Phenomenological Based Semi-physical Model Research Articles

Phenomenological-based semi-physical model characterizing the central metabolic response of Escherichia coli to changes in batch culture temperature

This work presents a phenomenological-based semi-physical model characterizing the E. coli response to environmental temperature changes in batch cultures. The proposed model is based on a general description of the mass balance of the central metabolism and an energy balance that focuses on key parameters. This model can represent changes in specific growth rate and alteration to the mass flows of the central metabolism. Moreover, the model can make predictions regarding microorganism growth, respiratory rate, glucose consumption, acetate accumulation, and heat exchange between E. coli and its environment at several different temperatures. The fit and validation of the model was assessed against published data obtained under aerobic growth conditions in agitated tank bioreactor cultures. Our assessment of this model indicates that mass and energy balances can predict global changes in E. coli metabolism that are associated with different temperature conditions and differences in heat flow between bacteria and the environment. This model can be used to correlate environmental conditions with experimental bioreactor parameters and simulate microorganismal response during the design of temperature control systems for bioreactors and can even be used as a base model to represent heat-inducible E. coli strains used in recombinant protein production.

Heat flow modeling in the alkaline activation process of fly ash

Cement production plays an important role in strengthening the infrastructure of growing countries such as Colombia. However, the production of this material has a high energy cost and contributes to the emission of large amounts of CO2. To address these environmental concerns, it is essential to explore alternative materials that can partially or completely replace traditional cement. Alkaline activated cement (AAC) has emerged as a promising candidate in this regard. Due to this, it is necessary to understand the process of alkaline activation and the variables that influence it. This research proposes a phenomenological-based semi-physical model, which predicts the performance of some variables that control alkaline activation: activator concentration (NaOH), heat flow, and degree of reaction. The model results show that with the increment of the activator concentration, the degree of reaction also increases. Furthermore, the model has an accurate response compared with the Freisleben-Hansen model. The integral square error criterion (ISE) was used in this comparison.

Heat flow modelling of the alkaline activation of fly ash with sodium hydroxide in the presence of portlandite

Alkali-activated cement is a promising alternative to replace totally or partially Portland cement. The heat flow and the degree of reaction are widely analyzed variables on the alkali-activation process. In this paper, a phenomenological-based semi-physical model of the alkali-activation process was developed. The model predicts the heat flow and the degree of reaction, by correlating the composition of the fly ash, the NaOH concentration (alkaline activator), the concentration of portlandite and curing temperature. The model considers balanced chemical reaction and the process diffusive phenomena. For validation microcalorimetric measurements at 25 °C and 35 °C was made, and secondly Freisleben-Hansen model was fitted with the experimental data. The results indicate a reaction acceleration for concentration of NaOH in (6–10) M and a reaction deceleration due to portlandite. The fly ash particle size and diffusivity-associated factors have an incidence of more than 5 % on the heat flow and degree of reaction.

A phenomenological-based dynamic model for a polymer electrolyte membrane fuel-cell humidifier assembly

This paper presents the deduction of a phenomenological-based semi-physical model (PBSM) for the gas humidification in a fuel-cell assembly. Unlike previously published models, the one proposed here includes the momentum transfer effects and their influences over simultaneous heat and mass transfers occurring into the process as a whole. These effects strongly affect the model precision due to the compressible characteristics of the air. High sensitivity of gas properties regarding changes in both pressure and temperature are also included by modelling the gas as a compressible fluid. The model reproduces the air–water vapour mixture behaviour from the compressor discharge to the fuel-cell inlet port. Since the air humidity and temperature conditions must be maintained at their set-points to guarantee an optimal fuel-cell performance, a model such as the one presented here can be used for designing any model-based control strategy towards achieving desirable operative conditions taking also into account the operational context of the considered assembly. The comparative assessment done with experimental data from a real test bench has shown the effectiveness of the proposed model in accurately reproduce the behaviour of such complex systems.

Modeling Based Identifiability and Parametric Estimation of an Enzymatic Hydrolysis Process of Amylaceous Materials.

This work presents the modeling of an enzymatic hydrolysis process of amylaceous materials considering the parameter identification problem as a basis for the construction of the model. For this, a modeling methodology is modified in order to apply the identifiability property and improve the proposed model structure. A brief theoretical explanation of the identifiability is described. This concept is based on the observability property of a nonlinear dynamic system. The used methodology is based on the phenomenological based semiphysical model (PBSM). This methodology visualizes that the structure of a dynamic model can only improve with new mass or energy balances suggested by model suppositions. Additionally, a computer algorithm is included in the methodology to validate if the model is structurally locally identifiable or know if the parameters are unidentifiable. Also, an optimization algorithm is used to obtain the numeric values of the identifiable parameters and, hence, guarantee the validity of the result. The methodology focuses on the liquefaction and saccharification stages of an enzymatic hydrolysis process. The results of the model are compared with experimental data. The comparison shows low errors of 7.96% for liquefaction and 7.35% for saccharification. These errors show a significant improvement in comparison with previous models and validate the proposed modeling methodology.

On-line estimation for predicting the “Conching Degree” (CD) and the viscosity of chocolate

The conching process is one of the main stages of chocolate manufacture involving the development of sensory attributes and the product rheological properties. At the current operation of the conching process, the on-line estimation of quality properties that define an optimal development of the chocolate is not possible. In that sense, the on-line estimation of the “Conching Degree” (CD) as a flavor quality indicator and the chocolate viscosity as a textural quality attribute are fundamental tasks for guaranteeing process monitoring and real-time decision making. To this aim, this paper proposes a Moving Horizon Estimator (MHE) for the on-line estimation, instead of measurement, of the CD and the viscosity of chocolate. In this formulation, only available physical measurements of process variables easy to obtain are required, avoiding time-consuming and extensive conventional laboratory methods to quantify these chocolate's quality indicators. The estimator is designed as an optimization problem over a moving horizon, constrained to a Phenomenological Based Semi-Physical Model (PBSM) describing the conching process main phenomena. The performance of the proposed estimator is evaluated by simulation with data from a real plant. Finally, the proposed optimization-based estimation structure allows estimating the CD and the viscosity of chocolate with estimation indices errors lower than 6% considering only two available measurements in the process. • Moving Horizon Estimator (MHE) for the estimation of CD and viscosity of chocolate. • CD and viscosity of chocolate are estimated considering only two measurements. • The estimator is constrained to a Phenomenological Based model describing the conching process. • The estimation of CD and viscosity allows real-time process monitoring. • The estimator uses easy-to-obtain measurements avoiding extensive laboratory methods.

Phenomenological-based model of glucose transport from liver to abdominal subcutaneous adipose tissue

BackgroundA good treatment for type 1 diabetes mellitus (T1DM) requires accurate measurements of blood glucose levels. Continuous glucose monitors (CGM) measure the glucose concentration in the interstitial fluid of the abdominal subcutaneous adipose tissue. However, glucose measured in the abdominal interstitial fluid does not represent blood glucose concentrations accurately due to the complex blood transport through the body and glucose diffusion in interstitial fluid. MethodsTo gain insight into this problem, a phenomenological-based semiphysical model (PBSM) of glucose transport by volumetric flow and diffusion from the bloodstream to interstitial fluid was constructed. A published 10-step modeling procedure was used to obtain a model for glucose transport time through the blood vessels and from the blood capillaries to the interstitial fluid, glucose diffusion within the interstitial fluid, and glucose diffusion through the semipermeable coating of the sensor needle. For this model, a healthy person is considered at rest with average parameters. ResultsThe simulations performed using the PBSM allow obtaining the glucose transport time from the liver to the sensor needle. In this way, it is possible to reconstruct an accurate dynamic measurement of blood glucose from the measurements in the interstitial fluid of the abdominal subcutaneous adipose tissue. ConclusionsPBSMs with parameters interpretability illustrate the connection of glucose concentrations in the interstitial fluid with that currently in the blood. Implementing this model in a CGM will result in more reliable measurements of blood glucose levels for T1DM treatment.

Phenomenological‐based semiphysical model to predict the water holding capacity of processed meats in the mixing process

AbstractThis study proposes a phenomenological‐based semiphysical model (PBSM) for the mixing process to predict meat protein denaturation and their interactions with water molecules when salts and phosphates are present. Protein fractions are measured according to their solubility, which is an indicator of structural changes. A reaction scheme is proposed based on the literature review and the consolidation of individual experiments carried out at an industrial scale mortadella fed‐batch reactor. The model is simulated with numerical integration of a set of ODEs governing the time‐evolution of species concentrations. The results show that the model was able to predict the water‐holding capacity at standard formulation and operational conditions.Practical applicationsThe knowledge of the effects of processing conditions on the structure and the interactions of muscle proteins contributes to understanding the impact of processing on meat product quality. The phenomenological‐based semiphysical model developed in this study has broad applicability in the food industry since it is the first attempt to link meat protein composition with the functional property water‐holding capacity mathematically. The high interpretability of model parameters provides a deeper understand of the process phenomena; so that the model becomes a way to develop strategies to improve the process.

A phenomenological-based semi-physical model of the kidneys and its role in glucose metabolism

The kidneys play an important role in glucose homeostasis in three ways: Via endogenous glucose production from non-carbohydrate precursors (e.g. glutamine, lactate, alanine, glycerol) during both postprandial and post-absorptive states; via glucose filtration and reabsorption by the glomerulus and proximal tubule, respectively; and via glucose utilization and the elimination of its excess in the urine when glucose levels exceed 180mg/dl. The renal release of glucose into the circulation occurs mainly in the renal cortex and results from the glucose phosphorylating capacity of those renal cells, meaning that, cells in the renal cortex can form glucose-6-phosphate. Considering glucose filtration and reabsorption, the kidneys filtrate and reabsorb all circulating glucose, rendering the urine virtually glucose-free in a healthy person. Finally, the kidneys take up glucose from the circulation for energetic self-supply. Besides their role in glucose metabolism, the kidneys are the major site of insulin clearance from the systemic circulation, removing approximately 50% of peripheral insulin. In this regard, insulin clearance by kidneys occurs by degradation in the proximal tubule after being filtered in the glomerulus. All the aforementioned mechanisms affect the glucose concentration levels in the blood, preventing the parametrization of a mathematical model for patients with diabetes mellitus, in the implementation of an artificial pancreas. Aiming for a complete physiological model of the glucose homeostasis, a physiological submodel of the kidneys is presented in a way not described in the literature so far. This submodel is a phenomenological-based semi-physical model with a basic structure rooted in the conservation law and for which the parameters are interpretable. The model’s results coincide well with the available clinical data reported for kidney functions associated with glucose and insulin.

Dynamic modelling of alkaline self-pressurized electrolyzers: a phenomenological-based semiphysical approach

This paper proposes a phenomenological based semiphysical model (PBSM) for a self-pressurized alkaline electrolyzer. The model, based on mass and energy balances, represents the dynamic behaviour of hydrogen and oxygen production using electrolysis. The model allows to anticipate operational variables as dynamic responses in the concentrations of the electrolytic cell, and variations in both, level and pressure, at the gas separation chambers due to the change in electric current. The model parameters have been adjusted based on experimental measurements taken from an available prototype and through a suitable identification process. Simulation results replicate the current dynamic response of the experimental self-pressurized electrolyzer assembly. This model proves to be useful in the improvement of the control of gas production rate in this kind of assemblies, both as a validated simulation platform and as a source of reduced order models for model-based control design.

Approximation of Scale-Up of Enzymatic Hydrolysis Process from Phenomenological-Based Semiphysical Model and Control Theory Tools

This study highlights the Scale-up of enzymatic hydrolysis of wheat starch from the phenomenological-based semi-physical model and control theory tools. The state variables involved were glucose co...

Flash Distillation Control Using a Feasible Operating Region: A Sliding Mode Control Approach

The aim of this work is to develop a sliding mode control scheme to keep the operation of a flash distiller in the feasible operating region. Flash distillation is characterized as a simple single-phase separation system but operationally complex because it has a strong nonlinear behavior due to the thermodynamic equilibrium, obligatory to reach the required component separation. Control of flash distillation processes must guarantee a good performance and robustness to achieve preseparation quality before entering to next fractional distillation processes. A phenomenological-based semiphysical model is used here to represent the flash distillation process. The proposed control structure tries to avoid the biggest problem of the flash distiller: its recurrent restart due to temperature disturbances in the feed flow. The suggested control schemes, applied to a given separation, allow regulation of the pressure and the liquid level of the flash drum, keeping also the concentration of propylene glycol at the flash vapor outlet near its set point, rejecting temperature disturbances. The simulation results show the features of the proposed controllers, which overcome some of the disadvantages of typical flash distiller controllers operating in design conditions.

A Novel Phenomenological Based Model of an Upstream Hydro-classifier for Control Purposes

Abstract Hydro-classification is one of the most important operation to separate particles according with their size and specific gravity. This operation involves many phenomena and its modeling is still not clear. Complex models demand high computational resources and are used to design the dimension of devices or predict the classification but off-line. However, for control proposes, it is necessary to get a suitable model with acceptable prediction level to be implemented online. This work presents a phenomenological based semi-physical model (PBSM) that describes the phenomena of the hydro-classification and forecasts its performance according to the operative conditions, summarizing all phenomena in the main physical properties of production.

Phenomenological based Semi-Physical Model for the Milk Evaporation Process

The aim of this study was present a Phenomenological Based Semi-physical Model (PBSM) for the milk evaporation process. The evaporation is the elimination of solvent in form of water steam from a solution. In the dairy industry, the evaporation is a pretreatment for the powder milk processes that reduce the cost of the drying process, storage and transport. Thus, an appropriated mathematical model is necessary in order to get a good control and optimized process of milk evaporation, which allows obtaining a high-quality product. Previous studies have presented mathematical models for the evaporation process. However, the majority of these models are based on maintaining constant values such as the thermal properties of fluids, latent heat of vaporization and global coefficient of heat transfer, among others and this means that there will be high errors of predictions. The aim of this model was to predict the temperature, concentration and milk level in the evaporator. The model obtained was solved using the Runge-Kutta method with the software “LABVIEW 2011” and it was quantitatively validated with experimental data from a real process using the absolute mean error. The experimental data of temperature, concentration and milk level in the evaporator were obtained applying step-like disturbances in the process variables: vacuum pressure in the evaporation chamber, steam flow and milk feeding flow. The quantitative validation showed that the obtained model can predict satisfactorily the dynamic behavior of the target variables of the milk evaporation process because the error was less than 5%.

Phenomenological-Based model of human stomach and its role in glucose metabolism

The stomach is a segment of the gastrointestinal (GI) tract which receives food from the esophagus, mixes it, breaks it down, and then passes it on to the small intestine in smaller portions. In the stomach, the main secretory functions and digestion process begin. However, the most critical and important function of the stomach in digestive physiology is perhaps gastric motility. In this way, the functions of the stomach are mainly three: (i) the storage of large quantities of food to be further processed in the duodenum, and lower intestinal tract, (ii) the mixing of this food with gastric secretions to form a semi-fluid mixture, and (iii) to slow down the emptying of that semi-fluid mixture into the small intestine at a rate suitable for proper digestion and absorption. Regarding the motor activity, the stomach must consume glucose to generate the power necessary to carry out the digestion process. Although glucose consumption in the stomach is relatively low, it can affect the glucose concentration in the bloodstream. In order to know the variations in the glucose levels in the bloodstream during the stomach digestion, a Phenomenological Based Semi-physical Model (PBSM) of the role of the stomach in the glucose homeostasis is developed. The simulation of the stomach model is able to mimic physiological results without risking the life of the patient, in order to test the impact of diverse medicines and foods on glucose homeostasis. The model may then be integrated to existing models of glucose homeostasis to improve the simulation scenario with respect to the glucose appearance from a mixed meal. Our model allows the change of the macronutrient composition and rheological properties of the meal as well as the digestion particularities of every subject. In this way, the integrated model will be fitted to real patient physiology providing a better model to use in, for example, automated insulin delivery systems like the artificial pancreas (AP).

A dynamical model of an aeration plant for wastewater treatment using a phenomenological based semi-physical modeling methodology

Diffused aeration is a sensitive process for wastewater treatment. Because of the nonlinearity and complexity of aerator dynamics due to microorganism metabolism and oxygen transfer, reliable mathematical models are needed to perform control-oriented tasks. To this end, in this study we develop a phenomenological based semi-physical model (PBSM) to predict and describe the dynamic behavior of the oxygen transfer in a diffused aeration process by means of a formal modeling methodology. This model will then be validated by using data from an aeration pilot plant. In this paper, we also show a lack of agreement in the literature in terms of the different available ways to represent the volumetric oxygen transfer coefficient kLa. Reasonable agreement between the developed model and plant data is found by considering a phenomenological approach of the kLa instead of considering many of the available empirical correlations in the literature.

Comparison of empirical correlations for the estimation of the oxygen transfer coefficient in an aerobic bioprocess

This paper shows a comparison of three different empirical correlations found in the literature for the estimation of the oxygen transfer coefficient in an aeration pilot plant. To this end, a phenomenological-based semi-physical model (PBSM) of the aeration pilot plant is used. This evaluation tested the relationship between empirical correlations and the oxygen transfer phenomenon from the gas phase to the liquid phase was assessed. The results show that empirical correlations of the oxygen transfer coefficient found in the literature are not based on the knowledge of the physical phenomena, and hence are not suitable to generalize the transference mechanism in other similar processes.

A phenomenologically based material flow model for friction stir welding

This paper presents a methodology for the construction of a phenomenological based semiphysical model (PBSM) applied to the soundness evaluation of friction stir welding (FSW) of aluminum alloy welded joints. The mentioned approach produces an interpretable and mathematically simple model but with enough prediction power. Different to other approaches, the obtained PBSM can be simulated in real time because of its low computational burden (<8ms per run with Core 2 INTEL® processor). Using AA2024-T3 aluminum for experimental validation and model simulation, the obtained model allows the evaluation of basic weld soundness with low computational cost and simple indicators that can be used by non-specialized users in process modeling. The achieved results show good agreement with the experimental data and indicate a clear upper boundary for the travel speed variable, from which it is possible to set a window of operation for the FSW process in the evaluated conditions for aluminum alloy AA2024-T3.

Scaleup of Batch Reactors Using Phenomenological-Based Models

This work presents a methodology for scaling up batch processes (BPs). First, the most popular scaleup methods differentiating batch from continuous processing are reviewed, revealing that traditional scaleup approaches do not consider BP characteristics and that many particular successful cases are reported, but no formal procedure has been developed for scaling up these processes. Considering these facts, a novel scaleup procedure is presented, in which a process phenomenological-based semiphysical model (PBSM) and its Hankel matrix are used for computing the state impactability index (SII) that allow the designer to determine (i) the main process dynamics at each stage of the batch and (ii) the critical point of the operating trajectory (OT) at which the batch must be scaled up. Finally, the methodology is applied to a batch suspension polymerization reactor, comparing the scaled unit design using this approximation and a traditional method.

Metodología para la Obtención de Modelos Semifísicos de Base Fenomenológica Aplicada a una Sulfitadora de Jugo de Caña de Azúcar

This work presents the Phenomenological Based Semi-physical Model (PBSM) as a useful tool for the design, control and optimization of chemical and bio-technology processes. First, a detailed description of a methodology to obtain a PBSM based on the knowledge of the basic principles of the process: mass, energy and momentum conservation, as well as gradient principle for obtaining model constitutive equations. The described methodology is applied to the clarification stage of a sugar cane refining plant. Specifically, the sulfitation tower is modelled step by step. The model is simulated and validated by comparing its behaviour with data taken from a real sulfitation tower.