[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128425-en":3,"doc-seo-128425-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128425,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine-Learning-Enabled Optimization and Online Monitoring for Efficient and High-Quality Smart Drying - Dissertation","Drying is a key food-industry operation affecting both production efficiency and product preservation, yet industrial control is difficult due to interacting parameters, conflicting objectives, and uncertain sample characteristics. The dissertation develops machine-learning-based process control tools enabling smart drying that improves energy efficiency and food quality. It introduces uncertainty-aware response-surface optimization for apple drying, multi-modal fusion models for accurate moisture prediction, and online readiness forecasting using video and process data for cookie drying. It further proposes data-driven zero-shot surface color trajectory prediction for drying quality assessment.","© 2025 Shichen Li  \nMACHINE-LEARNING-ENABLED OPTIMIZATION AND ONLINE MONITORING FOR EFFICIENT AND HIGH-QUALITY SMART DRYING  \nBY  \nSHICHEN LI  \nDISSERTATION  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering in the Graduate College of the  \nUniversity of Illinois Urbana-Champaign, 2025  \nUrbana, Illinois  \nDoctoral Committee:  \nAssociate Professor Chenhui Shao, Chair  \nProfessor Placid M. Ferreira  \nProfessor Srinivasa M Salapaka  \nProfessor Pingfeng Wang  \nAbstract  \nDrying is an important process in the food industry that plays a critical role in both food production and preservation. Industrial scale drying processes and systems involve multiple interacting process parameters, conflicting production objectives, and highly uncertain sample characteristics, which make process control extremely challenging. Current industrial practice lacks the necessary decision-making tools to simultaneously achieve high energy efficiency and food quality. To address these challenges, this dissertation develops a suite of machine-learning-based process control tools to enable smart drying with improved process efficiency and product quality. The contributions of this dissertation are summarized as follows.  \nIt is important to devise a drying strategy to optimize drying efficiency, energy consumption, and product quality, especially under intricate input-output relationships with process uncertainties. Chapter 2 developsan uncertainty-aware, machine-learning-based response surface methodology for apple drying. New drying experiments are designed to resemble industrial practice with variable slice thickness. Variable-response relationships are modeled using machine learning models; Monte Carlo simulations are applied to quantify process uncertainties; and a constrained optimization approach identifies feasible design spaces and optimal parameter combinations. The proposed method achieves a 17 .9% energy savings and a 19 .0% reduction in drying time.  \nPhysical phenomena in drying can be measured by heterogeneous data modalities, with each carrying unique and complementary information. Effectively leveraging multi-modal data is essential for improving the performance of predictive modeling but remains challenging. Chapter 3 develops a multi-modal data fusion framework for accurately predicting final moisture content in apple drying. Tabular data and high-dimensional images are integrated through an encoder-decoder network to capture both process conditions and sample variability. Experimental results demonstrate predictive accuracy improvements of 19 .3%, 24 .2%, and 15 .2% compared to tabular-only, image-only, and standard data fusion models, respectively. It is also shown that the proposed method is robust to varying modality ratios and can effectively capture process variabilities.  \nAccurate real-time forecasting of the drying readiness (the optimal drying endpoint) is crucial for minimizing energy consumption and ensuring product quality. Chapter 4 presents a multi-modal fusion framework for online cookie drying readiness prediction. The model integrates in-situ video data and tabular process parameters using modality-specific encoders and a transformer-based decoder. The proposed model achieves a 15-second average prediction error, outperforming the state-of-the-art method by 65.7%, while balancing accuracy, model size, and efficiency. The framework is extensible to various other modality fusion tasks for effective online monitoring.  \nDynamic changes in food attributes during drying directly reflect product quality, and accurately predicting the trajectories of these attributes provides valuable insights into determining optimal drying conditions. Chapter 5 develops a data-driven approach for zero-shot prediction of surface color trajectories during food drying. The method learns component function parameters to represent color evolution under unseen  \nconditions, with","cbCaieNYr6vxJAqN","https://ap.wps.com/l/cbCaieNYr6vxJAqN","pdf",10961552,1,92,"English","en",105,"# Abstract\n## Uncertainty-aware optimization for apple drying\n## Multi-modal fusion for moisture prediction\n## Online readiness forecasting for cookie drying\n## Zero-shot prediction of color trajectories","[{\"question\":\"Why is smart drying challenging in industrial settings?\",\"answer\":\"Industrial drying must balance energy efficiency and food quality under multiple interacting parameters, conflicting objectives, and high uncertainty in sample characteristics, making decision-making and control difficult.\"},{\"question\":\"What does the dissertation contribute for improving efficiency and quality in drying?\",\"answer\":\"It develops machine-learning-based process control tools, including uncertainty-aware optimization, multi-modal prediction frameworks, online monitoring for drying readiness, and zero-shot trajectory prediction for surface color evolution.\"},{\"question\":\"How is online monitoring of drying readiness achieved?\",\"answer\":\"A multi-modal fusion framework integrates in-situ video data with tabular process parameters using modality-specific encoders and a transformer-based decoder to forecast the optimal drying endpoint in real time.\"}]","Machine-Learning-Enabled Optimization and Online Monitoring for Efficient and High-Quality Smart Drying - 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