On-line identification of fermentation processes for ethanol production

Abstract

A strategy for monitoring fermentation processes, specifically, simultaneous saccharification and fermentation (SSF) of corn mash, was developed. The strategy covered the development and use of first principles, semimechanistic and unstructured process model based on major kinetic phenomena, along with mass and energy balances. The model was then used as a reference model within an identification procedure capable of running on-line. The on-line identification procedure consists on updating the reference model through the estimation of corrective parameters for certain reaction rates using the most recent process measurements. The strategy makes use of standard laboratory measurements for sugars quantification and in situ temperature and liquid level data. The model, along with the on-line identification procedure, has been tested against real industrial data and have been able to accurately predict the main variables of operational interest, i.e., state variables and its dynamics, and key process indicators. The results demonstrate that the strategy is capable of monitoring, in real time, this complex industrial biomass fermentation. This new tool provides a great support for decision-making and opens a new range of opportunities for industrial optimization.

Digital Twin for the SBR Cold Emulsion Copolymerization Process

“…Therefore, in addition to model accuracy, the success of industrial online applications also relies on the process data available and adequate estimation/reconciliation techniques, [4][5][6] as is the case of using a dynamic process model for inferences on unmeasured variables and product properties using the measurements available at the plant site for the online solution of the model equations. [7][8][9] In spite of that, the literature regarding the dynamic modeling of industrial emulsion SBR copolymerization reactions and online identification is rather scarce because most papers focus on modeling the production of polystyrene latex. [10] An early dynamic model for the SBR cold emulsion copolymerization in a train of continuous stirred-tank reactors (CSTR) was proposed by Gugliotta et al [11] The authors extended previous emulsion copolymerization models to estimate the particle size distribution and to consider the effect of some impurities on the reactor train operation.…”

“…Therefore, in addition to model accuracy, the success of industrial online applications also relies on the process data available and adequate estimation/reconciliation techniques, [ 4–6 ] as is the case of using a dynamic process model for inferences on unmeasured variables and product properties using the measurements available at the plant site for the online solution of the model equations. [ 7–9 ] In spite of that, the literature regarding the dynamic modeling of industrial emulsion SBR copolymerization reactions and online identification is rather scarce because most papers focus on modeling the production of polystyrene latex. [ 10 ]…”

Nonlinear Control for Bioprocesses with Model Uncertainties and External Disturbances

“…To improve the results of nominal optimization, a methodology called "run to run" optimization appears, which uses previous runs information to optimize the operation of subsequent ones [31][32][33][34][35][36][37]. Another strategy is the online optimization of the model parameters [38][39][40][41][42][43][44][45]. is kind of optimization is difficult to perform since the available models might only be locally valid and thus inappropriate for predicting final concentrations [46].…”

Water Cycle Algorithm for Modelling of Fermentation Processes

“…The complex structure of FP models is usually described by a set of nonlinear differential equations with time-varying, tightly related process variables [3]. As such, their modelling is not a trivial problem to be solved, and the choice of a suitable optimisation method is essential for the successful identification of model parameters [4,5].…”