Dynamics and Control Strategies for a Butanol Fermentation Process

Abstract

In this work, mathematical modeling was employed to assess the dynamic behavior of the flash fermentation process for the production of butanol. This process consists of three interconnected units as follows: fermentor, cell retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). Based on the study of the dynamics of the process, suitable feedback control strategies [single input/single output (SISO) and multiple input/multiple output (MIMO)] were elaborated to deal with disturbances related to the process. The regulatory control consisted of keeping sugar and/or butanol concentrations in the fermentor constant in the face of disturbances in the feed substrate concentration. Another objective was the maintenance of the proper operation of the flash tank (maintenance of the thermodynamic equilibrium of the liquid and vapor phases) considering that oscillations in the temperature in the tank are expected. The servo control consisted of changes in concentration set points. The performance of an advanced controller, the dynamic matrix control, and the classical proportional-integral controller was evaluated. Both controllers were able to regulate the operating conditions in order to accommodate the perturbations with the lowest possible alterations in the process outputs. However, the performance of the PI controller was superior because it showed quicker responses without oscillations.

Recent advances on biobutanol production

“…In other work, Mariano et al [82] used the same model proposed by Mariano et al [81], but the process was optimized using the method of particle swarm optimization to obtain the best operating conditions for butanol production. The same authors proposed the utilization of a servo control in flash fermentation [18]. This work was carried out because in previous studies it has been proved that this process can be used to improve butyric fermentation.…”

“…Alternative methods are reported with the objective to promote a cheaper and efficient separation. More recently, mathematical models have been developed to design the process as well as to simulate its behavior on an industrial scale without the need to carry out experiments to optimize the operational conditions of the reactor [17][18][19].…”

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

“…A well-known application of unstructured models is the design of simulation software of fed-batch penicillin fermentation production [55]. Based on the mathematical modeling, the dynamic behavior of fed-batch bioprocesses can be effectively analyzed so that the best control strategies can be chosen to control the substrate feeding [22,56]. More importantly, when an accurate mathematical process model is available, the dynamic optimization of feeding rate becomes possible to maximize the product yield.…”

“…Numerous literature works have reported applications of advanced control in fermentation processes [2,3,7,8,[18][19][20][21][22][23], involving dynamic programming, online adaptive control, nonlinear optimization, nonlinear control, optimal control, multivariable control, model reference control, fuzzy control, and model predictive control (MPC), etc. These types of methods have gained increasing popularity because of their strong capability in dealing with process nonlinearity, dynamics and optimization.…”

Metabolic Process Engineering for Biochemicals and Biofuels Production

“…For example, a fuzzy-PI controller has been developed to maintain a precise temperature by controlling temperature variation within a narrow range in large scale ethanol production [2]. The mathematic modelling has been successfully developed to assess the dynamic behaviour of bio-butanol fermentation consisting of various interconnected units such as fermenter, cell retention system, and vacuum vessel [3].However, it is hard to directly regulate or manipulate metabolic process parameters (e.g., basal medium, substrate, feed rate and feed formulation) in fed-batch fermentation due to complicated and dynamically variable metabolic activities of microorganism.…”