Optimal Beer Fermentation

Abstract

A new tool was developed to solve a wide range of optimal beer fermentation problems. Using a mathematical model of beer fermentation, the direct dynamic optimization technique of sequential quadratic programming was investigated for determining the optimal cooling policy to maximize ethanol production for a fixed final time, including effects that optimize flavor. The new tool is very efficient for determining optimal cooling strategies. New control strategies were found that increase ethanol production while decreasing the deleterious effects of fusel alcohols and keeping the acetaldehyde concentrations at moderate levels. Model parameter sensitivity was investigated and it was shown that the system could be regulated successfully around optimal temperature profiles.

The effect of temperature on fermentation and beer volatiles at an industrial scale

“…). Earlier studies by Ramirez and Maciejowski – at a laboratory scale – found that the production of ethanol was reduced with a progressive increase in temperature from 8 to 13.5°C. However, the work of Jonkov and Petkov suggested that the decrease in the content of the acetaldehyde is not directly proportional to the increase in the temperature of fermentation.…”

On the solution of biomass growth rate control in anaerobic fermentation process with quality constraint insight

“…A typical temperature profile ( Fig. 6) indicated also by other authors [17,18,19] is here compared with above introduced isothermal run (Fig. 5).…”

“…This is significant value but it proved to be realistic consideration [15,17]. Constraints on maximum (299.15K) and minimum (278.15K) process temperature is set, as these values are in the range of commonly reported industrial scale fermentations.…”

Optimal control of beer fermentation processes with Lipschitz-constraint on the control

“…Another model for the fermentation of beer was given by Gee and Ramirez . It was based upon previous works by Ramirez and Maciejowski , Gee and Ramirez and Engasser et al . .…”

“…Therefore, the authors proposed a forward–backward sweeping method to solve the problem instead of solving a large nonlinear problem of the discretized version of or applying some genetic optimization algorithm as was done in Ramirez and Maciejowski and de Andres‐Toro et al . , respectively.…”