Conventionally, designing rigid polyurethane foams (RPUFs) with improved physico-mechanical properties from new, bio-based polyols is performed by modifying foam formulations via experimentation. However, experimental endeavors are very resource-dependent, costly, cumbersome, time-intensive, waste-producing, and present higher health risks. In this study, an RPUF formulation utilizing a coconut-oil (CO)-based polyol with improved physico-mechanical properties was approximated through a computational alternative in the lens of the gel time of the RPUF formation. In the RPUF formation of most bio-based polyols, their very fast gel times negatively impact foam robustness. The computational alternative functioned by finding a CO-based RPUF formulation with a gel time in good agreement with a formulation based on commercial petroleum-derived polyol (control). The CO-based RPUF formulation with the best-fit catalyst loading was approximated by simulating temperature profiles using a range of formulations with modified catalyst loadings iteratively. The computational approach in designing RPUF with improved properties was found to effectively negate foam collapse (with a shrinkage decrease of >60%) and enhance foam strength (with a compressive strength increase of >300%). This study presents an economically and environmentally sustainable approach to designing RPUFs by enabling minimized utilization of material sources for experimentation and analysis and minimized dependence on waste-producing methods.

Bio-derived polyol products have gained global interest as a green and sustainable substitute for fossil-based polyols in a diverse range of polyurethane (PU) applications. According to previous studies, PU properties are highly influenced by the reaction kinetics during their formation. One major factor affecting this is the reactivity of their polyol’s functional hydroxyl moieties that are classified as primary, secondary, and hindered-secondary. However, experimental quantitative characterization of these polyol hydroxyl moieties remains a challenge in the field due to various factors affecting them, including extensive time requirements, the need for substantial and expensive resources, large potential errors, and the generation of wastes, as well as health and safety considerations. In this study, the molar fraction of primary, secondary, and hindered-secondary hydroxyl moieties of a petroleum-based polyol (V490) and a rice straw-based polyol were determined via an iterative computational method. The method employed a MATLAB script that can simultaneously solve multiple differential equations involving PU gelling reaction kinetics and thermodynamics. In this manner, numerical combinations of the fraction of each type of hydroxyl moiety are generated by looping together the respective numerical fractions for each moiety. The best-fit combinations of the fractions of the mixed polyol’s hydroxyl moieties were successfully found via curve fitting of the simulated and experimental gelling temperature profile with an average numerical deviation of less than 1%. Thus, the method presented in this study offers a faster and more reliable characterization of the polymeric reaction kinetics than the experimental and conventional computational methods for product property enhancement and development in the field.

The pursuit of sustainable polyurethane (PU) product development necessitates a profound understanding of precursor materials. Particularly, polyol plays a crucial role, since PU properties are heavily influenced by the type of polyol employed during production. While traditional PUs are solely derived from hydroxyl functionalized polyols, the emergence of amine-hydroxyl hybrid polyols has garnered significant attention due to their potential for enhancing PU product properties. These hybrid polyols are characterized by the presence of both amine and hydroxyl functional groups. However, characterizing these polyols remains a daunting challenge due to the lack of established experimental testing standards for properties, such as fractional hydroxyl and amine moieties and thermo-kinetic parameters for amine reactions with isocyanates. Additionally, characterization methods demand extensive time and resources and pose risks to health and the environment. To bridge these gaps, this study employed computational simulation via MATLAB to determine the moieties’ fractions and thermo-kinetic parameters for hybrid polyols. The computational method integrated energy balance and reaction kinetics analysis for various polyols to elucidate the influence of functional moieties on the thermo-kinetic behavior of PU formations. Validation of the simulated results was conducted by comparing their experimental and simulated prepolymer and foam temperature profiles, highlighting the direct influence of fractional moieties on PU formations. The comparisons revealed an average relative error of less than 5%, indicating the accuracy and credibility of the simulation. Thus, this study represents a pivotal opportunity for advancing knowledge and driving sustainable developments in bio-based polyol characterization for PU production streamlining and formulation optimization.