicated equipment will mitigate errors in identifying the turning point. Initially, it was determined that conductivity measurement should be performed using a sensor and a system that processes this information to be displayed in real-time on a screen, similar to a typical data acquisition and supervision system. In these circumstances, the development of equipment becomes feasible by employing an embedded system on a single-board computer and using free and open-source technologies. Once monitoring strategies and algorithms were defined, eight samples of different bentonites were characterized using methylene blue as a surfactant. For comparison purposes, tests were conducted in the traditional way and using the equipment. Uniquely, conductivity measurement and the identification of the turning point were carried out in an automated manner, precisely calculating the CEC of the evaluated samples and displaying the result in real-time. The process automation proved to be highly effective and efficient for evaluating the CEC of samples.