Influence of operating conditions and vessel size on oxygen transfer during cellulase production.

The production of low-cost cellulase enzyme is a key step in the development of an enzymatic-based process for conversion of lignocellulosic biomass to ethanol. Although abundant information is available on cellulase production, little of this work has examined oxygen transfer. We investigated oxygen transfer during the growth of Trichoderma reesei, a cellulase-producing microorganism, on soluble and insoluble substrates in vessel sizes from 7 to 9000 L. Oxygen uptake rates and volumetric mass transfer coefficients (kLa) were determined using mass spectroscopy to measure off gas composition. Experimentally measured kLa values were found to compare favorably with a kLa correlation available in the literature for a non-Newtonian fermentation broth during the period of heavy cell growth.

Improvements in titer, productivity, and yield using Solka-floc for cellulase production.

Researchers studying cellulase enzymes for the economical production of fuel ethanol envision cellulose as the carbon source. However, submerged Trichoderma reesei cultures grown on cellulose exhibit high run-to-run variability. Thus, an investigation of 30 batch cellulase production experiments was instrumental in determining fermentation conditions that improved enzyme titers, yields, and productivities. Eighteen of the 30 batch experiments experienced minimal process upsets and were classified into eight groups based on agitation rate, gas sparge rate, and the use of oxygen supplementation. Comparing corn steep liquor with yeast extract/peptone also tested the effect of different sources of nitrogen in the media. Average 7-d enzyme titers were doubled from 4 to 8 FPU/mL primarily by increasing aeration.

Mathematical modeling and optimization of cellulase protein production using Trichoderma reesei RL-P37.

The enzyme cellulase, a multienzyme complex made up of several proteins, catalyzes the conversion of cellulose to glucose in an enzymatic hydrolysis-based biomass-to-ethanol process. Production of cellulase enzyme proteins in large quantities using the fungus Trichoderma reesei requires understanding the dynamics of growth and enzyme production. The method of neural network parameter function modeling, which combines the approximation capabilities of neural networks with fundamental process knowledge, is utilized to develop a mathematical model of this dynamic system. In addition, kinetic models are also developed. Laboratory data from bench-scale fermentations involving growth and protein production by T. reesei on lactose and xylose are used to estimate the parameters in these models. The relative performances of the various models and the results of optimizing these models on two different performance measures are presented. An approximately 33% lower root-mean-squared error (RMSE) in protein predictions and about 40% lower total RMSE is obtained with the neural network-based model as opposed to kinetic models. Using the neural network-based model, the RMSE in predicting optimal conditions for two performance indices, is about 67% and 40% lower, respectively, when compared with the kinetic models. Thus, both model predictions and optimization results from the neural network-based model are found to be closer to the experimental data than the kinetic models developed in this work. It is shown that the neural network parameter function modeling method can be useful as a "macromodeling" technique to rapidly develop dynamic models of a process.