Predictive temperature modeling and experimental investigation of ultrasonic vibration-assisted pelleting of wheat straw

Abstract

Ethanol made from cellulosic biomass is an alternative to petroleum-based liquid transportation fuels. However, large-scale manufacturing of cellulosic ethanol is hindered by several factors. The main factor driving this hindrance is the low density of cellulosic biomass. Ultrasonic vibration-assisted pelleting can effectively increase cellulosic biomass density by compressing raw biomass into pellets, reducing transportation and storage costs. Pelleting temperature has also been identified as a key parameter influencing pellet quality. In this paper, a predictive mathematical model of pelleting temperature using spatio-temporal dynamics was developed to study multiple factors affecting temperature rise through pelleting. The mathematical model was then validated with experimental data along with high goodness of fit (average R2>0.83). Effects of three input variables (ultrasonic power, pelleting pressure, and pellet weight) on temperature ranges (highest temperature point and lowest temperature point) were investigated using a 23 (two levels and three variables) factorial design. Our results indicated that friction between mold and biomass has a marginal effect on the temperature profiles, and demonstrated the highest and lowest temperature points are significantly correlated to the input variables (ultrasonic power, pellet weight, and pellet pressure) and their interaction effects. The proposed mathematical model delivers a new guideline by avoiding unnecessary experiments and provides a systematic understanding of temperature profiles during the biomass pelleting process. Knowledge transferred from the current study fulfills the literature gap between mathematical modeling research and an optimal, ultrasonic, vibration-assisted pelleting process; and, therefore, provides insight into improving biomass quality in energy-related ultrasonic manufacturing.