Performing Department
(N/A)
Non Technical Summary
Thermal processing operations including drying, cooking, and pasteurization rely on heat (mostly obtained by burning fossil fuels) and fluid motion to raise the temperature and reduce the moisture content of food. These processing operations are widely employed for food preservation and sterilization, directly affecting the food supply chain. Thermal processing operations are also infamously known for their low energy efficiency (defined as the minimum theoretical energy required divided by the actual energy consumed) ranging from 10% to 60% in case of drying. A 10% improvement in efficiency for process heating in the food and beverage industry would reduce carbon emission by 1.1 million tons, equivalent to 2.7 billion miles driven by a gasoline-powered car annually. It has been shown in earlier studies that both yield and energy efficiency can be substantially improved via control and optimization of processing conditions. For example, the throughput or production rate will be set as high as possible while balancing the need to reduce energy loss (e.g., by lowering air temperature) and satisfying food quality and safety constraints. This project will take the next step toward the optimal control of thermal food processing -development, validation, and implementation of the approach to maintain optimality in daily operations under the inevitable influences from strong process variability.A thermal food processing operation is subject to many variations. First, there continues to be a need for scale-up, equipment changes, and food-stock switchover. Although these discrete variations are directly measurable and quantifiable, any of such changes renders existing control parameters including set processing conditions completely irrelevant. Abrand-new model for process control has to be built from the ground up requiring extensive experiments and causing a serious disruption to regular production.Therefore, amodular and physics-based modeling approachrequiring minimal production disruptions is highly desirable.Second, there are the unmeasurable disturbances and variations, such as changes in moisture contents of incoming food stocks, unmonitored ambient conditions, and aging of equipment. Unlike in the case of the discrete and measurable variations, minor tuning of the control parameters would suffice to maintain the optimal operation. This would be a straightforward task if direct feedback of the product state were available. However, it is almost impossible to acquire real-time values of the moisture content and other qualities in food, because installation of a physical sensor in each individual productunder the harsh processing environment is impractical. Therefore, anew on-line approach based on integration of a high-fidelity process model with indirect feedbackis required to accurately estimate the product state.The research objective of this project is to build and evaluate a novel cyber-physical system (CPS) for real-time management of the process variability from both measurable and unmeasurable sources in optimal control of thermal food processing.The first-principle models,representing the complex spatio-temporal dynamics of thermal food processing,can potentially help achieving such a goal. The model parameters of first-principle models, such as the thermal conductivity of food, have physical meanings. Therefore, the food model, once developed in a lab setting without causing any production disruptions, will remain valid regardless of changes in processing scale and equipment.This opens the way to modular structure built on separate models of food and equipment, so a cumulative model can be deployed rapidly via plug-and-play coupling of pre-built components. Moreover, high-fidelity simulation of thermal food processing can be accomplished via Computational Fluid Dynamics (CFD) analysis. However, the high computational cost of CFD models renders their use for any time-critical applications intractable. A key task and the major novelty of the proposed work will be overcoming this obstacle via employment of aReduced-Order Modeling (ROM)approach, in which a high-order CFD model is replaced with a lower-order model, thereby drastically lowering the computational cost with little loss in accuracy. Taking advantage of the computational efficiency of the developed ROM,estimation of unmeasurable variationsand on-line optimization of processing conditions will be implemented. The proposed CPS for smart food processing will be implemented and validated usingthe pilot-scale equipment atthe Michigan State University (MSU)for continuous hot-air drying of food as a test case. The developed procedures and algorithm from this project are expected to be broadly applicable to a wide range of thermal processing operations for food and agricultural products. Improved yield and energy efficiency of thermal food processingwill contribute to societal benefit through innovative and sustainable food production.
Animal Health Component
0%
Research Effort Categories
Basic
60%
Applied
25%
Developmental
15%
Goals / Objectives
Goal:Thermal food processing plays a vital role in coping with limited food supply while being a main contributor to the food and beverage industry's current status as the fifth highest CO2-emitting industry. Although improving yield and energy efficiency based on control will be highly desirable, this effort is hampered by the large and unavoidable process variability from various sources. The goal of this project is not only to achieve but to maintain the optimal operation of thermal food processing by developing and integrating new cyber-physical system (CPS) technologies for handling the process variability. The main idea of the proposed CPS is to 1) directly account for the measurable variations, such as food-stock switchovers, via rapid assembly of a control-friendly model based on reduced-order model (ROM) components and 2) estimate the unmeasurable ones by integrating other process feedback with the assembled high-fidelity model.Objectives:1. Experiment with a novel model order reduction technique that can automatically derive a ROM from a computational fluid dynamics (CFD) model, thereby decreasing the computational cost with little loss in accuracy.2. Evaluate separate development of food and equipment models and their assemblyfor rapid deployment of process models.3. Design and test an on-line observer algorithm to estimate unmeasurable variables such as the moisture contents of food based on measured variables, including air temperature and humidity.4. Implement amodel-predictive optimal control scheme to determine the processing conditions that will maximize the energy efficiency and throughput while satisfying product quality and safety requirements.5. Experimentally validatethe proposed CPS by usinga pilot-scale, hot-air drying machine with food items such as apple slices.
Project Methods
The project team will carry out four major tasks as listed below:Task 1:Reduced-order modeling of hot-air dryingEquipment and food ROMs will be constructed by establishing a CFD model as baseline and by implementing the proposed model-order reduction procedure.Task 2: Initial food modelThe primary food model will be developed and validated with pilot-scale drying equipment.Task 3: Enhanced food model and validation experimentsThe initial model will be further calibrated with additional quality or safety data, variables, and more accurate parameters for enhanced prediction of the overall goals.Task 4: Soft-sensing and on-line controlOn-line estimation and control of product quality will be achieved.The University of Michigan-Dearborn (UMD) will take the lead in Tasks 1 and 4 while Michigan State University (MSU) will be responsible for Tasks 2 and 3. A detailed description and milestones of each task are given below.Task 1:Reduced-Order Modeling (M1-M24)Task Summary:ACFD model of continuous hot-air drying involving both equipment and food will be built using nominal food model parameters. The CFD model will be used as a baseline model, and supply necessary information for construction of the ROM. The CFD model will be validated by drying experiments (Milestone 2). Equipment ROM with limited input will be built by implementing the Krylov subspace method based on 'alter-and-excite' scheme. Food ROM will be built based on the spectral method. The two ROMs will be combined to simulate hot-air drying of a stationary food product. In the second phase of this task, the equipment ROM will be modified to take multiple food locations and the coupling for moving products will be implemented. Optimal placement of interpolation points in the complex plane will be implemented to improve the accuracy of ROM. Validation experiments will be conducted with the actual drying process with two different types of food products to demonstrate the capability of plug-and-play assembly of ROMs.Milestone 1.1 (M12):Demonstrate an initial ROM having less than 1% error from the CFD model.Milestone 1.2 (M24):Demonstrate the capability of plug-and-play ROM assembly using two types of food products and achieve less than 10% overall root mean square error (RMSE) with both types after validation experiments.Task 2:InitialFood Model (M1-M12)Task Summary:Disc-shaped food products, such as apple and potato slices, will be modeled using primary heat and mass transfer PDEs with first order kinetic models for microbial safety and quality retainment. Off-line parameter estimation will be done by a) Plotting scaled sensitivity coefficients to determine parameter identifiability; b) Fixing the value of parameters to which the model is not sensitive; and c) Estimating the remaining parameters using sequential estimation, which updates the parameters as data are added and the experiment proceeds. The primary food model will be inserted into the CFD model of Task 1 and validated by drying experiments for Milestone 2.Milestone 2 (M12):Estimate drying/safety/quality parameters to achieve the accuracy of the model within expected biological variability of the food material and model bias, which may range up to 20% RMSE.Task 3:Enhanced Food Model and Validation Experiments (M13-M36)Task Summary:The initial model will be further calibrated with additional quality or safety data, variables, and more accurate parameters for enhanced prediction of the overall goals. To improve the models, parameter values from initial experiments will be used to create a more efficient set of the next experiments using optimal experimental design. Residual analysis will be used to determine whether the models need additional terms or modifications. The final target accuracy to achieve will be approximately 10% error in terms of RMSE. We will enhance the food model (hot-air drying) for possible improvement of overall performance by introducing humidity control in the system. For this, a previous MSU modified moist hot-air model will be incorporated into the developed primary model to increase drying efficiency and microbial inactivation efficacy by 10% overall. The MSU team will participate in the experiments for validation of soft-sensing and on-line control (Milestones 4.1 and 4.2).Milestone 3 (M24):Demonstrate the accuracy of enhanced food model reaches less than 10% overall RMSE after validation experiments, by reducing model bias.Task 4:Soft-Sensing and On-Line Control (M13-M36)Task Summary:Robust on-line estimation of product quality and important process variables will be achieved by implementing a multi-rate identification scheme and by carrying out the identifiability study based on Fisher information theory. The proposed on-line control framework will be developed by setting up the optimization scheme first and then by implementing adaptation framework using feedback. Both soft-sensing and on-line control methods will be tested and tuned using the CFD model of hot-air drying. Validation experiments will then be conducted with the actual drying process by applying unmeasurable disturbances on purpose (e.g., a sudden change in moisture contents of incoming food).Milestone 4.1 (M30):Demonstrate achieving less than 5% RMSE in soft-sensing of product qualities with the actual drying process by accounting for the unmeasurable food variations and demonstrate maintaining the accuracy when disturbances are applied.Milestone 4.2 (M36):Demonstrate maintaining the optimal performance in terms of energy efficiency and yield when disturbances are applied to the actual drying process.