Formulation of High-Performance Corrosion Inhibitors in the 21St Century: Robotic High Throughput Experimentation and Design of Experiments

Abstract

Abstract Critical micelle concentration (CMC) is a known indicator for surfactants such as corrosion inhibitors’ ability to partition to water from two phase systems such as oil and water. Most corrosion inhibitors are surface active. At critical micelle concentration, the chemical is partitioned to water from the interface, physisorption on metallic surfaces and forms a physical barrier between steel and corrosive water. This protective barrier thus prevents corrosion initiating on the metal surface. When the applied chemical concentration is equal or higher than the CMC, the surfactant is partitioned to aqueous phase from the oil-water interface. This would lead to higher chemical availability of the inhibitor in water, preventing corrosion. Therefore, it was suggested that CMC can be used as an indicator to optimal chemical dose for corrosion control1-5. The lower the CMC of a corrosion inhibitor product, the better is this chemical for corrosion control as the availability of the chemical in the aqueous phase increases. This can achieve corrosion control with lesser amount of corrosion inhibitor product. Thus, increasing the performance of corrosion inhibitor product. In this work, the physical property, CMC, was used as an indicator to differentiate corrosion inhibitor performance. A vast array of corrosion inhibitor formulations was achieved by combinatorial chemical methods using Design of Experiment (DoE) methodologies and these arrays of chemical formulations were screened by utilizing high throughput screening (HTE)6-8, using CMC as the selection guide. To validate the concept, a known corrosion inhibitor formulation (Inhibitor Abz) was selected to optimize its efficacy. This formula contains several active ingredients and a solvent package. Three raw materials of this formulation were selected and varied in combinatorial fashion, keeping the solvents and other raw materials constant9. These three raw materials were blended in a random but in a controled manner utizing DoE and using combinatorial techniques. Instead of rapidly blending a large amount of formulations using robotics, the design of experiment (DoE) methods were utilized to constrain the number of blends. When attempting to discover the important factors, DoE gives a powerful suite of statistical methodologies10. In this work, Design Expert software utilizes DoE methods and this prediction model was used to explore a desired design space. The more relevant (not entirely random) formulations were generated by DoE methods, using Design Expert software that can effectively explore a desired design space. The Design of Experiment software mathematically analyzes the space in which fundamental properties are being measured. The development of an equally robust prescreening analysis was also developed. After blending a vast array of formulations by using automated workstation, these products were screened for CMC by utilizing an automated surface tension workstation. Several formulations with lower CMCs than the reference product (Inhibitor Abz) were discovered and identified for further study. The selected corrosion inhibitor formulations were blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. As the focus of this project was to optimize the corrosion Inhibitor Abz, this chemical was used as the reference product throughout of this work. The testing indicated that several new corrosion inhibitor formulations discovered from this work outperformed the original blend, thus validating the proof of concept.