STATE ESTIMATION IN THE BATCH DRYING OF FOODS

ABSTRACT

Measurement of material moisture content is necessary for the control of product quality in batch drying. However, this variable cannot be measured on-line, and state estimation techniques are proposed. A non-linear dynamic model is developed for batch drying of foods. Process disturbances and measurement errors are modeled as stochastic processes and a hybrid extended Kalman filter is employed for state estimation. This filter is based on the local linearization of the process model around the suboptimal filter estimates. The moisture estimation approach was applied to experimental points obtained in a laboratory dryer with quite satisfactory results.     

Drying Lumber In Kilns: A Methodology for Stochastic Analysis Using Markov Modelling

The value of dried lumber to the kiln operator is to a large extent dependent on the final moisture content distribution of the batch. This variation in moisture content arises from the intrinsic heterogeneity of wood and the stochastic nature of the timber drying process. Theoretical analyses of wood drying have hitherto relied upon deterministic models and have limited practical applicability. A methodology is proposed herein whereby the inherent probabilistic dynamics of batch drying can be modelled efliciently using Markov chains. The discretization of the variables of moisture and time that is necessary for this approach is discussed. By simulating the drying process with the model, the progressive transformation of the batch moisture distribution throughout the drying cycle can be studied. Output from the model is compared with experimental drying results to test its validity. There are     

Prediction of Moisture Content Evolution in a Grain Batch Dryer Based on On–line Temperature Measurements

ABSTRACT

The time evolution of the moisture content in a grain batch fluidized bed dryer is estimated by means of an on–line non–linear estimator (Marquardt–Levenberg algorithm). The inputs to the estimation algorithm are on–line temperature measurements and the output is the surface moisture content. The surface moisture content is used for predicting the grain moisture content through the solution of a single ordinary differential equation that combines the moisture and energy balances over the dryer. In this way the drying curves are obtained through incorporating in a very simple model easily obtainable physical information of the process.     

INFLUENCE OF MODEL UNCERTAINTY ON THE DYNAMIC OPTIMAL CONTROL PERFORMANCE OF A BATCH CORN DRYING PROCESS

A semi-empirical nonlinear model of moisture content and wet-milling quality degradation in com drying is established. The model uncertainty is expressed as a relative error level which includes a pre-specified percentage of experimental data. An optimal control strategy for batch drying is determined which maximizes the dryer throughput, while simultaneously satisfying two quality constraints on the final state: achieving specified moisture content and wet-milling quality levels. Taking into account the model uncertainty results in a conservative but less efficient control strategy. The performance - robustness compromise is discussed.     

Computer-Vision System for Control of Drying Processes

Computer-vision system (CVS) for control of a drying process with a portable CCD camera with IEEE-1396 interface and configurable software LabView 7.0 and IMAQ^TM 6.1 was developed. An object area was continuously monitored through the CVS by extracting the green plane from the RGB color space followed by thresholding and pixel counting. An object color was continuously monitored through the CVS as color intensity in the hue-saturation-intensity (HSI) color space. The observability of a drying process was provided due to online image analysis and correlation of image attributes (area, color, texture) with physical parameters of drying (moisture, quality). A relationship between area shrinkage and moisture content was used for online estimation of actual moisture content. A relationship between color intensity and quality was used for online estimation of quality degradation.

Experimental study of the CVS for ginseng drying showed advantages of computer-vision for online monitoring of important state variables, such as moisture content and material quality. Color measurements demonstrated high sensitivity of quality to drying conditions: drying at 50°C resulted in significant color changes and unacceptable quality degradation. The quality of roots in three-stage (38-50-38°C) drying process was compatible with recommended isothermal (38°C) drying due to significant (30-40%) reduction of drying time. This control strategy was used in a pilot batch dryer for temperature control with respect to quality. Testing of a pilot dryer with embedded CVS proved stability and robustness of control strategy, combined with high accuracy in the estimation of moisture content (8-14% of error with 95% confidence). The composite moisture measurements at the endpoint demonstrated uniform drying of root mixture to target moisture content 0.1 g/g (db) with minor variations between individual roots in the range of 0.07-0.12 g/g.     

Computer-Vision System for Control of Drying Processes

Computer-vision system (CVS) for control of a drying process with a portable CCD camera with IEEE-1396 interface and configurable software LabView 7.0 and IMAQ^TM 6.1 was developed. An object area was continuously monitored through the CVS by extracting the green plane from the RGB color space followed by thresholding and pixel counting. An object color was continuously monitored through the CVS as color intensity in the hue-saturation-intensity (HSI) color space. The observability of a drying process was provided due to online image analysis and correlation of image attributes (area, color, texture) with physical parameters of drying (moisture, quality). A relationship between area shrinkage and moisture content was used for online estimation of actual moisture content. A relationship between color intensity and quality was used for online estimation of quality degradation.

Experimental study of the CVS for ginseng drying showed advantages of computer-vision for online monitoring of important state variables, such as moisture content and material quality. Color measurements demonstrated high sensitivity of quality to drying conditions: drying at 50°C resulted in significant color changes and unacceptable quality degradation. The quality of roots in three-stage (38-50-38°C) drying process was compatible with recommended isothermal (38°C) drying due to significant (30–40%) reduction of drying time. This control strategy was used in a pilot batch dryer for temperature control with respect to quality. Testing of a pilot dryer with embedded CVS proved stability and robustness of control strategy, combined with high accuracy in the estimation of moisture content (8–14% of error with 95% confidence). The composite moisture measurements at the endpoint demonstrated uniform drying of root mixture to target moisture content 0.1 g/g (db) with minor variations between individual roots in the range of 0.07–0.12 g/g.     

Composition estimation of multicomponent reactive batch distillation with optimal sensor configuration

An inferential state estimation scheme based on extended Kalman filter (EKF) with optimal selection of sensor locations using principal component analysis (PCA) is presented for composition estimation in multicomponent reactive batch distillation. The properties of PCA are exploited to provide the most sensitive dynamic temperature measurement information of the process to the estimator for accurate estimation of compositions. The state estimator is supported by a simplified dynamic model of reactive batch distillation that includes component balance equations together with thermodynamic relations and reaction kinetics. The performance of the proposed scheme is evaluated by applying it for composition estimation on all trays, reboiler, reflux drum and products of a reactive batch distillation column, in which ethyl acetate is produced through an esterification reaction between acetic acid and ethanol. This quaternary system with azeotropism is highly nonlinear and typically suited for implementation of the proposed scheme. The results demonstrate that the proposed EKF estimation scheme with optimal temperature sensor configuration is effective for inferential estimation of compositions in multicomponent reactive batch distillation.     

基于自适应神经模糊推理系统的间歇反应精馏组分估算(英文)

Composition estimation plays very important role in plant operation and control. Extended Kalman filter (EKF) is one of the most common estimators, which has been used in composition estimation of reactive batch distillation, but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium, which is difficult to initialize and tune. In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system (ANFIS), which is a model base estimator, is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation. The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics. The mathematical model is verified by pilot plant data. The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation. The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.     

A Simulation Tool for the Optimization of Lumber Drying Schedules

A two-dimensional wood drying model based on the water potential concept is used to simulate the convection batch drying of lumber at conventional temperature. The model computes the average drying curve, the internal temperature and moisture content profiles, and the maximum effective moisture content gradient through board thickness. Various scenarios of conventional kiln-drying schedules are tested and their effects on drying time, maximum effective moisture content gradient, final moisture content distribution within and between boards, and energy consumption are analyzed. Simulations are performed for two softwood species, black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). The simulation results indicate that the predictive model can be a very useful tool to optimize kiln schedules in terms of drying time, energy consumption, and wood quality. Such a model could be readily combined with intelligent adaptive kiln controllers for on-line optimization of the drying schedules.     

Modelling and Simulation of Batch and Continuous Fluidized Bed Dryers

The performance of batch and continuous fluidized solids dryers has been modeled, with allowance for diffusional moisture transport in the dense phase particles and for interstitial gas-to-particle mass transfer within the dense phase, as well as for interphase exchange resistance between gas bubbles and the dense phase. Two types of boundary conditions are employed. Variations of the bed temperature and product moisture content in the bed with time are predicted numerically under various batch drying conditions. Exit product moisture contents, bed temperatures and outlet air humidities are also predicted for continuous drying at various mean residence times. The model can be used for homogeneous as well as bubbling fluidized bed drying. It can be used for a wide range of materials, including cereal grains and granular synthetic polymeric materials.     

Bayesian method for state estimation of batch process with missing data

Formulated under the state space framework, most previous methods for the state estimation typically treated batch processes in the same way as continuous ones, and only considered the state transition within a single batch. Considering that the initial state of the current batch is often related to that of the previous one, this paper incorporates the information of the previous batches into the estimation of the current state, where the filtering and smoothing for the previous batch is implemented and then the initial state of the current batch is estimated by treating the smoothed initial state of the previous batch as a “measurement”. To deal with the nonlinear and non-Gaussian property of batch processes, the particle filter method is employed as the key algorithm for filtering and smoothing. In addition, in order to make full use of various measurements, the case of missing data is considered during the implementation of the particle filter algorithm. The proposed method is illustrated and evaluated through the simulation on a penicillin fed-batch fermentation process.     

Continuous fluidized bed drying: Residence time distribution characterization and effluent moisture content prediction

The mixing and drying behavior in a continuous fluidized bed dryer were investigated experimentally by characterizing the residence time distribution (RTD) and incorporating a micromixing model together with the drying kinetics obtained from batch drying. The RTD of the dryer was modeled using a tank-in-series model. It was found that a high initial material loading and a low material flow rate resulted in a reduced peak height and broaded peak width of the RTD curve. To predict the continuous dryer effluent moisture content, we combined: (a) the drying kinetics as determined in a batch fluidized bed dryer, (b) the RTD model, and (c) micromixing models—segregation and maximum mixedness models. It was found that the segregation model overpredicted the effluent moisture content by up to 5% for the cases we have studied while the maximum mixedness model gave a good prediction of the effluent moisture content.     

A Simulation Tool for the Optimization of Lumber Drying Schedules

Abstract

A two-dimensional wood drying model based on the water potential concept is used to simulate the convection batch drying of lumber at conventional temperature. The model computes the average drying curve, the internal temperature and moisture content profiles, and the maximum effective moisture content gradient through board thickness. Various scenarios of conventional kiln-drying schedules are tested and their effects on drying time, maximum effective moisture content gradient, final moisture content distribution within and between boards, and energy consumption are analyzed. Simulations are performed for two softwood species, black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). The simulation results indicate that the predictive model can be a very useful tool to optimize kiln schedules in terms of drying time, energy consumption, and wood quality. Such a model could be readily combined with intelligent adaptive kiln controllers for on-line optimization of the drying schedules.     

PROBABILISTIC ANALYSIS AND DESIGN OF THE INDUSTRIAL TIMBER DRYING PROCESS

The distribution in the moisture content of dried planks is an important parameter for kiln operators. The evolution of variability in the moisture content of timber boards during a batch drying process is investigated. This random variability in moisture content arises from a distribution in plank initial moisture content and dispersion in plank drying rate. A simple deterministic model of timber drying is outlined. Theoretical probabilistic analysis is applied to this model to predict the mean and standard deviation in board moisture content as a function of time. The solution is assessed with representative industrial kiln drying data. The utility of the approach in suggesting strategies to promote uniformity in final moisture content is outlined using some design studies. These strategies include sorting of the timber by moisture content and adjusting the drying rate and equilibrium moisture content to reduce variability. It is also demonstrated that the probabilistic approach can yield a better estimation of kiln average moisture content.     

Mathematical Modeling of the Fluidized Bed Drying of Cubic Materials

The unsteady‐state simultaneous heat and mass transfer between gas and potato cubes during the drying process in a batch fluidized bed was described by a mathematical model. Mass transfer was considered to occur in three dimensions whereas heat transfer between the gas and dried material was assumed to be lumped. It was found that the model could describe the drying process with acceptable accuracy. The moisture profile inside the material at any cross‐section and at any time can be predicted by the model.     

Heat and Moisture Transfer Model for Onion Drying

Onion slices of 3 mm thick were dried in an atmospheric batch tray dryer in order to investigate the influence of air temperature and drying time on parameters such as sample moisture content and drying rate. A model is proposed which takes into account both moisture and temperature distributions in the sample and is in a fair agreement with the experimental data. The models suggested so far by other workers take only the moisture distributions into account for onion drying.     

STRATEGIES FOR DRYING TOMATOES IN A TUNNEL DEHYDRATOR

A tunnel dehydrator can be operated using batch or semi-batch modes and while a batch mode is the simplest, it has the inherent characteristic of producing non-uniform moisture content. Although semi-batch mode does not have this problem, because all product experiences the same drying conditions, it is not used when drying tomatoes in Australia. The operation of most dehydrators is based on operator experience and rules of thumb, methods that are not likely to give optimum performance. A model that is suitable for simulating the operation of a tunnel dehydrator with any fruit has been developed. This model has been applied to the development of strategies for drying tomatoes with the objective of achieving optimum throughput, colour and drying efficiency. The results of these simulations are presented in this paper.     

A Mathematical Model for Constant and Intermittent Batch Drying of Grains in a Novel Rotating Jet Spouted bed

A diffusion-bared mathdcal model is prswmed for batch drying of corn in a novel rotating jn spouted bcd device under constant as well as intermitted drying conditions. Such a device is suited for drying of large partides (e.g. grains, beam, seed, etc) for which internal heat and mass tranfer rates control the drying kinetics. Based on literature data for moisture diffusivities the model predictions are compared with experimental data for both continous and time-dependend air supply and/or heat input. Effects of dcvant parameter are evaluated and discussed in the light of potential pnrctieal applications.     

SIMULATION OF DRYING IN A BATCH LUMBER KILN FROM SINGLE-BOARD TESTS

A deterministic model was developed to perform a board-by-board simulation of a forced convective batch lumber kiln. Individual board properties may be input and dryer operating parameters varied. The drying rates are empirical correlations based on single-board laboratory tests. The model incorporates the thermodynamic properties of the wood and gas, as well as mass and energy balances within the lumber stack. It also accounts for differences in heat and mass transfer resulting from position and changing gas properties throughout the dryer. The rate of drying predicted by the model and the final moisture content distribution were verified by weighing boards in a batch kiln before, during, and after drying. The application of the model is illustrated by simulating four common scenarios.