Mathematical modeling of xylose production from hydrolysis of sugarcane bagasse

Abstract

Sugar (or more specifically sucrose) is one of the major food carbohydrate energy sources in the world. It is used as a sweetener, preservative, and colorant in baked and processed foods and beverages and is one of lowest cost energy sources for human metabolism. On an industrial scale, sucrose is produced from two major crops-sugarcane, grown in tropical and subtropical regions of the world, and sugar beet, grown in more temperate climates. Sugarcane, however, accounts for the vast majority of global sugar production. For much of the history of sugarcane production, sugar was a scarce and highly valued commodity. Sugarcane processing focused on extracting sucrose as efficiently as possible for the lucrative markets in the United Kingdom and Europe. The potential for the production of alternative products from sugarcane, however, has long been recognized. The key process by-products including bagasse, molasses, mud, and ash have all been investigated as a basis for the production of alternative products (Rao 1997, Taupier and Bugallo 2000). Sugarcane is believed to have originated in southern Asia, and migrated in several waves following trade routes through the Pacific to Oceania and Hawaii and through India into Europe. Sugarcane was introduced and spread through the Americas following the expansion by British, Spanish, and Portuguese colonies in the 15th and 16th centuries (Barnes 1964). While various methods of juice extraction and sugar production have been used over centuries to produce sugar, substantial innovations in sugar chemistry and processing technologies throughout the 18th and 19th centuries have formed the basis of modern sugar production methods (Bruhns et al. 1998). Dramatic improvements in processing efficiency, sugar quality, and automation and control characterized sugar processing throughout the 20th century.

Modelling and dynamic simulation to produce fermentable sugars from lignocellulosic substrates through dilute acid hydrolysis

“…The precise forecasting of acid hydrolysis process behaviour and the identification of optimal operational parameters for the augmentation of fermentable sugars production were facilitated through the integration of comprehensive mass balances for each constituent. These mass balances were derived from the mathematical models of batch reactors, as postulated by Kumar et al, [ 13 ] Greenwood et al, [ 25 ] Tizazu and Moholkar, [ 18 ] Megawati et al, [ 14 ] and Verma and Shastri. [ 26 ] The encapsulated equations, denoted as Equations (), are formulated as a system of ordinary differential equations (ODEs), furnishing the capability to elucidate the intricate dynamics of lignocellulosic waste. …”