{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132453"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132453","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying","abstract":"Drying 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. It 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 develops an 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. Physical 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. Accurate 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. Dynamic 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 conditions, with DCT preprocessing and enhanced by multi-modal data fusion and similarity- informed training selection. The method is validated on two case studies: cookie and apple drying, significantly outperforming baseline models by 93.2% and 87.30%, respectively.","abstract_html":"Drying 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. It 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 develops an 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. Physical 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. Accurate 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. Dynamic 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 conditions, with DCT preprocessing and enhanced by multi-modal data fusion and similarity- informed training selection. The method is validated on two case studies: cookie and apple drying, significantly outperforming baseline models by 93.2% and 87.30%, respectively.","abstract_has_math":false,"creators":["Li, Shichen"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Shao, Chenhui","Ferreira, Placid Matthew","Salapaka, Srinivasa M","Wang, Pingfeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["machine learning","data-driven modeling","smart drying","response surface methodology","multi-modal data fusion","online monitoring","zero-shot learning"],"languages":["en"],"rights":["Copyright 2025 Shichen Li All Rights Reserved"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132453","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shao, Chenhui","Ferreira, Placid Matthew","Salapaka, Srinivasa M","Wang, Pingfeng"]},{"key":"dc:creator","label":"Author","values":["Li, Shichen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-08-18"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","data-driven modeling","smart drying","response surface methodology","multi-modal data fusion","online monitoring","zero-shot learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Shichen Li All Rights Reserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132453"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Drying 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. It 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 develops an 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. Physical 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. Accurate 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. Dynamic 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 conditions, with DCT preprocessing and enhanced by multi-modal data fusion and similarity- informed training selection. The method is validated on two case studies: cookie and apple drying, significantly outperforming baseline models by 93.2% and 87.30%, respectively.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Shichen Li, accepted the attached license on 2025-08-18 at 14:01.","The student, Shichen Li, submitted this Dissertation for approval on 2025-08-18 at 14:43.","This Dissertation was approved for publication on 2025-08-18 at 16:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22763 on 2026-02-19 at 18:24:09"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying"]}]}],"canonical_facts":{"dc:contributor":["Shao, Chenhui","Ferreira, Placid Matthew","Salapaka, Srinivasa M","Wang, Pingfeng"],"dc:creator":["Li, Shichen"],"dc:date":["2025-12","2025-08-18"],"dc:description":["Drying 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. It 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 develops an 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. Physical 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. Accurate 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. Dynamic 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 conditions, with DCT preprocessing and enhanced by multi-modal data fusion and similarity- informed training selection. The method is validated on two case studies: cookie and apple drying, significantly outperforming baseline models by 93.2% and 87.30%, respectively.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Shichen Li, accepted the attached license on 2025-08-18 at 14:01.","The student, Shichen Li, submitted this Dissertation for approval on 2025-08-18 at 14:43.","This Dissertation was approved for publication on 2025-08-18 at 16:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22763 on 2026-02-19 at 18:24:09"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132453"],"dc:language":["en"],"dc:rights":["Copyright 2025 Shichen Li All Rights Reserved"],"dc:subject":["machine learning","data-driven modeling","smart drying","response surface methodology","multi-modal data fusion","online monitoring","zero-shot learning"],"dc:title":["Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}