Calculation of life-cycle greenhouse gas (LC-GHG) emission is necessary to verify the rationale behind any biofuel production project, as a countermeasure for climate change. Here, a process inventory model was constructed based on pilot-scale experiments on biodiesel production using an ion-exchange resin catalyzed (IERC) process. The IERC process prevents soap formation, which also leads to the elimination of water-soluble contaminants entering the product biodiesel i.e., removes the two major problems in recently developed diesel engines with sophisticated injection mechanisms introduced for pollution reduction. Our mass-balance-consistent model reflects the influence of the FFA content in oil on the process inventory; therefore, it is capable of highlighting the characteristics of the technology. Simulated LC-GHG emissions per MJ of biodiesel fuel over varied (0–50 wt%) FFA content of feedstock oil shows that the product has lower LC-GHG emission than that of the fossil resource-based diesel fuel, and that it becomes even lower with a higher FFA content in the feedstock oil. Waste heat utilization would allow for the reduction of LC-GHG emissions to less than half of the fossil-based diesel, while large amounts of LC-GHGs are emitted through the conventional process to achieve fuel quality equivalent to that achieved by the IERC process.
This study proposes a fault detection and isolation (FDI) approach based on a semi-supervised convex nonnegative matrix factorization (SCNMF) algorithm. In contrast to the existing nonnegative matrix factorization (NMF) algorithm, SCNMF uses the convex combination of each class of labeled samples to calculate the clustering centroid of the samples. The convex combination enhances the accuracy of the clustering centroid and improves the clustering performance of SCNMF. Moreover, the SCNMF-based FDI method is suitable for overcoming the challenge of conducting FDI with insufficient labeled samples. Using a case study on FDI for a penicillin fermentation process, the effectiveness of the SCNMF-based FDI method was validated.
In pharmaceutical ingredients production, crystallizing desired polymorph selectively is important. Particularly, manufacturing of unstable or metastable polymorph crystalline products are crucial in pharmaceutical ingredients due to its high bioavailability. In the case of glycine, β-form is the most unstable form and has been investigated about stably crystallizing method in batch operations although it has not been researched about the methods of stably crystallizing in continuous operations. We have developed a continuous flow crystallizer that can generate strong micro mixing effect at reaction zone by utilizing Taylor vortex. We carried out anti-solvent crystallization of glycine to look over reaction conditions that can crystallize β-form stably. We also carried out experiments with varying ratio of glycine aqueous solution and ethanol, inner cylinder rotation speed and residence time. β-form glycine can be obtained over 65% ethanol mass ratio, high inner cylinder rotation speed and long residence time promotes crystallize β-form.