A substantial barrier to commercialization of lignocellulosic ethanol production is a lack of process specific sensors and associated control strategies that are essential for economic viability. Current sensors and analytical techniques require lengthy offline analysis or are easily fouled in situ. Raman spectroscopy has the potential to continuously monitor fermentation reactants and products, maximizing efficiency and allowing for improved process control.

In this paper we show that glucose and ethanol in a lignocellulosic fermentation can be accurately monitored by a 785 nm Raman spectroscopy instrument and novel immersion probe, even in the presence of an elevated background thought to be caused by lignin-derived compounds. Chemometric techniques were used to reduce the background before generating calibration models for glucose and ethanol concentration. The models show very good correlation between the real-time Raman spectra and the offline HPLC validation.

Our results show that the changing ethanol and glucose concentrations during lignocellulosic fermentation processes can be monitored in real-time, allowing for optimization and control of large scale bioconversion processes.

Despite the existence of various methods to remove cosmic spikes from Raman data, only a few of them are suitable for process Raman spectroscopy. The disadvantages of these algorithms include increased analysis time, low accuracy of spike detection, or reliance on variable parameters that must be chosen by trial and error in each case. We demonstrate a novel approach to detecting cosmic spikes in process Raman data and validate it using a wide range of experimental data. This new method features a multistage spike recognition algorithm that is based on tracking sharp changes of intensity in the time domain. The algorithm effectively distinguishes cosmic spikes from random spectral noise and abrupt variations of Raman peaks, allowing accurate detection of both high and low intensity cosmic spikes. The procedure is free from variable user-defined parameters and operates reliably in a fully automated manner with a wide range of time-series process Raman data sets containing more than 40 to 50 spectra.

The pharmaceutical industry has great need to reduce costs and improve manufacturing efficiency while consistently manufacturing quality drug product. Continuous flow reactors (CFRs) offer an option for a flexible, leaner, and cleaner process. CFRs are flow cells optimized for the continuous production of a target compound. Small-volume CFRs avoid some of the reaction control problems associated with scale-up to large-batch chemistry, while still allowing for process intensification through modular scale-out. Compared to batch reactors, CFRs are more energy efficient due to their superior mixing schemes and heat transfer properties. Reactions typically proceed much faster than in batch and require less excess solvent. Currently, CFRs are limited by the technologies available for online monitoring, which require product stream sampling and off-line analytics. This work addresses the analytical challenges inherent to CFRs by demonstrating the ability to assess the quality of product from a reactor stream rapidly, using effective online sampling and monitoring systems. Additionally, following the Quality by Design paradigm, techniques such as statistically based design of experiments, process analytical technologies, and multivariate statistical modeling methods were implemented to facilitate enhanced product and process understanding. An online sampling system that is able to interface with CFRs was developed, with custom software to monitor and control process variables using online analytics. Knowledge of these in-process parameters, combined with appropriate online process analytical technologies, provided a complete understanding of both the physical and chemical system. These improvements reduced the time an