Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Experimental study on the kinetic effect of N-butyl-N-methylpyrrolidinium tetrafluoroborate and poly(N-vinyl-caprolactam) on CH4 hydrate formation

In this work, a series of experiments were carried out to study the kinetic inhibition performance of N-butyl-N-methylpyrrolidinium tetrafluoroborate ([BMP][BF₄]), poly(N-vinylcaprolactam) (PVCap) and compound inhibitor systems on methane hydrate from both macroscopic and microscopic perspectives. In the macroscopic experiments, the influence of the concentration, the ratio of inhibitors, the subcooling on the induction time and gas consumption rate of methane hydrate were studied. The results indicated that [BMP][BF₄] could inhibit the growth rate of CH₄ hydrate, but failed to delay the nucleation. An improved inhibitory effect was observed by combining [BMP][BF₄] and PVCap, and the optimal ratio of the two inhibitors was obtained to gain the best inhibition performance. Furthermore, the microstructure and morphology of methane hydrate crystals formed in different inhibitor systems were investigated through powder X-ray diffraction (PXRD), Raman spectroscopy and scanning electron cryomicroscopy (Cryo-SEM) methods. It was found that [BMP][BF₄] and PVCap had different influences on the large cage occupancy by CH₄ and the morphology of methane hydrate.

Simultaneous application of predictive model and least cost formulation can substantially benefit biorefineries outside Corn Belt in United States: A case study in Florida

Previously, a predictive model was developed to identify optimal blends of expensive high-quality and cheaper low-quality feedstocks for a given geographical location that can deliver high sugar yields. In this study, the optimal process conditions were tested for application at commercially-relevant higher biomass loadings. We observed lower sugar yields but 100% conversion to ethanol from a blend that contained only 20% high-quality feedstock. The impact of applying this predictive model simultaneously with least cost formulation model for a biorefinery location outside of the US Corn Belt in Lee County, Florida was investigated. A blend ratio of 0.30 EC, 0.45 SG, and 0.25 CS in Lee County was necessary to produce sugars at high yields and ethanol at a capacity of 50 MMGY. This work demonstrates utility in applying predictive model and LCF to reduce feedstock costs and supply chain risks while optimizing for product yields.