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Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sen...
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... on the results from the numerical simulations, online observation strategies were implemented, and finally the proposed approaches were validated with experimental data. Figure 2 shows a general scheme of the methodology used in this research work. Firstly, an experimental database of the fermentation process-time evolution was generated with the main performance variables of substrate (í µí²í µí²), biomass (í µí²í µí²), ethanol (í µí±¬í µí±¬ í µí²í µí² ) and carbon dioxide (í µí±ªí µí±ªí µí±ªí µí±ª í µí¿í µí¿ ). ...
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Background
Biofilm-immobilized continuous fermentation has the potential to enhance cellular environmental tolerance, maintain cell activity and improve production efficiency.
Results
In this study, different biofilm-forming genes (FLO5, FLO8 and FLO10) were integrated into the genome of S. cerevisiae for overexpression, while FLO5 and FLO10 gave...
Citations
... Consequently, the design of an observer/soft sensor scheme may be a critical component of process control and monitoring systems. See [23] for fault detection schemes, [24] for a bank-of-observer design, [25] for sliding mode observers, and [26][27][28][29][30] for other estimation and/or control aspects. The main advantages of the use of such soft sensing schemes lie in their cost-efficiency, non-invasiveness and flexibility, as well as the reduction in downtime that may be required compared with traditional sensors in the case of calibration or replacement. ...
... A bank of local linear observers was designed for an alcoholic fermentation process in [24], where the design was based on the respective linear approximants of the mathematical description of the nonlinear process around operating points. A comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production was performed in [25]. The problem of estimation and control for alcoholic fermentation processes was investigated in [26] using adaptive controllers designed together with nonlinear estimation algorithms for unknown inputs and kinetics. ...
The present work focused on the development of soft sensors for single-input single-output (SISO) nonlinear dynamic systems with unknown physical parameters using a switching observer design. Toward the development of more accurate soft sensors, as compared with hard sensors, an extended design methodology for the determination of a bank of operating points satisfying the dense web principle was proposed, where for the determination of the bank of operating points and the observer parameters, a metaheuristic procedure was developed. To validate the results of the metaheuristic algorithm, the case of an alcoholic fermentation process was studied as a special case of the present approach. For the nonlinear model of the process, an observer-based soft sensor was developed using the metaheuristic procedure. First, the accuracy of the linear approximant of the process with respect to the original nonlinear model was investigated. Second, the I/O reconstructability of the linear approximant was verified. Third, based on the linear approximant, an observer was designed for the estimation of the non-measurable variable. Fourth, considering that the observer is designed upon the linear approximant, the linear approximant model parameters are derived through identification, for different operating points, upon the nonlinear model. Fifth, the observers corresponding to the different operating points, constitute a bank of observers. The design was completed using a data-driven rule-based system, performing stepwise switching between the observers of the bank. The efficiency of the proposed metaheuristic algorithm and the performance of the switching scheme were demonstrated through a series of computational experiments, where it was observed that the herein-proposed approach was more than two orders of magnitude more accurate than traditional single-step approaches of transition from one operating point to another.
... The sliding observer methodology was first proposed by Slotine et al. in the middle of 1980s in [20,21]. Since then, various fundamental theoretical frameworks for SMO design have been widely investigated by researchers in the field of control engineering, and the SMOs have been extensively adopted in industrial applications [22][23][24]. However, one drawback of the original SMO with constant gains is that the observer gains need to be chosen carefully to make a trade-off between the estimation error converging rate and the estimation error chattering amplitude [25]. ...
This paper proposes a new generalized adaptive gain sliding mode observer (GAGSMO) for estimating the unavailable states of a class of multi-input multi-output uncertain nonlinear systems. To further improve the estimation performance of conventional sliding mode observer, the observer gains of GAGSMO are designed for the first time as the generalized bounded positive functions of the available output errors and the upper bounds of disturbance terms. Due to the features of the designed observer gains, the GAGSMO has stronger robustness than the conventional sliding mode observer in the presence of system uncertainties and nonlinearities. The finite-time error convergence of GAGSMO is proved by the Lyapunov stability theorem in conjunction with the introduced mapping functions. Then, by catching sight of the inherent feature of sliding motion, a recursive mechanism based only on available estimation information is formulated to update the designed observer gains online in the sliding mode stage. With the recursive mechanism, the chattering level of GAGSMO is minimized, and the estimation accuracy of GAGSMO is further improved. The effectiveness and excellent performance of the proposed GAGSMO are illustrated with two numerical examples.
... Table 3. Comparison of estimation methods applied in AD processes [37][38][39][40][41][42][43][44]. To study the performance of an observer and its robustness, it is necessary to put it in the presence of abrupt perturbations as shown in Figure 8. Finally, it is important to take into account that the AD system may have external disturbances, since these can occur naturally in this type of application, the above may be due to a damaged sensor, poor connections or interference in sampling, for which the simulation was carried out for the case in which the disturbance occurs in the CO 2 sensor, as can be seen in Figure 10 and described by Equation (19): ...
The state variables in a biodigester are predicted using an unstructured model, and this study offers an analytical design of a Non-Linear Logistic Observer (NLLO), subsequently comparing its performance to that of other prominent state estimators. Because of variables such as temperature, pH, high pressure, volumetric organic load (VOC), and hydraulic retention time (HRT), among others, biodigester samples can be affected by the use of physical sensors, which are not always practical owing to their sensitivity to the type of sampling and external disturbances. The use of virtual sensors represents one approach to solving this issue. In this work, we suggest experimentally validating a mathematical model, then analytically designing a novel NLLO observer, and finally comparing the results to those obtained using a sliding-mode estimator and a Luenberger observer. By including online CH4 and CO2 measurements as inputs to the proposed observer, the local observability analysis demonstrated that all state variables were recoverable. After showing how well the suggested observer performs in numerical experiments, a proof based on the Lyapunov theory is offered. The primary innovation of this study is the incorporation of a novel algorithm that has been empirically validated and has output resilience to input parametric perturbations.
... The optimization of this is essential to obtain quality products, and minimize costs, operating times, and environmental pollution [2]. In this sense, online monitoring is essential to carry out control tasks, fault diagnosis, and the determination of the concentrations of the products of interest [2,3]. ...
... The optimization of this is essential to obtain quality products, and minimize costs, operating times, and environmental pollution [2]. In this sense, online monitoring is essential to carry out control tasks, fault diagnosis, and the determination of the concentrations of the products of interest [2,3]. However, this is a complicated, expensive, and sometimes very impractical task, this is due to the complexity of the reaction mixtures, which have to be carried out in controlled environments, with certain nutritional requirements for cell growth to exist and in consequence the production of the metabolites of interest (Products) [3,4]. ...
... In addition, it is very common to find situations where the product to be measured requires prior treatments and/or specific techniques to carry out its quantification, which requires excessively long processing times [3]. It is worth mentioning that many times the instrumentation and available sensors do not always cover all the needs or at least the necessary ones [2]. The low availability of sensors in the market, their high costs, the presence of noise measurement, the operational policies of bioreactors, and their intrinsic nonlinear behavior are strong obstacles to bioreactor instrumentation [2,3]. ...
Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, demonstrate failures in process operations, and implement feedback controllers. However, obtaining the signals of all the important variables in a real process is a task that can be difficult and expensive due to the lack of suitable sensors or simply because some variables cannot be measured directly. From the above, a model-based approach, such as the state observer, can be a viable alternative to solve the estimation problem. This article discusses the real-time performance of a familiar sliding-mode observation strategy to reconstruct key variables in a batch bioreactor for fermentative ethanol production. For estimation purposes, the Hinshelwood model for ethanol production by Saccharomyces cerevisiae is used. The experimental results reported here show that the selected observer performs well since the structure used is robust to uncertainties and detection noise, properties that benefit the bioprocess estimation process.
... The optimization of this is essential to obtain quality products, and minimize costs, operating times, and environmental pollution [2]. In this sense, online monitoring is essential to carry out control tasks, fault diagnosis, and the determination of the concentrations of the products of interest [2,3]. ...
... The optimization of this is essential to obtain quality products, and minimize costs, operating times, and environmental pollution [2]. In this sense, online monitoring is essential to carry out control tasks, fault diagnosis, and the determination of the concentrations of the products of interest [2,3]. However, this is a complicated, expensive, and sometimes very impractical task, this is due to the complexity of the reaction mixtures, which have to be carried out in controlled environments, with certain nutritional requirements for cell growth to exist and in consequence the production of the metabolites of interest (Products) [3,4]. ...
... In addition, it is very common to find situations where the product to be measured requires prior treatments and/or specific techniques to carry out its quantification, which requires excessively long processing times [3]. It is worth mentioning that many times the instrumentation and available sensors do not always cover all the needs or at least the necessary ones [2]. The low availability of sensors in the market, their high costs, the presence of noise measurement, the operational policies of bioreactors, and their intrinsic nonlinear behavior are strong obstacles to bioreactor instrumentation [2,3]. ...
Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, demonstrate failures in process operations, and implement feedback controllers. However, obtaining the signals of all the important variables in a real process is a task that can be difficult and expensive due to the lack of suitable sensors or simply because some variables cannot be measured directly. From the above, a model-based approach, such as the state observer, can be a viable alternative to solve the estimation problem. This article discusses the real-time performance of a familiar sliding-mode observation strategy to reconstruct key variables in a batch bioreactor for fermentative ethanol production. For estimation purposes, the Hinshelwood model for ethanol production by Saccharomyces cerevisiae is used. The experimental results reported here show that the selected observer performs well since the structure used is robust to uncertainties and detection noise, properties that benefit the bioprocess estimation process.
In this article, the problem of real-time estimation of the fermentative ethanol process is tackled. The considered observer is a model-based technique that is robust regarding the model parameter uncertainties and in-line noisy measurements. An unstructured kinetic model was used to describe the production of ethanol in a batch bioreactor for Saccharomyces cerevisiae. The biomass concentration was selected as the measured bioreactor´s output via an in-line device, where the estimate variables were the substrate and ethanol concentrations. An experimental prototype was constructed to demonstrate the observer's real-time performance. The experimental results show that the robust, smooth sliding mode observer performs better than the standard proportional sliding mode observer.
