Ahrim Yoo

Modeling and Parameter Identification of the Simultaneous Saccharification-Fermentation Process for Ethanol Production

Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil‐based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and “easy” to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification‐fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the modelapos;s parameters, employing experimental data reported in the literature.

Experimental simultaneous state and parameter identification of a pH neutralization process based on an extended Kalman Filter

The pH neutralization process is a representative nonlinear process. If a change in feed or buffer streams is introduced, the characteristics of the titration curve are altered and the way of change in titration curve is highly nonlinear. Moreover, if the changes are introduced in the middle of operation, then the nature of the process becomes nonlinear and time-varying. This is the one of the reason why conventional PID controller may fail. Even though the use of buffer solution may alleviate the nonlinearity, the improvement may be limited. A better way to tackle this type of process is to use nonlinear model-based control techniques with online parameter estimation. However, in most cases, the measurements of the process are not adequate enough so that the full state feedback control techniques can be utilized. If the states and crucial parameters are estimated online simultaneously, the effectiveness of the nonlinear state feedback control can be greatly enhanced. Thus, in this study, the capability of simultaneous estimation of states and parameters using Extended Kalman Filter (EKF) are experimentally investigated for a pH neutralization process. The process is modelled using reaction invariants and the concentrations of reaction invariants of the effluent stream (states) and the feed concentrations (parameters) are estimated online. From the comparison of experiments and simulations, it is found that the states and parameters can efficiently be identified simultaneously with EKF so that the estimated information can be exploited by state-feedback control techniques

Prediction of parathyroid hormone signalling potency using SVMs

Parathyroid hormone is the most important endocrine regulator of calcium concentration. Its N-terminal fragment (1–34) has sufficient activity for biological function. Recently, site-directed mutagenesis studies demonstrated that substitutions at several positions within shorter analogues (1–14) can enhance the bioactivity to greater than that of PTH (1–34). However, designing the optimal sequence combination is not simple due to complex combinatorial problems. In this study, support vector machines were introduced to predict the biological activity of modified PTH (1–14) analogues using mono-substituted experimental data and to analyze the key physicochemical properties at each position that correlated with bioactivity. This systematic approach can reduce the time and effort needed to obtain desirable molecules by bench experiments and provide useful information in the design of simpler activating molecules.

Modeling and identification of the bio-ethanol production process from starch: Cybernetic vs. unstructured modeling

Abstract In this work, an unstructured and a cybernetic model are proposed and compared for the Simultaneous Saccharification_— Fermentation process from Starch to Ethanol (SSFSE), in order to have good, reliable, and highly predictive models, which can be used in optimization and process control applications. The cybernetic is a novel model, which especially considers i) the starch degradation into both glucose and dextrins, and ii) the dynamic behavior of the concentration of the main enzymes involved in the intracellular processes, giving a more detailed description of the process. Furthermore, a new identification procedure based on a sensitivity index is proposed to identify the best set of parameters that not only minimizes the error function, but also contains a fewer number of parameters depending on the initial conditions of the process. Finally, an application of the two models for controlling the SSFSE process using an NMPC (following an optimal reference trajectory for the ethanol concentration) is presented, showing the potential and usefulness of each type of models.

Nonlinear Chemical Plant Modeling using Support Vector Machines: pH Neutralization Process is Targeted

This paper is concerned with the modeling and identification of pH neutralization process as nonlinear chemical system. The pH control has been applied to various chemical processes such as wastewater treatment, chemical, and biochemical industries. But the control of the pH is very difficult due to its highly nonlinear nature which is the titration curve with the steepest slope at the neutralization point. We apply SVM which have become an increasingly popular tool for machine teaming tasks such as classification, regression or detection to model pH process which has strong nonlinearities. Linear and radial basis function kernels are employed and each result has been compared. So SVH based on kernel method have been found to work well. Simulations have shown that the SVM based on the kernel substitution including linear and radial basis function kernel provides a promising alternative to model strong nonlinearities of the pH neutralization but also to control the system.