Jason Anthony Price

Mechanistic modeling of biodiesel production using a liquid lipase formulation

In this article, a kinetic model for the enzymatic transesterification of rapeseed oil with methanol using Callera™ Trans L (a liquid formulation of a modified Thermomyces lanuginosus lipase) was developed from first principles. We base the model formulation on a Ping‐Pong Bi‐Bi mechanism. Methanol inhibition, along with the interfacial and bulk concentrations of the enzyme was also modeled. The model was developed to describe the effect of different oil compositions, as well as different water, enzyme, and methanol concentrations, which are relevant conditions needed for process evaluation, with respect to the industrial production of biodiesel. The developed kinetic model, coupled with a mass balance of the system, was fitted to and validated on experimental results for the fed‐batch transesterification of rapeseed oil. The confidence intervals of the parameter estimates, along with the identifiability of the model parameters were presented. The predictive capability of the model was tested for a case using 0.5% (wt. Enzyme/wt. Oil), 0.5% (wt. Water /wt. Oil) and feeding 1.5 times the stoichiometric amount of methanol in total over 24 h. For this case, an optimized methanol feeding profile that constrains the amount of methanol in the reactor was computed and the predictions experimentally validated. Monte‐Carlo simulations were then used to characterize the effect of the parameter uncertainty on the model outputs, giving a biodiesel yield, based on the mass of oil, of 90.8 ± 0.55 mass %.

Scale-up of industrial biodiesel production to 40 m3 using a liquid lipase formulation

In this work, we demonstrate the scale‐up from an 80 L fed‐batch scale to 40 m3 along with the design of a 4 m3continuous process for enzymatic biodiesel production catalyzed by NS‐40116 (a liquid formulation of a modified Thermomyces lanuginosus lipase). Based on the analysis of actual pilot plant data for the transesterification of used cooking oil and brown grease, we propose a method applying first order integral analysis to fed‐batch data based on either the bound glycerol or free fatty acid content in the oil. This method greatly simplifies the modeling process and gives an indication of the effect of mixing at the various scales (80 L to 40 m3) along with the prediction of the residence time needed to reach a desired conversion in a CSTR. Suitable process metrics reflecting commercial performance such as the reaction time, enzyme efficiency, and reactor productivity were evaluated for both the fed‐batch and CSTR cases. Given similar operating conditions, the CSTR operation on average, has a reaction time which is 1.3 times greater than the fed‐batch operation. We also showed how the process metrics can be used to quickly estimate the selling price of the enzyme. Assuming a biodiesel selling price of 0.6 USD/kg and a one‐time use of the enzyme (0.1% (w/woil) enzyme dosage); the enzyme can then be sold for 30 USD/kg which ensures that that the enzyme cost is not more than 5% of the biodiesel revenue. Biotechnol. Bioeng. 2016;113: 1719–1728.

Real‐time model based process monitoring of enzymatic biodiesel production

In this contribution we extend our modelling work on the enzymatic production of biodiesel where we demonstrate the application of a Continuous‐Discrete Extended Kalman Filter (a state estimator). The state estimator is used to correct for mismatch between the process data and the process model for Fed‐batch production of biodiesel. For the three process runs investigated, using a single tuning parameter, qx = 2 × 10−2 which represents the uncertainty in the process model, it was possible over the entire course of the reaction to reduce the overall mean and standard deviation of the error between the model and the process data for all of the five measured components (triglycerides, diglycerides, monoglycerides, fatty acid methyl esters, and free fatty acid). The most significant reduction for the three process runs, were for the monoglyceride and free fatty acid concentration. For those components, there was over a ten‐fold decrease in the overall mean error for the state estimator prediction compared with the predictions from the pure model simulations. It is also shown that the state estimator can be used as a tool for detection of outliers in the measurement data. For the enzymatic biodiesel process, given the infrequent and sometimes uncertain measurements obtained we see the use of the Continuous‐Discrete Extended Kalman Filter as a viable tool for real time process monitoring.

Application of Uncertainty and Sensitivity Analysis to a Kinetic Model for Enzymatic Biodiesel Production

Abstract This paper demonstrates the added benefits of using uncertainty and sensitivity analysis in the kinetics of enzymatic biodiesel production. For this study, a kinetic model by Fedosov and co-workers is used. For the uncertainty analysis the Monte Carlo procedure was used to statistically quantify the variability in the model outputs due to uncertainties in the parameter estimates; showing the model is most reliable in the start (first 5 hours) of the reaction. To understand which input parameters are responsible for the output uncertainty, two global sensitivity methods (Standardized Regression Coefficients, and Morris screening) were used. The results from both sensitivity analyses identified that only 10 of the 32 parameters are influential to the model outputs. The model was then simplified by removing the non-influential parameters. A parity plot of the simplified model vs. the full model gave a R 2 value of over 0.95 for all the model outputs.

Single Cell Protein from Landfill Gas

Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source.