Dynamics of inhibition patterns during fermentation processes-Zea Mays and Sorghum Bicolor case study

Int J Ind Chem (2017) 8:91–99 DOI 10.1007/s40090-016-0105-9 RESEARCH Dynamics of inhibition patterns during fermentation processes-Zea Mays and Sorghum Bicolor case study Neba F. Abunde1 • N. Asiedu2 • Ahmad Addo1 Received: 15 January 2016 / Accepted: 7 November 2016 / Published online: 15 November 2016 Ó The Author(s) 2016. This article is published with open access at Springerlink.com Abstract Recently ethanol production involved the processing and fermentation of sorghum and maize extracts. Sorghum and maize are cheaper, locally available and a substitute to imported barley malt. Large scale ethanol fermentation systems are usually hampered by instability, in the form of oscillations resulting from ethanol inhibition and the lag response of yeast cells to this inhibition. There is limited information regarding the mathematical nature of such inhibitions in the fermentation of sorghum and maize extracts. In the present work, mathematical models are developed to determine the nature of ethanol inhibition during the fermentation of sorghum and maize extracts. The models were sets of coupled ordinary differential equations based on a Monod type cell growth kinetic model that accounts for product inhibition. The Inhibition patterns considered were; Linear, Sudden Growth Stop and Exponential. The results obtained showed that there is product inhibition during ethanol fermentation using sorghum extracts, with inhibition patterns being Linear and Exponential. However, the results obtained from ethanol fermentation of maize extract also showed that there is product inhibition during ethanol fermentation using maize extracts, with inhibition patterns being Linear and Sudden Growth Stop. The obtained models described with high accuracy, 99% Confidence Interval the dynamics of & N. Asiedu 1 Department of Agricultural Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 2 Department of Chemical Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana substrate utilization, product formation and cell growth. These inhibitions which affect the high ethanol yields can be minimized by setting up an optimal control problem using the developed models and solved to determine the control variables that minimize the effect of such inhibitions during the fermentation of sorghum and maize extracts. Keywords Alcoholic fermentation  Mathematical modeling  Ethanol inhibition  Maize extracts sorghum extracts List of symbols lmax Maximum specific growth rate (h-1) qpmax Maximum rate of product formation (h-1) Pxmax Product concentration when product formation ceases (g/100 g) Ppmax Product concentration when cell growth ceases (g/ 100 g) Kix Product inhibition coefficient on cell growth Kip Product inhibition coefficient on product formation Kisx Substrate inhibition coefficient on cell growth Kisp Substrate inhibition coefficient on product formation Ksx Substrate saturation (Monod) constant for cell growth (g/100 g) Ksp Substrate saturation (Monod) constant for product formation (g/100 g) Yx Yield coefficient of cell based on substrate utilization (g/g) Yp Yield coefficient of cell based on substrate utilization (g/g) Gs Yield coefficient of cell based on substrate utilization (g/g h) 123 92 Ms Int J Ind Chem (2017) 8:91–99 Cell growth coefficient on substrate (g/g h) Introduction In several studies regarding the alcoholic fermentation oscillations in batch fermenters resulting from ethanol inhibition and the lag response to yeast cells to this inhibition has been observed and reported. It is often conventional during the modeling of ethanol fermentation to predefine an inhibition pattern but the success of this practice is based on probabilities, since such patterns vary based on the type and strength of the fermentation wort. Sorghum, a cereal which belongs to the family Graminae was first used as a brewing adjunct during the Second World War and is now used in most breweries as locally available alternative to imported barley malt. In recent years, the search for cheaper locally available substitutes to imported barley malt rekindled the involvement of most firms in expensive experiments regarding beer production from various materials and today, most of the more successful firms use maize and sorghum in their beer production process. In a generalized view of processing sorghum and maize for beer production, though involves several unit operations, the fermentation step is regarded as the heart of the entire production where a near optimal environment is desired for microorganisms to grow, multiply and produce the desired product, Alford [1]. However, the fermentation of sorghum and maize extracts at large scale is usually hampered by sub optimal conditions including instability, in the form of oscillations resulting from ethanol inhibition and the lag response of yeast cells to this inhibition, Chen and McDonald [2, 3], Beuse et al. [4], Fengwu [5]. These inhibitions observed results in an increase in residual sugar at the end of the fermentation, which decreases raw material consumption and correspondingly, decreases the ethanol yield if no economically acceptable attenuation strategies are developed, Fengwu [5]. In a typical procedure for modeling ethanol fermentation, if inhibition is considered, it is often conventional to predefine the inhibition pattern and this practice increases uncertainties in the model since ethanol inhibition pattern varies depending on the type of microorganism, and on the type and strength of fermentation wort, Russell [6]. This increase the unreliability of process controllers and simulators since these automatic tools are usually based on a mathematical representation of the considered system, Alford [1]. Dynamic models were developed, incorporating three effects of product inhibition into the kinetic model and simulation resulted in interesting findings. 123 Model development The fermentation process kinetics was described with a Monod type cell growth model that accounts for substrate and product inhibition. Modeling kinetics of growth and product formation Starting from the Monod Equation for cell growth and product formation, Eq. (1), three inhibition patterns were considered in modeling product inhibition; linear, Sudden growth stop and exponential as shown in Table 1 below. lðSÞ ¼ lmax S K sx þ S ð1Þ qpmax S K sp þ S ð2Þ qp ð S Þ ¼ Introducing the effect of product inhibition on the Monod equation, using the respective inhibition factors, the following kinetic models were obtained: Kinetics with Linear Product Inhibition, -Hinshelwood– Dagley model [7] lmax S K sx þ S ð3Þ   qmax S qðS; PÞ ¼ 1  K ip P K sx þ S ð4Þ lðS; PÞ ¼ ð1  K ix PÞ Kinetics Sudden Growth Stop Product Inhibition, Ghose and Tyagi [8]   P lmax S lðS; PÞ ¼ 1  ð5Þ Pmax K sx þ S   P qmax S ð6Þ qðS; PÞ ¼ 1  Ppmax K sx þ S Kinetics with Exponential Pr (...truncated)