Presenter: Jian Shi
Advisor(s): Dr. Ratna R. Sharma
Author(s): Jian Shi, Ratna R. Sharma, Mari S. Chinn, and Ralph A. Dean
Graduate Program: Biological and Agricultural Engineering, Biological and Agricultural Engineering, Biological and Agricultural Engineering, Plant Pathology (Fungal Genomics)
Title: Studying the Kinetics of Microbial Pretreatment of Cotton Stalks by Phanerochaete chrysosporium
Abstract: Cotton stalks left in the field after harvest lead to disposal issues and difficulty in cultivation apart from being a potential cause for cotton diseases and pests. However, the lignocellulosic characteristic of cotton stalk makes it a promising resource for converting to fuel ethanol thereby providing a solution to these problems and benefiting both the environment and economy. Investigation of pretreatment processes is the first step towards converting this renewable feedstock into a value added product. Microbial pretreatment, utilizing a white rot fungus, Phanerochaete chrysosporium, offers advantages such as energy-saving, environmental friendliness, simpler equipment, and low cost, and provides a “green” alternative to prevalent high energy physio-chemical pretreatments.
This study was therefore undertaken to optimize the treatment parameters including culture conditions, salt supplementation, moisture content and pretreatment time of two microbial pretreatment strategies, submerged cultivation (SmC) and solid state cultivation (SSC). Severity and statistical response surface (RSM) models were developed through regression of the above variables with time, lignin reduction and fermentable sugar availability. Agitated SmC supplemented within NREL salts and SSC at initial moisture of 75% without salts, resulted in highest lignin degradation at 30.8% and 27.7% respectively.
Besides establishing the statistical model, models providing in depth information or the kinetics of a microbial pretreatment process are being developed. Based on the assumptions that lignin and holo-cellulose are the limited carbon sources (substrate), the models describe the relationship between substrate consumption, lignin-degrading enzymes production (products), and fungal growth. Fungal growth is quantified indirectly by measuring the change in total protein (Total Kjeldahl Nitrogen,TKN) and correlated with oxygen uptake rate (OUR) and carbon dioxide production rate (CPR). The models will be validated by comparing predicted and experimental values. It is expected that these models will be invaluable for microbial pretreatment process scale up and optimization.