Modeling and simulation of the drying of thin sheets in a continuous infrared dryer

The model gives the temperature and moisture distributions of the air, and of the moist sheet, as a function of time and distance in the dryer. The influence of the sheet's velocity and that of the radiant energy on the dryer performance as well as the effect of the moisture content of the entering sheet have been studied. A set of 27 experiments was carried out using the infrared dryer in order to calibrate the model. In these, the following three variables each had three operational levels: web velocity, initial web moisture and heating power. The model predictions agreed very well with the experimental data. Model predictions using arithmetic averages for the parameters, and parameters correlated with operational variables, are also presented and discussed.     

Model-based multivariable control of the drying of a thin sheet of fibres in a continuous infrared dryer

We developed an Internal Model Control (IMC) algorithm for drying a thin textile fabric, continuously passing through an electric infrared dryer, based on a reduced linear model of the drying dynamics. This model relates the controlled variables, the humidity and temperature of the web at the dryer outlet, to the manipulated variables, the electrical power supplied to the sources and the web speed through the dryer, and also to changes in the initial humidity of the web at the dryer inlet. The control algorithm was first tested by simulation using the model in regulation mode, and in set-point tracking mode, to vary the manipulated variables so as to maintain the controlled variables at their respective set-points when the inlet web temperature and humidity were changed. The performance under simulated operational conditions was compared to that of a conventional feedback proportional-integral (PI) controller coupled with a feedforward control. The IMC controller was then tested directly in regulation mode using a pilot scale infrared dryer, acting simultaneously on the manipulated variables, the emitter power and the web speed, to control the fabric temperature and humidity at the dryer outlet. The experimental results were compared with those from the above feedback-feedforward controller, on the pilot scale dryer. The results have indicated that the closed-loop stability of the process is assured simply by choosing a stable IMC controller. Also, such a controller does not require the design of specific compensators for the strong interactions between variables of the drying process.     

Modelling the continuous drying of a thin sheet of fibres on a cylinder heated by electric induction

The derived model predicts the evolution of the humidity and temperature of a thin web of fibrous material during drying on the surface of a metal cylinder heated by electric induction. The model explicitly considered heat conduction, convection and radiation, and thermal induction as well as energy transfer caused by the evaporation of the water. It also predicted the process responses to perturbations in the manipulated variables and in the initial humidity of the web entering a small‐scale induction dryer. The manipulated variables included the rotational speed of the cylinder, the electric power fed to the inductors, and the area of the web in contact with the cylinder. The simulations carried out showed a high degree of correspondence between the model predictions and the experimental data.     

Reduction of a single granule drying model: An essential step in preparation of a population balance model with a continuous growth term

The development of a Population Balance Model (PBM) for a pharmaceutical granule drying process requires a continuous growth term; the latter actually represents the drying process as the moisture content is the internal coordinate of the PBM. To establish such a PBM, a complex drying model for a single granule needs reduction in complexity. The starting point is a detailed model that describes the drying behavior of single pharmaceutical granules. A Global Sensitivity Analysis (GSA) was performed to detect the most sensitive degrees of freedom in the model as these need to be retained in the reduced model. Simulations of the complex drying model were, in a next phase, used to develop the reduced model, which describes the decrease of the moisture content in function of the gas temperature. The developed reduced model was then included in a Population Balance Equation (PBE) to describe the drying behavior of a population of granules. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1127–1138, 2013     

Reduction of a model for single droplet drying and application to CFD of skim milk spray drying

In this work, a novel methodology for the development of a high-accuracy computational fluid dynamics (CFD) model for the spray-drying process is described. Starting point is an own spatially resolving model of droplet/particle drying, which was developed and validated on the basis of a series of single droplet drying (SDD) experiments. This sophisticated model is transformed to a much simpler version: the characteristic drying curve approach, after running the full SDD model in a wide range of operating conditions. Then, the obtained reduced model is implemented into the CFD solver. The CFD spray-drying model takes into account the hydrodynamics of the continuous phase, particle drying kinetics, changes in the particle diameter, and the heat loss from the drying chamber to the environment. Validation of the entire procedure is provided by data obtained from drying experiments performed in a co-current laboratory spray tower. High accuracy of the developed CFD model of skim milk spray drying has been found for both phases, for the mean outlet temperature of the continuous phase (air) and for the change in average particle moisture content along the spray tower (discrete phase).     

A study on drying models and internal stresses of the rice kernel during infrared drying

Due to the limited penetration of infrared, it is very difficult to develop an infrared drying model of rice kernels. In this study, two kinds of simplified drying models, which assumed the penetration depth is infinity and zero, were developed to investigate the effects of penetration on drying characteristics of thin layer infrared drying. The results show each model can predict temperature and moisture contents (MC) accurately. The maximum temperature difference of rice kernels in both models was always less than 1.5°C, so it is reasonable to exclude the influence of thermal stresses due to nonuniform temperature. This study also developed the internal stresses model with the mechanical properties from literatures. Mechanical properties were with the changes of temperature and MC. These models were solved with COMSOL Multiphysics and there are two stress concentration areas. One is near the surface of the endosperm, another close to the center. Comparison between the von Mises stress distribution and the moisture gradient was made after the simulation. The maximum MC in the endosperm appeared at its surface, which reached 190 1/m at 110?s, and the maximum stress appeared at the same place, which, a little later, reached 7?MPa at 160?s. Moisture gradient at the center was zero due to the existence of symmetry, while there was a significant stress, which reached 3.2?MPa during drying.     

An experimental study for property control in a continuous styrene polymerization reactor using a polynomial ARMA model

By using a multivariable nonlinear model predictive controller (NLMPC), the control experiments for the monomer conversion and the weight-average molecular weight are conducted in a continuous styrene polymerization reactor. Instead of a complex first-principles model, a polynomial auto-regressive moving average model (ARMA) is used to describe the nonlinear behavior of the polymerization reactor. The pseudorandom multilevel input signals mounted on the jacket inlet temperature and the feed flow rate are applied to the polymerization reaction system to identify a polynomial ARMA model. In the experiments of identification and control, the monomer conversion and the weight-average molecular weight are measured by on-line densitometer and viscometer with appropriate correlations. The on-line measurements are found to be in good agreement with the off-line analysis by the gravimetry and the gel permeation chromatography. Since a polynomial ARMA model is expected to give a higher order objective function of input variables, we employ the extended Kalman filter based NLMPC scheme to reduce the computational requirement in the control experiments. The NLMPC based on the polynomial ARMA model is found to perform satisfactorily for the control of the polymer properties during a grade-transition period as well as under the steady-state operation.     

Reduced‐order modeling for the control of selective noncatalytic reduction of nitrogen monoxide

The design of a reduced‐order model is discussed to help in the control of selective noncatalytic reduction (SNCR) of nitrogen monoxide. Instead of relying on a look‐up table of nominal operating points, it is proposed to solve for the time evolution of a set of stochastic particles interacting through a model for turbulent mixing and a reduced chemistry. Each particle is representative of a fraction of the mass flowing in the system in gaseous or liquid form. To calibrate and validate the reduced‐order model, which runs in a few minutes on a desktop computer, reference three‐dimensional and unsteady large‐eddy simulation is performed in the complex geometry of a real incinerator. This is done solving the full set of conservation equations of mass, momentum, and energy over a 162 million cells mesh. The results confirm the possibility of real‐time SNCR optimization from the solving of partial differential equations. © 2015 American Institute of Chemical Engineers AIChE J, 62: 928–938, 2016     

Obtaining model parameters of drying kinetics for highly shrinkable materials without knowing the surface area a priori

Shrinkage parameters of highly shrinkable materials such as length, diameter and surface area during drying are difficult to quantify in situ. However, these are significant components of an accurate model. In this study, an attempt to isolate the surface area effect is reported in order to fetch the REA model (reaction engineering approach) parameters without knowing it a priori. Carrot cube and cabbage leaf were selected as experimental material and dried with hot air under a range of conditions. Shrinkages was calculated using an optical method which is used to qualitatively compare with that “calculated” using the current approach. By matching the experimental temperature and moisture content profiles against time after obtaining REA parameters for both samples without knowing the surface area, the surface areas can be “calculated” numerically. Surface area was found to be affected by sample temperature as well as the moisture content. Drying simulations can be well carried out when correlating the surface area against sample moisture content X and temperature T, and it provides the best accuracy in predicting data on T and X vs. time. In addition, carrot cube can shrink ideally while cabbage leaf cannot. The overall relative errors of predicted moisture content and temperature were less than 1%.     

Molecular dynamics study of a macromolecular model of hydrous sodium aluminosilicate geopolymer with charge balancing by extra-structural aluminum for prediction of its properties

Geopolymer is a material with unique properties and has various uses. This substance is mainly amorphous, and its qualitative characteristics are related to its binder phase that is called the hydrous sodium aluminosilicate geopolymer. The molecular structural model of this geopolymer includes ${\text{Q}}^4\left(4\text{Al}\right),{\text{Q}}^4\left(3\text{Al}\right),{\text{Q}}^4\left(2\text{Al}\right)$, and ${\text{Q}}^4\left(1\text{Al}\right)\text{Si}$ units, which have been balanced in terms of electric charge by extra-framework $\text{Al}$ and ${\text{Na}}^{+}$ ions. In this study, we calculated the density, Young's modulus, and RDF curve of the geopolymer from the molecular dynamics simulation. The results of the simulation were in good agreement with the results of the laboratory obtained from several studies.     

A Three-Dimensional Numerical Study of the Gas/Particle Interactions in an Industrial-Scale Spray Dryer for Milk Powder Production

Gas/particle interaction plays an important role in modern spray dryers and may have influences on wall deposition, agglomeration, powder degradation, etc. In the present study, the three-dimensional (3-D) transient multiphase flow in an industrial-scale spray dryer has been investigated using the CFD package FLUENT. The Eulerian–Lagrangian approach and two-way coupling method were used in the simulations. The reaction engineering approach (REA model) for milk particles has been implemented. Some new characteristics of the gas flow pattern and the particle behavior (e.g., temperature–time profiles) were identified from the numerical results; for example, the milk particles flow in such a way that makes the central jet oscillation more nonlinear. The discrete phase enhances the turbulence near the air/droplet inlet but damps it downstream. The transient turbulent flow causes significant uncertainties in the particle tracking, which presented some challenges in simulations. The study has highlighted the importance in performing 3-D transient simulations in order to understand the industrial-scale dryers.     

A hydrodynamic model of a continuous supercritical fluid extraction system for the treatment of oil contaminated solids

Supercritical fluid extraction (SFE) has the potential to recover compounds from a range of solid matrices if a fully continuous process can be commercialized. This paper presents the development of a hydrodynamic model for a continuous pilot scale SFE process, involving countercurrent flow of a slurry and a supercritical fluid. The model developed is based on first principles and focuses on predicting pressure and slurry level within the extraction vessel. The model was validated using pilot scale system data. Using adjusted parameters, the model accurately predicted steady state pressure and provided a good estimate of slurry level.     

Two phase mathematical model for a bio-trickling reactor for the production of ultra low sulfur diesel (ULSD) from deeply hydrodesulfurized diesel

A trickle bed reactor (TBR) having a diameter of 0.066 m and a height of 0.6 m has been used for the bio-desulfurization of hydrotreated diesel fraction having sulfur concentration in the range of 200–540 ppm. Rhodococcus sp. (NCIM 2891, Pune) has been used to degrade the residual organo-sulfur compounds present in deeply hydrodesulfurized diesel. The microorganisms have been immobilized on the packing material prior to desulfurization within the trickle bed reactor. The volumetric flow rate and hence, the substrate loading rate have been used as the parameters. Sulfur reduction within the range of 84–95% has been achieved. To avoid the excess accumulation of the biomass within the reactor, backwashing technique is incorporated. For such desulfurization, batch studies have been conducted in Erlenmeyer flasks maintaining the concentration of diesel in the range of 0–100% in a diesel supplemented sulfur-free aqueous medium. The concentration of biomass with time has been monitored using dry cell weight method. The concentration of sulfur has been determined by “trace sulfur in petroleum distillate by nickel reduction” (UOP 357-80) method. From the growth curve, it is observed that the system follows uninhibited Monod type model within the range of substrate studied. A systematic and programmed investigation has been carried out to determine the growth kinetic parameters, namely maximum specific growth rate, saturation constant K_s and yield coefficient Y_X/S. A deterministic mathematical model for the TBR has been developed using judicious assumptions to predict its performance characteristics.     

Estimation of the interaction energy between small molecules and a silica model as an approach for predicting the interaction order between elastomers and silica

BACKGROUND: Many elastomers are reinforced with fillers to improve their mechanical properties; good reinforcement requires favorable interactions between the elastomeric chains and the surfaces of the filler particles. A useful goal is the development of computational methods that estimate these interactions, and thereby guide choices of fillers for elastomers based on the structures of the two components in nanocomposites in general. RESULTS: Experimental results available from inverse gas chromatography rank nitriles, aromatics and 1‐alkenes with regard to the magnitude of favorable interactions with silica reinforcing particles. Calculations using the Gaussian 03 package of computer programs were carried out, both with and without corrections for superposition errors. For the nitrile compounds, the formation of hydrogen bonds was predicted, and the interactions of the aromatic and 1‐alkene compounds with silica were shown to be dependent on electron transfer from the silica to the tested molecules. CONCLUSION: The method developed should be useful for ranking polymer–filler combinations in general with regard to the interactions known to be conducive to good reinforcement. Copyright © 2009 Society of Chemical Industry     

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.     

Quick identification of a simple enzyme deactivation model for an extended-Michaelis-Menten reaction type. Exemplification for the D-glucose oxidation with a complex enzyme deactivation kinetics

One essential engineering problem when developing an industrial enzymatic process concerns the model-based design and optimal operation of the enzymatic reactor based on the process and enzyme inactivation kinetics. For a complex enzymatic system, the “default” used first-order enzyme deactivation model has been proved to lead to inadequate process design or sub-optimal operating policies. The present study investigates if a complex enzyme deactivation can be approximated with simple 1st, 2nd, or a novel proposed model with variable deactivation constant. The approached complex enzymatic system is those of the oxidation of D-glucose to 2-keto-D-glucose in the presence of pyranose 2-oxidase. The necessary “simulated experimental data” have been generated by means of an extended kinetic model from literature used to simulate a batch reactor under well-defined nominal conditions. The proposed enzyme deactivation model has been found to be the best lumping alternative, presenting several advantages: simplicity, flexibility, and a very good adequacy.